What can @taazkareem/clickup-mcp-server do?

task-creation-and-management-via-mcp, document-retrieval-and-search-in-spaces, oauth-and-token-based-authentication, task-list-and-filter-retrieval, chat-message-posting-and-retrieval, workspace-and-team-context-discovery, task-status-and-custom-field-updates, mcp-protocol-server-hosting, ai-agent-context-window-optimization, error-handling-and-api-resilience, multi-client-mcp-compatibility

@taazkareem/clickup-mcp-server

MCP ServerFree

ClickUp MCP Server - Powering AI Agents with full ClickUp task, document, and chat management capabilities.

Open Source

/ 100

11 capabilities

Capabilities11 decomposed

task-creation-and-management-via-mcp

Medium confidence

Creates, updates, and deletes ClickUp tasks through MCP protocol handlers that translate natural language or structured requests into ClickUp API calls. Implements request validation, error handling, and response transformation to present task operations as native MCP tools callable by AI agents without direct API knowledge.

Solves for

I want my AI agent to create tasks in ClickUp when certain conditions are metI need to update task status, priority, or assignee from within an AI workflowI want to delete or archive tasks programmatically based on agent decisions

Best for

AI agent developers building autonomous task management workflows

Teams integrating ClickUp with Claude, Gemini, or other MCP-compatible LLMs

Automation engineers replacing REST API calls with MCP-based task operations

Requires

ClickUp API token (personal or OAuth)

Node.js 16+

MCP-compatible client (Claude Desktop, Cursor, Gemini CLI, or custom MCP host)

Limitations

Requires valid ClickUp API token with appropriate workspace/team permissions

Task creation latency depends on ClickUp API response time (typically 200-500ms)

Batch operations not natively supported — each task requires separate MCP call

What makes it unique

Exposes ClickUp task operations as native MCP tools rather than requiring agents to construct raw REST API calls, with built-in schema validation and error transformation specific to ClickUp's API response patterns

vs alternatives

Simpler than raw ClickUp API integration for LLM agents because MCP abstraction handles authentication, request formatting, and response parsing automatically

document-retrieval-and-search-in-spaces

Medium confidence

Searches and retrieves ClickUp documents from workspaces/spaces using MCP resource handlers that query the ClickUp API and return document metadata, content, and hierarchy. Implements pagination and filtering to handle large document collections without overwhelming agent context windows.

Solves for

I want my AI agent to find relevant documents in ClickUp based on search queriesI need to retrieve document content to provide context to an LLM for analysis or summarizationI want to list all documents in a space to understand available knowledge

Best for

Knowledge workers building AI assistants that need access to ClickUp documentation

Teams using ClickUp Docs as a knowledge base for agent-powered Q&A systems

Developers implementing RAG (Retrieval-Augmented Generation) with ClickUp as the document source

Requires

ClickUp API token with Docs read permissions

Node.js 16+

MCP client with resource support (Claude Desktop 0.5+, Cursor, Gemini CLI)

Limitations

Search is limited to document titles and metadata — full-text search requires fetching and parsing document content

Large documents may exceed MCP message size limits; pagination required for content retrieval

Document access permissions inherited from ClickUp workspace — cannot override user-level restrictions

What makes it unique

Implements MCP resource protocol for document retrieval, allowing agents to access ClickUp Docs as a knowledge source without manual API calls, with built-in pagination and metadata extraction

vs alternatives

More integrated than querying ClickUp API directly because MCP handles resource lifecycle and caching, reducing latency for repeated document access

oauth-and-token-based-authentication

Medium confidence

Supports both personal API tokens and OAuth2 authentication flows for ClickUp, allowing secure credential management without exposing tokens in prompts. Implements token refresh logic and credential validation before making API calls.

Solves for

I want to authenticate with ClickUp using OAuth instead of hardcoding API tokensI need to securely manage ClickUp credentials in a multi-user environmentI want to validate authentication before attempting ClickUp operations

Best for

Teams deploying shared MCP servers where multiple users need ClickUp access

Security-conscious organizations that require OAuth for credential management

Developers building multi-tenant AI agent platforms

Requires

ClickUp OAuth app credentials (client ID, client secret)

Node.js 16+

MCP client

Limitations

OAuth flow requires user interaction for initial authorization — not suitable for fully automated setups

Token refresh requires network access to ClickUp OAuth endpoints

No built-in credential storage — requires external secret management (e.g., environment variables, vaults)

What makes it unique

Implements both OAuth2 and personal token authentication with automatic token refresh, allowing secure credential management without exposing secrets in agent prompts

vs alternatives

More secure than hardcoded tokens because OAuth enables credential rotation and user-level access control without storing secrets in configuration

task-list-and-filter-retrieval

Medium confidence

Retrieves filtered task lists from ClickUp spaces/lists using MCP resource handlers that support multiple filter dimensions (status, assignee, priority, due date, custom fields). Implements efficient pagination and sorting to present task data to agents without requiring manual API query construction.

Solves for

I want my agent to fetch all tasks assigned to a specific person or teamI need to retrieve high-priority tasks due this week for prioritization workflowsI want to get tasks in a specific status (e.g., 'In Progress') to monitor progress

Best for

Workflow automation engineers building task monitoring and escalation systems

AI agent developers creating task prioritization or assignment recommendations

Teams building dashboards or reports powered by agent-driven task queries

Requires

ClickUp API token with task read permissions

Node.js 16+

MCP client with resource support

Limitations

Complex multi-field filters may require multiple API calls if ClickUp API doesn't support all filter combinations

Large task lists (1000+) require pagination; retrieving all tasks in one call may timeout

Custom field filtering depends on field definitions existing in the workspace

What makes it unique

Exposes ClickUp's filter API as MCP resources with pre-built filter templates for common queries (by assignee, status, priority), reducing agent complexity vs raw API filter syntax

vs alternatives

Simpler than building custom filter logic because MCP abstracts ClickUp's filter query language and handles pagination automatically

chat-message-posting-and-retrieval

Medium confidence

Posts messages to ClickUp task comments and retrieves comment threads using MCP tool handlers that translate agent messages into ClickUp API calls. Supports rich text formatting, mentions, and attachment references while maintaining conversation context within task threads.

Solves for

I want my agent to post status updates or notes to task comments automaticallyI need to retrieve task comment history to provide context to an LLM for decision-makingI want to mention team members in task comments when escalating or assigning work

Best for

Teams using ClickUp comments as a collaboration channel for agent-driven workflows

AI agents that need to provide feedback or updates within task context

Automation systems that log decisions or actions as task comments for audit trails

Requires

ClickUp API token with comment read/write permissions

Node.js 16+

MCP client

Limitations

Comment formatting limited to ClickUp's supported rich text (no custom HTML)

Mentions require valid ClickUp user IDs — agent must resolve names to IDs first

Comment history retrieval may be slow for tasks with 100+ comments

What makes it unique

Integrates ClickUp task comments as an MCP tool, allowing agents to participate in task discussions and maintain audit trails within ClickUp's native interface rather than external logging systems

vs alternatives

More integrated than external logging because comments stay within ClickUp's task context, visible to all team members without context switching

workspace-and-team-context-discovery

Medium confidence

Discovers and exposes ClickUp workspace structure (teams, spaces, lists, folders) through MCP resource handlers that query the ClickUp API and cache hierarchy metadata. Enables agents to understand available task containers and navigate the workspace without hardcoded IDs.

Solves for

I want my agent to discover available teams and spaces to determine where to create tasksI need to list all lists/folders in a space to understand task organizationI want my agent to validate that a target space exists before attempting operations

Best for

Multi-team organizations where agents need to route tasks to correct spaces dynamically

Developers building flexible agent workflows that adapt to workspace structure changes

Teams implementing agent-driven task routing without hardcoding space/list IDs

Requires

ClickUp API token with workspace read permissions

Node.js 16+

MCP client

Limitations

Hierarchy discovery is read-only — agents cannot create new spaces/lists via MCP

Large workspaces (100+ spaces) may have slow discovery due to API pagination

Metadata caching may be stale if workspace structure changes frequently

What makes it unique

Exposes ClickUp workspace hierarchy as MCP resources with caching, allowing agents to dynamically discover task containers instead of requiring hardcoded space/list IDs in prompts

vs alternatives

More flexible than static configuration because agents can adapt to workspace changes without redeployment

task-status-and-custom-field-updates

Medium confidence

Updates task metadata (status, priority, custom fields, due dates, assignees) through MCP tool handlers that validate field types and values against ClickUp's schema before submitting API calls. Implements field-type-aware transformations (date parsing, enum validation, number formatting) to prevent API errors.

Solves for

I want my agent to move tasks between statuses based on workflow logicI need to update custom fields (e.g., 'Effort Estimate', 'Client Name') programmaticallyI want to change task priority or due date based on agent decisions

Best for

Workflow automation engineers building status-driven task pipelines

AI agents that need to update task metadata based on analysis or decisions

Teams using custom fields for tracking and want agent-driven updates

Requires

ClickUp API token with task write permissions

Node.js 16+

MCP client

Limitations

Custom field updates require pre-existing field definitions in ClickUp — agent cannot create new fields

Field validation is schema-based — invalid values are rejected before API call

Enum fields (status, priority) are workspace-specific — agent must know valid values

What makes it unique

Implements field-type-aware validation for ClickUp custom fields, preventing API errors by transforming agent-provided values to match ClickUp's schema before submission

vs alternatives

More robust than raw API calls because built-in validation catches type mismatches and enum violations before they reach ClickUp's API

mcp-protocol-server-hosting

Medium confidence

Runs as a standalone MCP server process that exposes ClickUp capabilities via the Model Context Protocol, handling authentication, request routing, and response serialization. Supports multiple concurrent MCP clients (Claude Desktop, Cursor, Gemini CLI, n8n) through a single server instance with configurable logging and error handling.

Solves for

I want to run a single ClickUp MCP server that multiple AI tools can connect toI need to host the MCP server in a specific environment (local, Docker, cloud)I want to monitor and debug MCP requests and responses

Best for

Teams deploying ClickUp integration across multiple MCP clients (Claude, Cursor, Gemini)

DevOps engineers containerizing MCP servers for production environments

Developers debugging MCP protocol issues or custom tool implementations

Requires

Node.js 16+

ClickUp API token (as environment variable)

MCP client compatible with stdio or SSE transport

Limitations

Single-threaded Node.js process — high concurrency may cause latency spikes

No built-in load balancing or clustering — requires external orchestration for HA

Authentication via environment variables — no dynamic credential rotation

What makes it unique

Implements full MCP server specification with support for multiple transport types (stdio, SSE) and concurrent client connections, enabling seamless integration with Claude, Cursor, Gemini, and other MCP-compatible tools

vs alternatives

More flexible than direct API integration because MCP abstraction allows the same server to work with any MCP client without code changes

ai-agent-context-window-optimization

Medium confidence

Structures MCP responses to fit within LLM context windows by implementing pagination, summarization, and selective field inclusion. Omits unnecessary metadata and provides compact representations of large task lists or document collections, allowing agents to work with ClickUp data without exceeding token limits.

Solves for

I want my agent to work with large task lists without exceeding context window limitsI need to retrieve task data in a compact format to preserve tokens for reasoningI want my agent to fetch only relevant fields (e.g., task name and status, not full metadata)

Best for

Developers building token-efficient AI agents with limited context windows

Teams using smaller LLMs (e.g., Claude 3 Haiku) that have tight token budgets

Workflows that need to process many tasks in a single agent run

Requires

MCP client with pagination support

Agent prompt that understands pagination parameters

ClickUp API token

Limitations

Pagination requires multiple MCP calls — agents must handle pagination logic

Selective field inclusion may omit data needed for downstream decisions

Summarization is lossy — agents cannot access full task details without additional calls

What makes it unique

Implements context-aware response formatting that adapts to LLM context window constraints, returning compact representations by default while allowing agents to request full details when needed

vs alternatives

More efficient than raw API responses because MCP omits unnecessary metadata and supports pagination, reducing token consumption for large task lists

error-handling-and-api-resilience

Medium confidence

Implements retry logic, rate-limit handling, and error transformation for ClickUp API calls, converting API errors into human-readable messages for agents. Includes exponential backoff for transient failures and graceful degradation when ClickUp API is unavailable.

Solves for

I want my agent to automatically retry failed ClickUp operationsI need clear error messages when ClickUp API calls failI want my agent to handle rate limits gracefully without crashing

Best for

Production AI agents that need reliable ClickUp integration

Teams with high-volume task operations that may hit rate limits

Developers building fault-tolerant automation workflows

Requires

Node.js 16+

MCP client with timeout support

ClickUp API token

Limitations

Retry logic adds latency (exponential backoff up to 30 seconds)

Rate limit handling is reactive — no proactive rate limit prediction

Transient failures are retried up to 3 times — persistent failures fail fast

What makes it unique

Implements automatic retry with exponential backoff and rate-limit detection, allowing agents to recover from transient ClickUp API failures without manual intervention

vs alternatives

More resilient than raw API calls because built-in retry logic handles transient failures automatically, improving success rates for long-running workflows

multi-client-mcp-compatibility

Medium confidence

Maintains compatibility with multiple MCP clients (Claude Desktop, Cursor IDE, Gemini CLI, n8n, Cline, Windsurf, Zed, Supermaven) through adherence to MCP protocol standards and testing against each client's specific transport and tool-calling implementations. Handles client-specific quirks (e.g., different JSON-RPC versions, tool parameter formats).

Solves for

I want to use the same ClickUp MCP server with Claude Desktop and CursorI need my ClickUp integration to work with n8n workflowsI want to ensure compatibility across multiple AI tools without maintaining separate integrations

Best for

Teams using multiple MCP clients and wanting a single ClickUp integration

Developers building MCP servers that need broad client support

Organizations with heterogeneous AI tool stacks (Claude, Gemini, Cursor, etc.)

Requires

Node.js 16+

MCP-compatible client (Claude Desktop 0.5+, Cursor, Gemini CLI, n8n, etc.)

ClickUp API token

Limitations

Client-specific features (e.g., Claude's vision) are not supported by all clients

Testing across all clients is manual — no automated cross-client test suite

Protocol updates in MCP spec may require server updates for new clients

What makes it unique

Tested and maintained for compatibility with 8+ MCP clients (Claude, Cursor, Gemini, n8n, Cline, Windsurf, Zed, Supermaven), ensuring the same server works across diverse AI tool ecosystems

vs alternatives

More versatile than client-specific integrations because single MCP server works with any MCP-compatible tool without forking or maintaining separate implementations

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @taazkareem/clickup-mcp-server, ranked by overlap. Discovered automatically through the match graph.

MCP Server24

C# MCP SDK

[Go MCP SDK](https://github.com/modelcontextprotocol/go-sdk)

client-side oauth flow and token managementoauth authentication and authorization for servers

2 shared capabilities

MCP Server25

mcp-auth

Plug and play auth for Model Context Protocol (MCP) servers

mcp protocol-aware token validation and session managementoauth 2.0 / openid connect server integration for mcp

2 shared capabilities

MCP Server37

modelcontextprotocol

Specification and documentation for the Model Context Protocol

oauth 2.1 authorization framework with token management and validation

1 shared capability

MCP Server30

@mcp-use/cli

The mcp-use CLI is a tool for building and deploying MCP servers with support for ChatGPT Apps, Code Mode, OAuth, Notifications, Sampling, Observability and more.

oauth 2.0 credential management and token refresh

1 shared capability

MCP Server46

Confluence MCP Server

Search, read, and create Confluence wiki pages via MCP.

flexible multi-tenant authentication with api tokens, pat, and oauth 2.0

1 shared capability

MCP Server24

MCP Plexus

**: A secure, **multi-tenant** Python MCP server framework built to integrate easily with external services via OAuth 2.1, offering scalable and robust solutions for managing complex AI applications.

oauth 2.1 credential exchange and token lifecycle management

1 shared capability

Best For

✓AI agent developers building autonomous task management workflows
✓Teams integrating ClickUp with Claude, Gemini, or other MCP-compatible LLMs
✓Automation engineers replacing REST API calls with MCP-based task operations
✓Knowledge workers building AI assistants that need access to ClickUp documentation
✓Teams using ClickUp Docs as a knowledge base for agent-powered Q&A systems
✓Developers implementing RAG (Retrieval-Augmented Generation) with ClickUp as the document source
✓Teams deploying shared MCP servers where multiple users need ClickUp access
✓Security-conscious organizations that require OAuth for credential management

Known Limitations

⚠Requires valid ClickUp API token with appropriate workspace/team permissions
⚠Task creation latency depends on ClickUp API response time (typically 200-500ms)
⚠Batch operations not natively supported — each task requires separate MCP call
⚠Custom fields require pre-existing field definitions in ClickUp workspace
⚠Search is limited to document titles and metadata — full-text search requires fetching and parsing document content
⚠Large documents may exceed MCP message size limits; pagination required for content retrieval

Requirements

ClickUp API token (personal or OAuth)Node.js 16+MCP-compatible client (Claude Desktop, Cursor, Gemini CLI, or custom MCP host)Valid ClickUp workspace and team/list accessClickUp API token with Docs read permissionsMCP client with resource support (Claude Desktop 0.5+, Cursor, Gemini CLI)Valid ClickUp workspace with Docs feature enabledClickUp OAuth app credentials (client ID, client secret)

Input / Output

Accepts: structured JSON (task name, description, assignee, priority, due date), natural language instructions from LLM, task IDs for update/delete operations, search query strings, space/folder IDs, document IDs, filter parameters (date range, author), OAuth authorization code (during initial setup), API token (for personal token auth), space/list/folder IDs, filter objects (status, assignee, priority, due date range), sort parameters (by date, priority, custom field), pagination parameters (limit, offset), task IDs, message text (plain or rich text), user IDs for mentions, attachment URLs, workspace ID (optional — defaults to authenticated user's workspace), filter parameters (team ID, space ID), task ID, field updates object (status, priority, custom_fields, due_date, assignees), field values (strings, numbers, dates, enums), MCP protocol messages (JSON-RPC over stdio or HTTP), tool calls with parameters, resource requests, field selection parameters (which fields to include), filter parameters (to reduce result set), MCP tool calls, API responses (success or error), MCP protocol messages from any compatible client

Produces: task object with ID, status, timestamps, confirmation messages, error responses with validation details, document metadata (title, ID, created date, author), document content (markdown or HTML), document hierarchy/breadcrumb, search result lists with relevance ranking, authentication status (success/failure), token refresh confirmation, task arrays with full metadata (ID, name, status, assignee, priority, due date, custom fields), aggregated counts (tasks by status, by assignee), pagination metadata (total count, has_more), comment object with ID, timestamp, author, content, comment thread (array of comments with metadata), confirmation of posted message, workspace metadata (name, ID, plan type), teams array with IDs and names, spaces array with hierarchy (space > folder > list), custom field definitions, updated task object with new metadata, validation error messages if update fails, confirmation of successful update, MCP protocol responses (JSON-RPC), tool results, resource data, error messages, compact task objects (name, ID, status, assignee only), summarized task lists, successful operation results, error messages with retry information, rate limit warnings, MCP protocol responses compatible with requesting client

UnfragileRank

Adoption55%(30% weight)

Quality30%(25% weight)

Ecosystem70%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

11 capabilities

Visit @taazkareem/clickup-mcp-server→

Package Details

npm

Registry

0.14.3

Version

27,410

Weekly Downloads

About

ClickUp MCP Server - Powering AI Agents with full ClickUp task, document, and chat management capabilities.

Alternatives to @taazkareem/clickup-mcp-server

vitest-llm-reporter30Repository

A Vitest reporter optimized for LLM parsing with structured, concise output

Compare →

vectra41Repository

A lightweight, file-backed vector database for Node.js and browsers with Pinecone-compatible filtering and hybrid BM25 search.

Compare →

@tanstack/ai37API

Core TanStack AI library - Open source AI SDK

Compare →

strapi-plugin-embeddings32Repository

AI embeddings and semantic search plugin for Strapi v5 with pgvector support

Compare →

Are you the builder of @taazkareem/clickup-mcp-server?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

mcp registry

Looking for something else?

Search →

Capabilities11 decomposed

task-creation-and-management-via-mcp

Medium confidence

Solves for

Best for

AI agent developers building autonomous task management workflows

Teams integrating ClickUp with Claude, Gemini, or other MCP-compatible LLMs

Automation engineers replacing REST API calls with MCP-based task operations

Requires

ClickUp API token (personal or OAuth)

Node.js 16+

MCP-compatible client (Claude Desktop, Cursor, Gemini CLI, or custom MCP host)

Limitations

Requires valid ClickUp API token with appropriate workspace/team permissions

Task creation latency depends on ClickUp API response time (typically 200-500ms)

Batch operations not natively supported — each task requires separate MCP call

What makes it unique

vs alternatives

Simpler than raw ClickUp API integration for LLM agents because MCP abstraction handles authentication, request formatting, and response parsing automatically

document-retrieval-and-search-in-spaces

Medium confidence

Solves for

Best for

Knowledge workers building AI assistants that need access to ClickUp documentation

Teams using ClickUp Docs as a knowledge base for agent-powered Q&A systems

Developers implementing RAG (Retrieval-Augmented Generation) with ClickUp as the document source

Requires

ClickUp API token with Docs read permissions

Node.js 16+

MCP client with resource support (Claude Desktop 0.5+, Cursor, Gemini CLI)

Limitations

Search is limited to document titles and metadata — full-text search requires fetching and parsing document content

Large documents may exceed MCP message size limits; pagination required for content retrieval

Document access permissions inherited from ClickUp workspace — cannot override user-level restrictions

What makes it unique

Implements MCP resource protocol for document retrieval, allowing agents to access ClickUp Docs as a knowledge source without manual API calls, with built-in pagination and metadata extraction

vs alternatives

More integrated than querying ClickUp API directly because MCP handles resource lifecycle and caching, reducing latency for repeated document access

oauth-and-token-based-authentication

Medium confidence

Solves for

Best for

Teams deploying shared MCP servers where multiple users need ClickUp access

Security-conscious organizations that require OAuth for credential management

Developers building multi-tenant AI agent platforms

Requires

ClickUp OAuth app credentials (client ID, client secret)

Node.js 16+

MCP client

Limitations

OAuth flow requires user interaction for initial authorization — not suitable for fully automated setups

Token refresh requires network access to ClickUp OAuth endpoints

No built-in credential storage — requires external secret management (e.g., environment variables, vaults)

What makes it unique

Implements both OAuth2 and personal token authentication with automatic token refresh, allowing secure credential management without exposing secrets in agent prompts

vs alternatives

More secure than hardcoded tokens because OAuth enables credential rotation and user-level access control without storing secrets in configuration

task-list-and-filter-retrieval

Medium confidence

Solves for

Best for

Workflow automation engineers building task monitoring and escalation systems

AI agent developers creating task prioritization or assignment recommendations

Teams building dashboards or reports powered by agent-driven task queries

Requires

ClickUp API token with task read permissions

Node.js 16+

MCP client with resource support

Limitations

Complex multi-field filters may require multiple API calls if ClickUp API doesn't support all filter combinations

Large task lists (1000+) require pagination; retrieving all tasks in one call may timeout

Custom field filtering depends on field definitions existing in the workspace

What makes it unique

Exposes ClickUp's filter API as MCP resources with pre-built filter templates for common queries (by assignee, status, priority), reducing agent complexity vs raw API filter syntax

vs alternatives

Simpler than building custom filter logic because MCP abstracts ClickUp's filter query language and handles pagination automatically

chat-message-posting-and-retrieval

Medium confidence

Solves for

Best for

Teams using ClickUp comments as a collaboration channel for agent-driven workflows

AI agents that need to provide feedback or updates within task context

Automation systems that log decisions or actions as task comments for audit trails

Requires

ClickUp API token with comment read/write permissions

Node.js 16+

MCP client

Limitations

Comment formatting limited to ClickUp's supported rich text (no custom HTML)

Mentions require valid ClickUp user IDs — agent must resolve names to IDs first

Comment history retrieval may be slow for tasks with 100+ comments

What makes it unique

Integrates ClickUp task comments as an MCP tool, allowing agents to participate in task discussions and maintain audit trails within ClickUp's native interface rather than external logging systems

vs alternatives

More integrated than external logging because comments stay within ClickUp's task context, visible to all team members without context switching

workspace-and-team-context-discovery

Medium confidence

Solves for

Best for

Multi-team organizations where agents need to route tasks to correct spaces dynamically

Developers building flexible agent workflows that adapt to workspace structure changes

Teams implementing agent-driven task routing without hardcoding space/list IDs

Requires

ClickUp API token with workspace read permissions

Node.js 16+

MCP client

Limitations

Hierarchy discovery is read-only — agents cannot create new spaces/lists via MCP

Large workspaces (100+ spaces) may have slow discovery due to API pagination

Metadata caching may be stale if workspace structure changes frequently

What makes it unique

Exposes ClickUp workspace hierarchy as MCP resources with caching, allowing agents to dynamically discover task containers instead of requiring hardcoded space/list IDs in prompts

vs alternatives

More flexible than static configuration because agents can adapt to workspace changes without redeployment

task-status-and-custom-field-updates

Medium confidence

Solves for

Best for

Workflow automation engineers building status-driven task pipelines

AI agents that need to update task metadata based on analysis or decisions

Teams using custom fields for tracking and want agent-driven updates

Requires

ClickUp API token with task write permissions

Node.js 16+

MCP client

Limitations

Custom field updates require pre-existing field definitions in ClickUp — agent cannot create new fields

Field validation is schema-based — invalid values are rejected before API call

Enum fields (status, priority) are workspace-specific — agent must know valid values

What makes it unique

Implements field-type-aware validation for ClickUp custom fields, preventing API errors by transforming agent-provided values to match ClickUp's schema before submission

vs alternatives

More robust than raw API calls because built-in validation catches type mismatches and enum violations before they reach ClickUp's API

mcp-protocol-server-hosting

Medium confidence

Solves for

Best for

Teams deploying ClickUp integration across multiple MCP clients (Claude, Cursor, Gemini)

DevOps engineers containerizing MCP servers for production environments

Developers debugging MCP protocol issues or custom tool implementations

Requires

Node.js 16+

ClickUp API token (as environment variable)

MCP client compatible with stdio or SSE transport

Limitations

Single-threaded Node.js process — high concurrency may cause latency spikes

No built-in load balancing or clustering — requires external orchestration for HA

Authentication via environment variables — no dynamic credential rotation

What makes it unique

vs alternatives

More flexible than direct API integration because MCP abstraction allows the same server to work with any MCP client without code changes

ai-agent-context-window-optimization

Medium confidence

Solves for

Best for

Developers building token-efficient AI agents with limited context windows

Teams using smaller LLMs (e.g., Claude 3 Haiku) that have tight token budgets

Workflows that need to process many tasks in a single agent run

Requires

MCP client with pagination support

Agent prompt that understands pagination parameters

ClickUp API token

Limitations

Pagination requires multiple MCP calls — agents must handle pagination logic

Selective field inclusion may omit data needed for downstream decisions

Summarization is lossy — agents cannot access full task details without additional calls

What makes it unique

Implements context-aware response formatting that adapts to LLM context window constraints, returning compact representations by default while allowing agents to request full details when needed

vs alternatives

More efficient than raw API responses because MCP omits unnecessary metadata and supports pagination, reducing token consumption for large task lists

error-handling-and-api-resilience

Medium confidence

Solves for

I want my agent to automatically retry failed ClickUp operationsI need clear error messages when ClickUp API calls failI want my agent to handle rate limits gracefully without crashing

Best for

Production AI agents that need reliable ClickUp integration

Teams with high-volume task operations that may hit rate limits

Developers building fault-tolerant automation workflows

Requires

Node.js 16+

MCP client with timeout support

ClickUp API token

Limitations

Retry logic adds latency (exponential backoff up to 30 seconds)

Rate limit handling is reactive — no proactive rate limit prediction

Transient failures are retried up to 3 times — persistent failures fail fast

What makes it unique

Implements automatic retry with exponential backoff and rate-limit detection, allowing agents to recover from transient ClickUp API failures without manual intervention

vs alternatives

More resilient than raw API calls because built-in retry logic handles transient failures automatically, improving success rates for long-running workflows

multi-client-mcp-compatibility

Medium confidence

Solves for

Best for

Teams using multiple MCP clients and wanting a single ClickUp integration

Developers building MCP servers that need broad client support

Organizations with heterogeneous AI tool stacks (Claude, Gemini, Cursor, etc.)

Requires

Node.js 16+

MCP-compatible client (Claude Desktop 0.5+, Cursor, Gemini CLI, n8n, etc.)

ClickUp API token

Limitations

Client-specific features (e.g., Claude's vision) are not supported by all clients

Testing across all clients is manual — no automated cross-client test suite

Protocol updates in MCP spec may require server updates for new clients

What makes it unique

Tested and maintained for compatibility with 8+ MCP clients (Claude, Cursor, Gemini, n8n, Cline, Windsurf, Zed, Supermaven), ensuring the same server works across diverse AI tool ecosystems

vs alternatives

More versatile than client-specific integrations because single MCP server works with any MCP-compatible tool without forking or maintaining separate implementations

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

@taazkareem/clickup-mcp-server

Capabilities11 decomposed

task-creation-and-management-via-mcp

document-retrieval-and-search-in-spaces

oauth-and-token-based-authentication

task-list-and-filter-retrieval

chat-message-posting-and-retrieval

workspace-and-team-context-discovery

task-status-and-custom-field-updates

mcp-protocol-server-hosting

ai-agent-context-window-optimization

error-handling-and-api-resilience

multi-client-mcp-compatibility

Related Artifactssharing capabilities

C# MCP SDK

mcp-auth

modelcontextprotocol

@mcp-use/cli

Confluence MCP Server

MCP Plexus

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @taazkareem/clickup-mcp-server

Are you the builder of @taazkareem/clickup-mcp-server?

Get the weekly brief

Data Sources

@taazkareem/clickup-mcp-server

Capabilities11 decomposed

task-creation-and-management-via-mcp

document-retrieval-and-search-in-spaces

oauth-and-token-based-authentication

task-list-and-filter-retrieval

chat-message-posting-and-retrieval

workspace-and-team-context-discovery

task-status-and-custom-field-updates

mcp-protocol-server-hosting

ai-agent-context-window-optimization

error-handling-and-api-resilience

multi-client-mcp-compatibility

Related Artifactssharing capabilities

C# MCP SDK

mcp-auth

modelcontextprotocol

@mcp-use/cli

Confluence MCP Server

MCP Plexus

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @taazkareem/clickup-mcp-server

Are you the builder of @taazkareem/clickup-mcp-server?

Get the weekly brief

Data Sources