What can @daanvanhulsen/granola-mcp do?

granola task orchestration via mcp protocol, mcp tool schema generation from granola task definitions, granola task execution with parameter binding and result streaming, mcp resource exposure for granola task history and metadata

@daanvanhulsen/granola-mcp

MCP ServerFree

MCP tool for Granola

Open Source

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4 capabilities

Best for: granola task orchestration via mcp protocol, mcp tool schema generation from granola task definitions, granola task execution with parameter binding and result streaming
Type: MCP Server · Free
Score: 22/100
Best alternative: AWS MCP Servers
Agent-compatible: Yes — MCP protocol

Capabilities4 decomposed

granola task orchestration via mcp protocol

Medium confidence

Exposes Granola task management and execution capabilities through the Model Context Protocol (MCP) standard, enabling LLM agents and Claude instances to discover, invoke, and monitor Granola workflows as remote tools. Implements MCP server interface with JSON-RPC 2.0 transport, allowing bidirectional communication between Claude/LLM clients and Granola backend without direct API integration.

Solves for

I want Claude to trigger and monitor Granola tasks from within a conversationI need to expose Granola workflows as callable tools in an agentic systemI want to build a multi-step automation where Claude orchestrates Granola jobs based on reasoning

Best for

Teams building Claude-powered automation agents that depend on Granola workflows

Developers integrating Granola into MCP-compatible LLM frameworks

Organizations standardizing on MCP for tool discovery and execution

Requires

Node.js 16+ (MCP server runtime)

Granola instance with accessible API endpoint

MCP-compatible client (Claude, LLM framework with MCP support)

Limitations

Requires Granola instance to be running and accessible from MCP server host

No built-in retry logic or circuit breaker for failed Granola task invocations

Limited to Granola API capabilities — cannot extend or transform task definitions

What makes it unique

Bridges Granola task execution into the MCP ecosystem, enabling Claude and other LLM agents to discover and invoke Granola workflows through a standardized protocol rather than custom integrations or direct API calls

vs alternatives

Provides MCP-native Granola integration vs building custom Claude tools or REST API wrappers, enabling tool discovery and standardized error handling across MCP-compatible platforms

mcp tool schema generation from granola task definitions

Medium confidence

Automatically generates MCP-compliant tool schemas from Granola task definitions, mapping task parameters to JSON Schema and exposing them as discoverable tools to LLM clients. Uses introspection of Granola task metadata to construct tool descriptions, input schemas, and execution handlers without manual schema definition.

Solves for

I want Claude to automatically discover what Granola tasks are available without hardcoding tool definitionsI need to expose dynamic Granola workflows as tools that LLM agents can reason aboutI want to keep tool schemas in sync with Granola task definitions without manual updates

Best for

Teams with frequently-changing Granola task definitions who want dynamic tool discovery

Developers building flexible agent systems that adapt to available Granola workflows

Organizations using MCP to standardize tool exposure across multiple backends

Requires

Granola instance with accessible task metadata API

MCP server runtime (Node.js 16+)

Granola tasks with well-defined parameter schemas

Limitations

Schema generation accuracy depends on Granola task metadata completeness

Complex nested parameters may not map cleanly to JSON Schema

No custom validation rules beyond what Granola task definitions specify

What makes it unique

Dynamically generates MCP tool schemas from live Granola task metadata rather than requiring manual schema definition, enabling automatic tool discovery and reducing schema maintenance overhead

vs alternatives

Eliminates manual tool schema maintenance vs static tool definitions, enabling Granola task changes to automatically propagate to LLM agents without code updates

granola task execution with parameter binding and result streaming

Medium confidence

Executes Granola tasks through MCP with full parameter binding, input validation, and result streaming back to the LLM client. Implements request-response pattern with support for long-running tasks, capturing execution status, logs, and structured results in real-time without blocking the LLM agent.

Solves for

I want to run a Granola task from Claude and get back the results in my conversationI need to pass parameters from Claude's reasoning into a Granola task executionI want to monitor long-running Granola jobs and stream results back to the agent

Best for

Developers building Claude agents that execute Granola workflows as part of multi-step reasoning

Teams needing real-time feedback from Granola task execution in agentic loops

Organizations running long-running Granola jobs that need streaming status updates

Requires

Granola instance with task execution API

MCP client supporting streaming responses

Valid Granola task identifiers and parameters

Limitations

No built-in timeout management — relies on Granola backend timeout behavior

Streaming results depend on Granola API support for incremental output

Parameter validation happens at Granola backend, not in MCP layer

What makes it unique

Implements full parameter binding and result streaming for Granola task execution through MCP, allowing LLM agents to pass context-aware parameters and receive incremental results without polling

vs alternatives

Provides streaming task execution vs batch-only alternatives, enabling real-time feedback loops where Claude can react to Granola task progress mid-execution

mcp resource exposure for granola task history and metadata

Medium confidence

Exposes Granola task execution history, logs, and metadata as MCP resources, allowing LLM clients to retrieve and reason about past task executions, results, and performance metrics. Implements MCP resource protocol with URI-based access patterns (e.g., `granola://task/{id}/history`) for querying execution records without direct Granola API calls.

Solves for

I want Claude to look up the history of a previous Granola task executionI need to retrieve logs or metadata from a Granola task to debug failuresI want Claude to reason about past task performance to make decisions about future executions

Best for

Teams building observability-aware agents that learn from past Granola executions

Developers needing Claude to access task history for debugging or auditing

Organizations using MCP resources to centralize access to execution metadata

Requires

Granola instance with task history API

MCP client supporting resource protocol

Access permissions to task execution records

Limitations

Resource access depends on Granola backend retention policies — old records may be unavailable

No built-in filtering or pagination — large result sets may exceed context limits

Metadata format is opaque to MCP — no normalization across different Granola versions

What makes it unique

Exposes Granola task history and metadata as MCP resources with URI-based access patterns, enabling LLM agents to retrieve and reason about past executions without custom API integration

vs alternatives

Provides MCP-native access to task history vs requiring agents to make separate API calls, enabling seamless integration with Claude's context and reasoning

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

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Best For

✓Teams building Claude-powered automation agents that depend on Granola workflows
✓Developers integrating Granola into MCP-compatible LLM frameworks
✓Organizations standardizing on MCP for tool discovery and execution
✓Teams with frequently-changing Granola task definitions who want dynamic tool discovery
✓Developers building flexible agent systems that adapt to available Granola workflows
✓Organizations using MCP to standardize tool exposure across multiple backends
✓Developers building Claude agents that execute Granola workflows as part of multi-step reasoning
✓Teams needing real-time feedback from Granola task execution in agentic loops

Known Limitations

⚠Requires Granola instance to be running and accessible from MCP server host
⚠No built-in retry logic or circuit breaker for failed Granola task invocations
⚠Limited to Granola API capabilities — cannot extend or transform task definitions
⚠MCP protocol overhead adds latency compared to direct REST API calls
⚠No persistent state management — task execution history depends on Granola backend
⚠Schema generation accuracy depends on Granola task metadata completeness

Requirements

Node.js 16+ (MCP server runtime)Granola instance with accessible API endpointMCP-compatible client (Claude, LLM framework with MCP support)@daanvanhulsen/granola-mcp npm packageGranola instance with accessible task metadata APIMCP server runtime (Node.js 16+)Granola tasks with well-defined parameter schemasGranola instance with task execution API

Input / Output

Accepts: task definitions (JSON), workflow parameters (key-value pairs), task identifiers (strings), Granola task metadata (JSON), task parameter definitions, task identifier (string), task parameters (JSON object), execution options (timeout, priority, etc.), time range filters (optional), metadata query parameters

Produces: task execution status (structured JSON), task results (Granola-specific format), execution logs (text/structured), MCP tool schemas (JSON Schema format), tool descriptions (text), execution status (JSON), execution logs (text stream), task execution history (JSON array), execution logs (text), performance metrics (structured data)

UnfragileRank

Adoption5%(25% weight)

Quality18%(25% weight)

Ecosystem30%(15% weight)

Match Graph25%(23% weight)

Freshness52%(12% 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

4 capabilities

Visit @daanvanhulsen/granola-mcp→

Package Details

npm

Registry

1.0.5

Version

Weekly Downloads

About

MCP tool for Granola

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Capabilities4 decomposed

granola task orchestration via mcp protocol

Medium confidence

Solves for

Best for

Teams building Claude-powered automation agents that depend on Granola workflows

Developers integrating Granola into MCP-compatible LLM frameworks

Organizations standardizing on MCP for tool discovery and execution

Requires

Node.js 16+ (MCP server runtime)

Granola instance with accessible API endpoint

MCP-compatible client (Claude, LLM framework with MCP support)

Limitations

Requires Granola instance to be running and accessible from MCP server host

No built-in retry logic or circuit breaker for failed Granola task invocations

Limited to Granola API capabilities — cannot extend or transform task definitions

What makes it unique

vs alternatives

Provides MCP-native Granola integration vs building custom Claude tools or REST API wrappers, enabling tool discovery and standardized error handling across MCP-compatible platforms

mcp tool schema generation from granola task definitions

Medium confidence

Solves for

Best for

Teams with frequently-changing Granola task definitions who want dynamic tool discovery

Developers building flexible agent systems that adapt to available Granola workflows

Organizations using MCP to standardize tool exposure across multiple backends

Requires

Granola instance with accessible task metadata API

MCP server runtime (Node.js 16+)

Granola tasks with well-defined parameter schemas

Limitations

Schema generation accuracy depends on Granola task metadata completeness

Complex nested parameters may not map cleanly to JSON Schema

No custom validation rules beyond what Granola task definitions specify

What makes it unique

Dynamically generates MCP tool schemas from live Granola task metadata rather than requiring manual schema definition, enabling automatic tool discovery and reducing schema maintenance overhead

vs alternatives

Eliminates manual tool schema maintenance vs static tool definitions, enabling Granola task changes to automatically propagate to LLM agents without code updates

granola task execution with parameter binding and result streaming

Medium confidence

Solves for

Best for

Developers building Claude agents that execute Granola workflows as part of multi-step reasoning

Teams needing real-time feedback from Granola task execution in agentic loops

Organizations running long-running Granola jobs that need streaming status updates

Requires

Granola instance with task execution API

MCP client supporting streaming responses

Valid Granola task identifiers and parameters

Limitations

No built-in timeout management — relies on Granola backend timeout behavior

Streaming results depend on Granola API support for incremental output

Parameter validation happens at Granola backend, not in MCP layer

What makes it unique

Implements full parameter binding and result streaming for Granola task execution through MCP, allowing LLM agents to pass context-aware parameters and receive incremental results without polling

vs alternatives

Provides streaming task execution vs batch-only alternatives, enabling real-time feedback loops where Claude can react to Granola task progress mid-execution

mcp resource exposure for granola task history and metadata

Medium confidence

Solves for

Best for

Teams building observability-aware agents that learn from past Granola executions

Developers needing Claude to access task history for debugging or auditing

Organizations using MCP resources to centralize access to execution metadata

Requires

Granola instance with task history API

MCP client supporting resource protocol

Access permissions to task execution records

Limitations

Resource access depends on Granola backend retention policies — old records may be unavailable

No built-in filtering or pagination — large result sets may exceed context limits

Metadata format is opaque to MCP — no normalization across different Granola versions

What makes it unique

Exposes Granola task history and metadata as MCP resources with URI-based access patterns, enabling LLM agents to retrieve and reason about past executions without custom API integration

vs alternatives

Provides MCP-native access to task history vs requiring agents to make separate API calls, enabling seamless integration with Claude's context and reasoning

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

Alternatives to @daanvanhulsen/granola-mcp

AWS MCP Servers59MCP Server

AWS Labs' official MCP suite — docs, CDK, Bedrock KB, cost, Lambda and more as agent tools.

Compare →

Zapier MCP62MCP Server

Zapier's hosted MCP — 8,000+ app integrations exposed as allowlisted agent tools.

Compare →

Hugging Face MCP Server61MCP Server

Official Hugging Face MCP — search models/datasets/Spaces/papers and call Spaces as tools.

Compare →

Atlassian Remote MCP Server61MCP Server

Atlassian's official hosted MCP — Jira + Confluence with OAuth, permission-bounded agent access.

Compare →

See all alternatives to @daanvanhulsen/granola-mcp→

@daanvanhulsen/granola-mcp

Capabilities4 decomposed

granola task orchestration via mcp protocol

mcp tool schema generation from granola task definitions

granola task execution with parameter binding and result streaming

mcp resource exposure for granola task history and metadata

Related Artifactssharing capabilities

@forwardimpact/libmcp

devmind-mcp

gigahard-mcp-server

mcp-sequentialthinking-tools

mcp-server-mas-sequential-thinkingfork

sequentialthinking2

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

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Alternatives to @daanvanhulsen/granola-mcp

Are you the builder of @daanvanhulsen/granola-mcp?

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@daanvanhulsen/granola-mcp

Capabilities4 decomposed

granola task orchestration via mcp protocol

mcp tool schema generation from granola task definitions

granola task execution with parameter binding and result streaming

mcp resource exposure for granola task history and metadata

Related Artifactssharing capabilities

@forwardimpact/libmcp

devmind-mcp

gigahard-mcp-server

mcp-sequentialthinking-tools

mcp-server-mas-sequential-thinkingfork

sequentialthinking2

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @daanvanhulsen/granola-mcp

Are you the builder of @daanvanhulsen/granola-mcp?

Get the weekly brief

Data Sources