workspace-entity-querying-via-mcp, entity-creation-and-mutation, workspace-schema-discovery, mcp-protocol-transport-and-authentication, relationship-and-linked-entity-traversal, batch-entity-operations, field-value-filtering-and-search

Fibery

MCP ServerFree

** - Perform queries and entity operations in your [Fibery](https://fibery.io) workspace.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

workspace-entity-querying-via-mcp

Medium confidence

Executes structured queries against Fibery workspace entities using the Model Context Protocol (MCP) transport layer, enabling LLM agents and tools to fetch entity data, relationships, and metadata without direct API calls. Implements MCP resource and tool abstractions that map to Fibery's GraphQL query engine, handling authentication via workspace API tokens and translating natural language or structured requests into optimized Fibery queries.

Solves for

Query specific entities or collections from my Fibery workspace within an AI agent workflowFetch entity relationships and linked data to build context for decision-making tasksRetrieve workspace metadata and schema information to understand available entity types and fieldsFilter and search entities by field values or complex conditions without writing raw GraphQL

Best for

AI agent developers building autonomous workflows that need to read Fibery workspace state

Teams integrating Fibery as a knowledge source for LLM-powered decision systems

Developers building Claude/LLM tools that need structured access to workspace data

Requires

Fibery workspace with API access enabled

Valid Fibery API token (workspace-level authentication)

MCP client implementation (e.g., Claude Desktop, custom MCP host)

Limitations

Query complexity limited by MCP message size constraints and Fibery GraphQL depth limits

No built-in query result caching — each request hits Fibery API, adding latency for repeated queries

Requires explicit schema knowledge or discovery step to construct valid queries; no automatic schema introspection exposed

What makes it unique

Exposes Fibery workspace queries through MCP protocol, allowing LLM agents to treat Fibery as a first-class data source without custom API client code. Uses MCP resource abstraction to represent entity types and tool abstraction for query operations, bridging Fibery's GraphQL API to LLM-native tool-calling patterns.

vs alternatives

Enables direct Fibery integration in Claude and other MCP-compatible LLMs without building custom API wrappers, whereas REST API clients require boilerplate authentication and query construction logic in agent code.

entity-creation-and-mutation

Medium confidence

Creates, updates, and deletes entities in Fibery workspace via MCP tool calls, translating structured mutation requests into Fibery GraphQL mutations. Handles field validation, relationship assignment, and error propagation back to the LLM agent, enabling autonomous workflows to modify workspace state based on decisions or external triggers.

Solves for

Create new entities (tasks, documents, records) in Fibery from agent decisions or external eventsUpdate entity fields and relationships based on workflow logic or user inputDelete or archive entities when conditions are metBatch create or update multiple entities in a single operation

Best for

Autonomous agents that need to write state changes to Fibery (e.g., task creation, status updates)

Workflow automation systems that create entities as side effects of decisions

Integration pipelines that ingest external data and populate Fibery workspace

Requires

Fibery workspace with write API permissions enabled

API token with mutation scope (not read-only)

Knowledge of target entity type schema (field names, types, required fields)

Limitations

Mutations are synchronous and blocking — no async job queue for bulk operations

No transaction support across multiple entity mutations; partial failures may leave workspace in inconsistent state

Field validation errors are returned to agent but require agent-side retry logic

What makes it unique

Exposes Fibery mutations as MCP tools, allowing LLM agents to modify workspace state through natural tool-calling patterns rather than requiring agents to construct GraphQL mutations. Handles schema validation and error translation to provide agent-friendly feedback.

vs alternatives

Simpler than building custom mutation handlers in agent code; MCP abstraction hides GraphQL complexity and provides consistent error handling, whereas direct API calls require agents to understand Fibery's mutation syntax and error codes.

workspace-schema-discovery

Medium confidence

Introspects Fibery workspace schema to expose available entity types, fields, relationships, and field metadata (types, constraints, enums) through MCP resources. Enables agents to dynamically understand workspace structure without hardcoded schema knowledge, supporting adaptive queries and mutations based on actual workspace configuration.

Solves for

Discover what entity types exist in my workspace and their available fieldsUnderstand field types and constraints to construct valid queries and mutationsFind relationship definitions between entity types to build context graphsAdapt agent behavior based on workspace schema (e.g., conditional field handling)

Best for

Generic Fibery agents that need to work across different workspace configurations

Integration builders who want schema-driven mutation/query generation

Teams with evolving workspace schemas that need agents to adapt automatically

Requires

Fibery workspace with schema introspection API enabled

API token with read access to workspace metadata

MCP client to invoke schema discovery tools

Limitations

Schema introspection adds latency on first call; no built-in caching mechanism

Custom field types or plugins may not be fully exposed in schema metadata

Permissions-based field visibility not reflected in schema — agent may attempt queries on fields it lacks access to

What makes it unique

Provides dynamic schema introspection as an MCP resource, allowing agents to query workspace structure at runtime rather than relying on static schema definitions. Enables schema-driven code generation for queries and mutations within the agent's reasoning loop.

vs alternatives

Agents can adapt to workspace schema changes without redeployment, whereas hardcoded schema assumptions require manual updates when workspace structure evolves. Reduces agent hallucination by grounding queries in actual workspace metadata.

mcp-protocol-transport-and-authentication

Medium confidence

Implements MCP server protocol handling with Fibery API authentication, managing request/response serialization, error handling, and session state. Translates MCP tool calls and resource requests into authenticated Fibery API calls, handling token refresh, rate limiting, and connection lifecycle. Provides standardized MCP interface for LLM clients (Claude, custom hosts) to invoke Fibery operations.

Solves for

Connect Claude or other MCP clients to my Fibery workspace securelyAuthenticate requests using workspace API tokens without exposing credentials to LLMHandle MCP protocol details transparently so agents focus on business logicManage API rate limits and connection pooling for reliable multi-request workflows

Best for

Teams deploying Fibery MCP server as a bridge between Claude/LLMs and workspace

Developers building custom MCP hosts that need Fibery integration

Organizations requiring secure credential management for workspace API access

Requires

Fibery workspace API token (generated in workspace settings)

MCP client implementation (Claude Desktop, custom MCP host, etc.)

Network access from MCP server to Fibery API endpoints

Limitations

Authentication is token-based; no OAuth2 or SAML support for multi-user scenarios

Single workspace per MCP server instance — multi-workspace support requires multiple server instances

Rate limiting enforced by Fibery API; MCP server does not implement client-side queuing or backoff

What makes it unique

Implements full MCP server lifecycle for Fibery, handling protocol serialization, authentication, and error translation. Abstracts Fibery API complexity behind MCP tool and resource interfaces, allowing LLM clients to interact with workspace without understanding GraphQL or Fibery API details.

vs alternatives

MCP protocol provides standardized interface that works with Claude and other LLM platforms out-of-the-box, whereas custom API clients require platform-specific integration code for each LLM provider.

relationship-and-linked-entity-traversal

Medium confidence

Queries and traverses entity relationships within Fibery workspace, enabling agents to fetch linked entities, build context graphs, and understand entity connections. Implements relationship resolution through GraphQL nested queries, supporting both one-to-many and many-to-many relationships with optional depth limits and field filtering.

Solves for

Fetch all linked entities for a given entity (e.g., all tasks linked to a project)Build context by traversing relationship chains (e.g., project → tasks → assignees)Query entities by relationship criteria (e.g., find all tasks assigned to a specific person)Understand relationship cardinality and constraints to avoid invalid operations

Best for

Agents building rich context from interconnected workspace data

Workflow systems that need to understand entity dependencies and relationships

Knowledge graph builders that map Fibery workspace structure

Requires

Fibery workspace with relationship definitions between entity types

Query capability (read access to related entities)

Understanding of workspace relationship schema

Limitations

Deep relationship traversal (3+ levels) may hit GraphQL query complexity limits

No built-in cycle detection for circular relationships; agent must handle cycles

Relationship queries return full linked entity objects; no lazy loading or pagination within relationships

What makes it unique

Exposes Fibery relationship queries through MCP, allowing agents to traverse entity graphs without constructing complex nested GraphQL queries. Handles relationship resolution transparently, presenting linked entities as natural tool outputs.

vs alternatives

Agents can build rich context by following relationships without understanding GraphQL nesting syntax; direct API clients require agents to construct nested queries manually, increasing complexity and error risk.

batch-entity-operations

Medium confidence

Supports batch creation, update, and deletion of multiple entities in a single MCP call, translating batch requests into optimized Fibery API operations. Handles partial failures gracefully, returning per-entity status and allowing agents to retry failed items independently.

Solves for

Create multiple entities in bulk from external data sources or agent decisionsUpdate multiple entities with the same field values or computed changesDelete or archive multiple entities matching criteriaReduce latency by batching related operations into single API calls

Best for

Integration pipelines importing bulk data into Fibery

Agents performing bulk state updates based on workflow decisions

Teams needing to minimize API call overhead for high-volume operations

Requires

Fibery workspace with write API permissions

API token with mutation scope

Batch size within Fibery API limits

Limitations

Batch size limited by Fibery API (typically 100-1000 items per batch)

No transactional guarantees — partial failures leave some entities modified

Batch operations are sequential; no parallelization within batch

What makes it unique

Provides batch operation abstraction through MCP, allowing agents to submit multiple mutations in a single tool call. Handles partial failure semantics and per-entity error reporting, enabling agents to implement retry logic for failed items.

vs alternatives

Reduces API call overhead compared to individual entity mutations; agents can batch 100 operations into 1 call instead of 100 calls, improving latency and throughput for bulk workflows.

field-value-filtering-and-search

Medium confidence

Filters and searches entities by field values, supporting exact matches, range queries, text search, and complex boolean conditions. Translates filter expressions into Fibery GraphQL where clauses, enabling agents to query entities without fetching entire collections. Supports field types including text, numbers, dates, enums, and relationships.

Solves for

Find entities matching specific field criteria (e.g., status = 'open', priority > 5)Search entities by text fields with partial matchingQuery entities by date ranges (e.g., created after date X)Combine multiple filters with AND/OR logic to narrow results

Best for

Agents querying workspace for entities matching specific conditions

Workflow systems that need to find entities by state or attributes

Search-driven agents that filter large collections efficiently

Requires

Knowledge of entity field names and types

Understanding of filter syntax (exact match, range, text search operators)

Query capability (read access to entities)

Limitations

Filter syntax must match Fibery GraphQL where clause format; complex filters may be unintuitive for agents

Full-text search limited to text fields; no cross-field search

Regex or fuzzy matching not supported; only exact and prefix matching

What makes it unique

Exposes Fibery filtering as MCP tool, allowing agents to construct queries with field-level filters without writing GraphQL. Supports multiple filter operators (equals, range, text search) and boolean combinations, enabling flexible entity queries.

vs alternatives

Agents can filter entities efficiently without fetching full collections; direct API clients require agents to construct where clauses manually or fetch all entities and filter in-memory, reducing efficiency.

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

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3 shared capabilities

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google workspace user provisioning and lifecycle management via mcpgoogle workspace resource management (calendars, rooms) via mcp

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Tinybird

** - Interact with Tinybird serverless ClickHouse platform

workspace-and-resource-introspection

1 shared capability

MCP Server43

@taazkareem/clickup-mcp-server

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

workspace-and-team-context-discovery

1 shared capability

MCP Server27

Dart

** - Interact with task, doc, and project data in [Dart](https://itsdart.com), an AI-native project management tool

configuration retrieval for workspace context

1 shared capability

MCP Server24

Baserow

** - Read and write access to your Baserow tables.

schema-introspection-and-table-discovery

1 shared capability

Best For

✓AI agent developers building autonomous workflows that need to read Fibery workspace state
✓Teams integrating Fibery as a knowledge source for LLM-powered decision systems
✓Developers building Claude/LLM tools that need structured access to workspace data
✓Autonomous agents that need to write state changes to Fibery (e.g., task creation, status updates)
✓Workflow automation systems that create entities as side effects of decisions
✓Integration pipelines that ingest external data and populate Fibery workspace
✓Generic Fibery agents that need to work across different workspace configurations
✓Integration builders who want schema-driven mutation/query generation

Known Limitations

⚠Query complexity limited by MCP message size constraints and Fibery GraphQL depth limits
⚠No built-in query result caching — each request hits Fibery API, adding latency for repeated queries
⚠Requires explicit schema knowledge or discovery step to construct valid queries; no automatic schema introspection exposed
⚠Read-only by default for query operations; mutations require separate capability
⚠Mutations are synchronous and blocking — no async job queue for bulk operations
⚠No transaction support across multiple entity mutations; partial failures may leave workspace in inconsistent state

Requirements

Fibery workspace with API access enabledValid Fibery API token (workspace-level authentication)MCP client implementation (e.g., Claude Desktop, custom MCP host)Network connectivity to Fibery API endpointsFibery workspace with write API permissions enabledAPI token with mutation scope (not read-only)Knowledge of target entity type schema (field names, types, required fields)MCP client with tool-calling capability

Input / Output

Accepts: structured query parameters (entity type, filters, field selections), natural language query intent (delegated to LLM for translation), GraphQL query strings (advanced use case), entity type identifier, field name-value pairs (JSON object), relationship references (entity IDs or unique identifiers), optional: batch operation arrays, optional: entity type filter (discover schema for specific type only), optional: field name filter, MCP protocol messages (tool calls, resource requests), Fibery API token (configuration), source entity ID, relationship name/type, optional: field filters for linked entities, optional: depth limit for traversal, array of entity creation/update/deletion requests, each request: entity type, fields, optional ID for updates, entity type to query, filter conditions (field name, operator, value), optional: sort order and pagination

Produces: JSON entity objects with requested fields, Paginated result sets with metadata, Relationship graphs (entity + linked entities), created/updated entity object with assigned ID and computed fields, mutation result status (success/error), validation error details if mutation fails, workspace schema object (entity types array), entity type definitions (fields, relationships, constraints), field metadata (type, required, enum values, validation rules), MCP protocol responses (tool results, resource contents), error messages with MCP-standard error codes, array of linked entity objects, relationship metadata (cardinality, type), nested entity structures for multi-level traversal, array of operation results (success/error per entity), created/updated entity objects with IDs, error details for failed operations, filtered entity array, result count and pagination metadata

UnfragileRank

Adoption15%(25% weight)

Quality16%(25% weight)

Ecosystem30%(15% weight)

Match Graph25%(30% 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

7 capabilities

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** - Perform queries and entity operations in your [Fibery](https://fibery.io) workspace.

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Capabilities7 decomposed

workspace-entity-querying-via-mcp

Medium confidence

Solves for

Best for

AI agent developers building autonomous workflows that need to read Fibery workspace state

Teams integrating Fibery as a knowledge source for LLM-powered decision systems

Developers building Claude/LLM tools that need structured access to workspace data

Requires

Fibery workspace with API access enabled

Valid Fibery API token (workspace-level authentication)

MCP client implementation (e.g., Claude Desktop, custom MCP host)

Limitations

Query complexity limited by MCP message size constraints and Fibery GraphQL depth limits

No built-in query result caching — each request hits Fibery API, adding latency for repeated queries

Requires explicit schema knowledge or discovery step to construct valid queries; no automatic schema introspection exposed

What makes it unique

vs alternatives

entity-creation-and-mutation

Medium confidence

Solves for

Best for

Autonomous agents that need to write state changes to Fibery (e.g., task creation, status updates)

Workflow automation systems that create entities as side effects of decisions

Integration pipelines that ingest external data and populate Fibery workspace

Requires

Fibery workspace with write API permissions enabled

API token with mutation scope (not read-only)

Knowledge of target entity type schema (field names, types, required fields)

Limitations

Mutations are synchronous and blocking — no async job queue for bulk operations

No transaction support across multiple entity mutations; partial failures may leave workspace in inconsistent state

Field validation errors are returned to agent but require agent-side retry logic

What makes it unique

vs alternatives

workspace-schema-discovery

Medium confidence

Solves for

Best for

Generic Fibery agents that need to work across different workspace configurations

Integration builders who want schema-driven mutation/query generation

Teams with evolving workspace schemas that need agents to adapt automatically

Requires

Fibery workspace with schema introspection API enabled

API token with read access to workspace metadata

MCP client to invoke schema discovery tools

Limitations

Schema introspection adds latency on first call; no built-in caching mechanism

Custom field types or plugins may not be fully exposed in schema metadata

Permissions-based field visibility not reflected in schema — agent may attempt queries on fields it lacks access to

What makes it unique

vs alternatives

mcp-protocol-transport-and-authentication

Medium confidence

Solves for

Best for

Teams deploying Fibery MCP server as a bridge between Claude/LLMs and workspace

Developers building custom MCP hosts that need Fibery integration

Organizations requiring secure credential management for workspace API access

Requires

Fibery workspace API token (generated in workspace settings)

MCP client implementation (Claude Desktop, custom MCP host, etc.)

Network access from MCP server to Fibery API endpoints

Limitations

Authentication is token-based; no OAuth2 or SAML support for multi-user scenarios

Single workspace per MCP server instance — multi-workspace support requires multiple server instances

Rate limiting enforced by Fibery API; MCP server does not implement client-side queuing or backoff

What makes it unique

vs alternatives

relationship-and-linked-entity-traversal

Medium confidence

Solves for

Best for

Agents building rich context from interconnected workspace data

Workflow systems that need to understand entity dependencies and relationships

Knowledge graph builders that map Fibery workspace structure

Requires

Fibery workspace with relationship definitions between entity types

Query capability (read access to related entities)

Understanding of workspace relationship schema

Limitations

Deep relationship traversal (3+ levels) may hit GraphQL query complexity limits

No built-in cycle detection for circular relationships; agent must handle cycles

Relationship queries return full linked entity objects; no lazy loading or pagination within relationships

What makes it unique

vs alternatives

batch-entity-operations

Medium confidence

Solves for

Best for

Integration pipelines importing bulk data into Fibery

Agents performing bulk state updates based on workflow decisions

Teams needing to minimize API call overhead for high-volume operations

Requires

Fibery workspace with write API permissions

API token with mutation scope

Batch size within Fibery API limits

Limitations

Batch size limited by Fibery API (typically 100-1000 items per batch)

No transactional guarantees — partial failures leave some entities modified

Batch operations are sequential; no parallelization within batch

What makes it unique

vs alternatives

Reduces API call overhead compared to individual entity mutations; agents can batch 100 operations into 1 call instead of 100 calls, improving latency and throughput for bulk workflows.

field-value-filtering-and-search

Medium confidence

Solves for

Best for

Agents querying workspace for entities matching specific conditions

Workflow systems that need to find entities by state or attributes

Search-driven agents that filter large collections efficiently

Requires

Knowledge of entity field names and types

Understanding of filter syntax (exact match, range, text search operators)

Query capability (read access to entities)

Limitations

Filter syntax must match Fibery GraphQL where clause format; complex filters may be unintuitive for agents

Full-text search limited to text fields; no cross-field search

Regex or fuzzy matching not supported; only exact and prefix matching

What makes it unique

vs alternatives

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

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Fibery

Capabilities7 decomposed

workspace-entity-querying-via-mcp

entity-creation-and-mutation

workspace-schema-discovery

mcp-protocol-transport-and-authentication

relationship-and-linked-entity-traversal

batch-entity-operations

field-value-filtering-and-search

Related Artifactssharing capabilities

SingleStore

Google Admin MCP

Tinybird

@taazkareem/clickup-mcp-server

Dart

Baserow

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Fibery

Are you the builder of Fibery?

Get the weekly brief

Data Sources

Fibery

Capabilities7 decomposed

workspace-entity-querying-via-mcp

entity-creation-and-mutation

workspace-schema-discovery

mcp-protocol-transport-and-authentication

relationship-and-linked-entity-traversal

batch-entity-operations

field-value-filtering-and-search

Related Artifactssharing capabilities

SingleStore

Google Admin MCP

Tinybird

@taazkareem/clickup-mcp-server

Dart

Baserow

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Fibery

Are you the builder of Fibery?

Get the weekly brief

Data Sources