Fibery MCP Server
Connect your Fibery workspace to automate work management — query entities, create tasks, and manage comments directly from your AI agent.
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Fibery MCP Server : voyez votre AI Agent en action
Capacités intégrées (11)
add_comment
Add a comment to an entity
create_entity
Create a new entity in a specific database
delete_entity
Delete an entity
get_comments
Retrieve comments for a specific entity
get_entity
Get a specific entity by its UUID
get_schema
Retrieve the full schema of the workspace, including all databases (types) and fields
list_apps
List all Fibery apps (spaces)
list_users
List all users in the Fibery workspace
query_entities
Query entities from a specific database (type)
search_entities
Search for entities by keyword across all databases
update_entity
Update an existing entity
Ce que ce connecteur débloque
Fibery is a work management platform that adapts to your unique processes. This MCP server allows your AI agent to interact with your Fibery workspace seamlessly.
Key Features
- Space & Schema Discovery — List all your spaces (apps) and retrieve the full schema to understand your custom databases and fields.
- Entity Management — Query, create, update, and delete entities across any of your custom databases flawlessly.
- Comment Integration — Read and add comments to entities to keep your team in sync natively.
- Advanced Querying — Use granular filters and field selections to retrieve exactly the data you need synchronously.
- Cross-Database Search — Search for information across your entire workspace flawlessly through the agent.
How it works
1. Subscribe to this server 2. Enter your Fibery workspace name and API Token 3. Start managing your projects and tasks from Claude, Cursor, or any MCP-compatible clientWho is this for?
- Product Managers — automate task updates and insight gathering without manual portal navigation
- Software Teams — sync development progress and manage bug tracking directly from your chat workflow
- Operations Teams — pull structured data and manage custom workflows through simple natural language queries
Questions fréquemment posées
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