Bloomy MCP
The Bloomy MCP server enables interaction with Bloom Growth's GraphQL API through the Model Context Protocol, facilitating AI assistant integration. You can:
Execute GraphQL queries and mutations with optional variables
Retrieve detailed information about specific queries and mutations, including argument requirements, return types, and usage examples
Get the ID of the currently authenticated user associated with the API token
View available GraphQL operations through resource endpoints (
bloom://queriesandbloom://mutations)Benefit from automatic schema introspection for effective development and debugging
Connects to Bloom Growth's GraphQL API, enabling AI assistants to perform operations against the Bloom Growth platform, including executing queries and mutations, retrieving schema information, and accessing authenticated user data.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Bloomy MCP show me my recent company goals"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Bloomy MCP
A Model Context Protocol (MCP) server for interacting with Bloom Growth's GraphQL API.
Overview
Bloomy MCP is a server that connects to Bloom Growth's GraphQL API and exposes it through the Model Context Protocol, enabling AI assistants to perform operations against the Bloom Growth platform.
Related MCP server: Flow MCP Server
Features
Query Bloom Growth GraphQL API through MCP
Retrieve query and mutation details
Execute GraphQL queries and mutations via MCP tools
Get authenticated user information
Automatic schema introspection
Installation
Prerequisites
Python 3.12 or higher
Access to Bloom Growth API
uv (recommended) or pip for package management
Package Management
This project recommends using uv, a fast Python package installer and resolver that serves as a drop-in replacement for pip/pip-tools. It's significantly faster than traditional package managers.
Installing uv
curl -sSf https://astral.sh/uv/install.sh | shFor other installation methods, see the uv documentation.
Setup
Clone this repository
Set up a Python virtual environment:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall the package in development mode:
Using pip:
pip install -e .Using uv (recommended):
uv pip install -e .For development dependencies:
uv pip install -e ".[dev]"
Environment Variables
Create a .env file with the following variables:
BLOOM_API_URL=<Your Bloom API URL>
BLOOM_API_TOKEN=<Your Bloom API Token>Usage
Cursor Integration
To use this MCP server with Cursor (AI-powered IDE):
Go to Cursor > Cursor Settings > MCP
Click on "Add new MCP server"
Configure the server with the following details:
Name: "Bloom Growth" (or "BG" or any name you prefer)
Type: Command
Command:
uv run --project /path/to/your/repo/ --env-file /path/to/your/repo/.env bloomy-server
Important: Replace
/path/to/your/repo/with the actual path to your bloomy-mcp repository (e.g.,/Users/username/workspace/bloomy-mcp/).
Running the Server
Start the Bloomy MCP server:
bloomy-serverDevelopment Mode Inspection
For development and debugging purposes, you can use the MCP inspector tool:
npx @modelcontextprotocol/inspector bloomy-serverThis allows you to inspect the MCP server's behavior and responses during development.
Recommended Tools
For optimal development workflow:
direnv: Use for managing environment variables and automatically loading them when entering the project directory
uv: Use for fast and reliable package management
Setting up direnv:
Install direnv (e.g.,
brew install direnvon macOS)Create a
.envrcfile in your project root:export BLOOM_API_URL=your_api_url export BLOOM_API_TOKEN=your_api_tokenRun
direnv allowto authorize the environment variables
This combination of tools (direnv + uv) provides an efficient environment for both secrets management and package management.
Available MCP Tools
The following MCP tools are available for AI assistants:
get_query_details- Get detailed information about specific GraphQL queriesget_mutation_details- Get detailed information about specific GraphQL mutationsexecute_query- Execute a GraphQL query or mutation with variablesget_authenticated_user_id- Get the ID of the currently authenticated user
Available MCP Resources
bloom://queries- Get a list of all available queriesbloom://mutations- Get a list of all available mutations
Development
Project Structure
src/
└── bloomy_mcp/
├── __init__.py # Package initialization
├── client.py # GraphQL client implementation
├── formatters.py # Data formatting utilities
├── introspection.py # GraphQL schema introspection
├── operations.py # GraphQL operation utilities
└── server.py # MCP server implementationDependencies
mcp[cli]- Model Context Protocol servergql- GraphQL client libraryhttpx- HTTP clientpyyaml- YAML processing
Available Tools
4 toolsexecute_queryA
Execute a GraphQL query or mutation with variables.
Parses and executes the provided GraphQL operation string with optional variables.
Args:
query: Raw GraphQL query or mutation string
variables: Optional dictionary of variables to use in the operation
Returns:
Dictionary containing the operation results or an error message string
Raises:
Exception: Handled internally, returns error message as string
Inputs: query (required), variables.
get_authenticated_user_idA
Get the ID of the currently authenticated user.
Uses a special mutation to retrieve the ID of the user associated with
the current API token.
Returns:
User ID string if successful, None if user not found, or error message string
Raises:
Exception: Handled internally, returns error message as string
Inputs: no named parameters.
get_mutation_detailsA
Get detailed information about specific GraphQL mutations.
Retrieves argument requirements, return type information, descriptions, and
example usage for the specified mutations.
Args:
mutation_names: Comma-separated list of mutation names to get details for
Returns:
A YAML-formatted string containing detailed information about the requested mutations
Inputs: mutation_names (required).
get_query_detailsA
Get detailed information about specific GraphQL queries.
Retrieves argument requirements, return type information, descriptions, and
example usage for the specified queries.
Args:
query_names: Comma-separated list of query names to get details for
Returns:
A YAML-formatted string containing detailed information about the requested queries
Inputs: query_names (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: execute_query performs operations, get_authenticated_user_id handles authentication, and get_mutation_details/get_query_details provide metadata. The two detail tools are differentiated by query vs mutation focus, preventing confusion.
All tools follow a consistent verb_noun naming pattern with snake_case: execute_query, get_authenticated_user_id, get_mutation_details, get_query_details. The pattern is predictable and readable throughout the set.
Four tools is well-scoped for a GraphQL-focused server: one for execution, one for authentication, and two for schema introspection. Each tool earns its place without bloat or thin coverage for the domain.
The surface covers core GraphQL workflows well: execution, authentication, and schema details for both queries and mutations. A minor gap is the lack of a tool for subscription details or general schema exploration beyond specific queries/mutations, but agents can work around this.
Maintenance
Related MCP Connectors
A Model Context Protocol server for Wix AI tools
The Grafbase MCP server sits in front of a GraphQL API and exposes an MCP protocol-compliant interface that allows AI agents and LLMs to explore and query GraphQL APIs using natural language. It provides tools to search schemas, introspect types and fields, and execute GraphQL queries while minimizing context bloat by returning only relevant schema subsets, with built-in support for authentication, authorization, and configurable access control.
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables LLMs to interact with GraphQL APIs by providing schema introspection and query execution capabilities.936 npm3MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables AI assistants to access Flow blockchain data and perform operations such as checking balances, resolving domains, executing scripts, and submitting transactions.1MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables LLMs to interact with GraphQL APIs by providing schema introspection and query execution capabilities.6 npmMIT
- FlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that provides AI models with structured access to external data and services, acting as a bridge between AI assistants and applications, databases, and APIs in a standardized, secure way.2-