Fieldflow
MCP server for fieldflow
What is Fieldflow?
Fieldflow is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to mcp server for fieldflow
MCP server for fieldflow
This server falls under the Business Applications category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- MCP server for fieldflow
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx fieldflowConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Fieldflow
FieldFlow is an MCP server that acts as a smart proxy between AI assistants and any REST API, using an OpenAPI specification to dynamically generate tools from your API's endpoints. It supports field selection with JSONPath-like syntax so AI agents retrieve only the fields they need, reducing token usage and noise. It handles Bearer token, API key, and Basic authentication out of the box, making it practical for connecting AI workflows to business APIs in operations management, CRM, ERP, or any system with an OpenAPI spec.
Prerequisites
- Python 3.9 or higher
- pip package manager
- An OpenAPI JSON or YAML specification file for the target REST API
- Authentication credentials for the target API (Bearer token, API key, or Basic auth credentials)
- An MCP-compatible client such as Claude Desktop
Clone the repository and install dependencies
Clone the FieldFlow repository and install it with the MCP extras enabled. This installs the FastMCP framework and all required dependencies.
git clone https://github.com/guillaumegay13/fieldflow.git
cd fieldflow
python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -e '.[mcp]'Provide your OpenAPI specification
Place your target API's OpenAPI JSON or YAML specification file in an accessible location. Set the FIELD_FLOW_OPENAPI_SPEC_PATH environment variable to its path. The default is examples/jsonplaceholder_openapi.yaml.
export FIELD_FLOW_OPENAPI_SPEC_PATH=/path/to/your/api-spec.yaml
export FIELD_FLOW_TARGET_API_BASE_URL=https://api.example.comConfigure authentication
Set the authentication environment variables to match your API's auth method. FieldFlow supports Bearer tokens, API keys, and Basic auth.
# Bearer token / OAuth:
export FIELDFLOW_AUTH_TYPE=bearer
export FIELDFLOW_AUTH_VALUE=your-access-token
# API Key:
export FIELDFLOW_AUTH_TYPE=apikey
export FIELDFLOW_AUTH_HEADER=X-API-Key
export FIELDFLOW_AUTH_VALUE=your-api-key
# Basic auth:
export FIELDFLOW_AUTH_TYPE=basic
export FIELDFLOW_AUTH_VALUE=base64-encoded-user:passwordStart the FieldFlow HTTP server
Launch the FieldFlow server. It reads the OpenAPI spec and dynamically generates MCP tools for each API endpoint.
fieldflow serve-http --reloadConfigure your MCP client
Add FieldFlow to your MCP client configuration, passing the required environment variables for the spec path, target API, and authentication.
{
"mcpServers": {
"fieldflow": {
"command": "python",
"args": ["-m", "fieldflow"],
"env": {
"FIELD_FLOW_OPENAPI_SPEC_PATH": "/path/to/api-spec.yaml",
"FIELD_FLOW_TARGET_API_BASE_URL": "https://api.example.com",
"FIELDFLOW_AUTH_TYPE": "bearer",
"FIELDFLOW_AUTH_VALUE": "your-access-token"
}
}
}
}Fieldflow Examples
Client configuration
Claude Desktop configuration using FieldFlow with a Bearer token to proxy requests to a custom REST API.
{
"mcpServers": {
"fieldflow": {
"command": "python",
"args": ["-m", "fieldflow"],
"env": {
"FIELD_FLOW_OPENAPI_SPEC_PATH": "/Users/yourname/api-specs/my-api.yaml",
"FIELD_FLOW_TARGET_API_BASE_URL": "https://api.example.com",
"FIELDFLOW_AUTH_TYPE": "bearer",
"FIELDFLOW_AUTH_VALUE": "your-bearer-token"
}
}
}
}Prompts to try
These prompts show how to use FieldFlow to query a REST API with field selection to retrieve only the data needed.
- "Get user ID 1 from the API and return only their name and email"
- "List all posts from the API and show only the title and userId fields"
- "Fetch the details for product SKU-9876 and return the name, price, and stock level"
- "Get Pokémon #150 from the API and show only its name and types[].type.name"Troubleshooting Fieldflow
FieldFlow fails to parse the OpenAPI spec on startup
Verify that FIELD_FLOW_OPENAPI_SPEC_PATH points to a valid, well-formed OpenAPI 3.x JSON or YAML file. Check the file for syntax errors using a validator such as https://editor.swagger.io and ensure the path is absolute.
API requests return 401 Unauthorized errors
Confirm that FIELDFLOW_AUTH_TYPE matches the target API's auth scheme and that FIELDFLOW_AUTH_VALUE contains a valid, non-expired credential. For API key auth, also verify FIELDFLOW_AUTH_HEADER matches the header name expected by the API.
Generated tools do not match the expected API endpoints
FieldFlow derives tools directly from the OpenAPI spec's operation definitions. If endpoints are missing, check that they are defined in the spec with valid operationId fields. Regenerate or update the spec from the API provider's documentation.
Frequently Asked Questions about Fieldflow
What is Fieldflow?
Fieldflow is a Model Context Protocol (MCP) server that mcp server for fieldflow It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Fieldflow?
Follow the installation instructions on the Fieldflow GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Fieldflow?
Fieldflow works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Fieldflow free to use?
Yes, Fieldflow is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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