by guillaumegay13 · MCP Server · ★ 111
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FieldFlow FieldFlow turns OpenAPI-described REST endpoints into selectively filtered tools. It generates Pydantic models and FastAPI routes that forward requests to the upstream API and return only the fields the caller asks for. An optional MCP layer exposes those generated OpenAPI tools to Model Context Protocol clients such as Claude Desktop. Features Discovers endpoints and schemas from OpenAPI 3.0 JSON or YAML files. Builds request/response Pydantic models dynamically, preserving aliases and optional fields. Generates FastAPI routes that accept parameters plus an optional list to slice responses. Proxies requests with , automatically formatting URL paths and query parameters. Works with any OpenAPI-compliant spec, including nested schemas and refs. What FieldFlow Supports Today FieldFlow currently has two supported application modes: exposes generated HTTP tool endpoints from an OpenAPI spec. exposes the same generated OpenAPI tools through MCP over stdio. FieldFlow does not yet wrap arbitrary third-party MCP servers. That direction is planned around MCP tools that expose structured , but it is not part of the current release.
| Stars | 111 |
| Forks | 11 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.6848067618086/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-20 |
| Created | 2025-09-15 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
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fieldflow is an open-source mcp server by guillaumegay13 with 111 GitHub stars.
fieldflow is primarily written in Python. It covers topics such as ai, ai-agent, ai-agents.
You can find installation instructions and usage details in the fieldflow GitHub repository at github.com/guillaumegay13/fieldflow. The project has 111 stars and 11 forks, indicating an active community.
fieldflow is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fieldflow on Agent Skills Hub include LLM-Agents-Ecosystem-Handbook, web-agent-protocol, robloxstudio-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.