Fastapi

We expose our internal knowledge base over both REST and MCP using fastapi-mcp, which mounts existing FastAPI routes as MCP tools: no second server, no protocol-translation proxy. Two tools matter here: search_kb (semantic retrieval) and ask_kb (retrieval plus an LLM synthesis call that returns a cited answer). search_kb worked flawlessly everywhere. ask_kb failed, intermittently, and the failure was maddeningly opaque. In the MCP client it surfaced as nothing more than: Command failed with no output No stack trace. No error payload. Just silence. And only sometimes. ...

In a previous post we rebuilt our company knowledge base server from an MCP SSE endpoint to a plain FastAPI REST server because MCP client support was too fragmented to be reliable. The conclusion was: REST is the pragmatic choice, MCP can come back when the ecosystem matures. The ecosystem has matured faster than expected. We added MCP back on top of the FastAPI server and it now works across every client simultaneously: claude.ai, Claude Teams (company-wide), Claude desktop, ChatGPT, and OpenWebUI. This is what we learned in the process. ...

We built an internal knowledge base server to give our AI agents access to Conselara’s company data: capabilities, past performance, GSA rates, certifications. The idea was straightforward: expose it as an MCP server so any AI client could query it semantically. It worked in Claude Code. It worked nowhere else. What MCP promises The Model Context Protocol is Anthropic’s open standard for connecting AI models to external tools and data sources. The pitch is compelling: define your server once, and any MCP-compatible client can call it. Claude Code has native MCP support. The ecosystem is growing. ...