My Skills
FastMCP
MCP servers built on FastMCP — an async tool layer behind a PyPI-published memory server, a production system-of-record server, an HR server designed fail-closed, and a multiuser Telegram server binding each caller to their own account — with tool surfaces curated for agents rather than generated from an API.
Details & related links
FastMCP is the framework under every MCP server I build, and the skill is knowing which decisions the framework leaves to you. The framework handles the protocol; the server's worth is decided in what its tools say and refuse. Four servers exercise that: a durable agent-memory server published to PyPI and listed in the official MCP Registry, whose async tool layer fronts thread-scoped storage with full-text, semantic, and hybrid retrieval; a production system-of-record server that turns a project-tracking platform into agent-callable tools, plus a second, deliberately narrow endpoint exposing only the search and fetch pair ChatGPT's Deep Research connector accepts; an HR-platform server designed fail-closed around a tiered sensitivity model before the first tool shipped, its broad tier now live in production; and a multiuser Telegram server that binds every caller to their own account through a self-service enrollment ceremony, read-only by default.
The recurring craft across them: tool surfaces curated for an agent's context budget rather than generated one-to-one from an upstream API — responses trimmed to what the caller needs with raw payloads one parameter away, errors written as corrections an agent can act on rather than status codes, per-user credentials whose responses are never cached, and partial degradation that marks the inaccessible corner instead of failing the whole call. Servers run across stdio and streamable HTTP transports, into Claude Code, agent runtimes, and gateway deployments alike. The four servers are in the Related Projects panel above.