Model Context Protocol server for continue.dev
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-alexsmirnov-mcps","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Model Context Protocol ( MCP ) Python server to use with continue.dev MCP server that exposes a customizable prompt templates, resources, and tools It uses FastMCP to run as server application. Dependencies, build, and run managed by uv tool. ## Provided functionality ### prompts prompts created from markdown files in `prompts` folder. Additional content can be added by templating, by variable names in {{variable}} format Initial list of prompts: - review code created by another llm - check code for readability, confirm with *Clean Code* rules - Use a conversational LLM to hone in on an idea - wrap out at the end of the brainstorm to save it as `spec.md` file - test driven development, to create tests from spec - Draft a detailed, step-by-step blueprint for building project from spec ### resources **NOTE: continue does not understand templates, so resource name should contain all information** **resouce name left as is in prompt, so it should not confuse llm** - extract url content as markdown - full documentation about libraries, preferable from llms-full.txt - complete project structure and content, created by `CodeWeawer` or `Repomix` ### tools - web search, using `serper` or - web search results with summary, by `perplexity.io` - find missed tests - run unit tests and collect errors
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.