{"uid":"cap_XbJ6FidKCDKVLuLuZRTGn","slug":"repo-to-rag-mcp-ea162f43","name":"repo-to-rag-mcp","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/repo-to-rag-mcp/api/process","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"payload":{"type":"string"}}},"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.25","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.25/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.25","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.25","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_U7Ao6GY2jR6J-asqa2kH-","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.25","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts a code repository into a RAG-ready (Retrieval-Augmented Generation) knowledge base via an MCP-compatible endpoint","exampleAgentPrompt":"Take this repository content and process it into a RAG-ready knowledge base so I can query it with an LLM — here's the raw repo data: [repo contents].","exampleUseCases":[{"title":"Index open-source library for AI queries","prompt":"I want to make the contents of this open-source Python library queryable by my AI agent — process this repo data into a RAG-ready format so the agent can answer questions about the codebase."},{"title":"Prepare internal codebase for developer assistant","prompt":"Take our internal monorepo source and convert it into a retrieval-ready corpus so our coding assistant can answer questions about how our services are structured."},{"title":"Build searchable docs from repo for support bot","prompt":"I have a GitHub repo with documentation and code — can you process this repository data into chunked, RAG-ready documents so our support bot can look up implementation details?"}],"resultDescription":"Returns a processed, structured representation of the repository suitable for ingestion into a RAG pipeline — typically chunked text segments, code blocks, or indexed documents ready for embedding and vector store loading.","failureModes":["Empty or malformed 'data' field returns a 400-level error","Repository data too large for single request may time out or be truncated","Non-text binary content in repo may be skipped or cause parsing errors","Payment failure (x402) results in 402 Payment Required before processing begins","Malformed repo structure may produce low-quality or incomplete RAG chunks"],"whenToPreferThis":"Choose this endpoint when you need to convert raw repository or source code content into a RAG-compatible format for LLM retrieval, especially in agentic workflows where paying per-call via x402 is acceptable and you need an MCP-compatible output. Prefer this over manual chunking pipelines when you want a single-step, hosted transformation without managing embedding infrastructure yourself.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T13:14:44.939Z","isFirstParty":false}