{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-idosal--git-mcp","slug":"idosal--git-mcp","name":"git-mcp","type":"mcp","url":"https://gitmcp.io","page_url":"https://unfragile.ai/idosal--git-mcp","categories":["mcp-servers"],"tags":["agentic-ai","agents","ai","claude","copilot","cursor","git","llm","mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-idosal--git-mcp__cap_0","uri":"capability://tool.use.integration.remote.mcp.server.endpoint.generation","name":"remote-mcp-server-endpoint-generation","description":"Transforms GitHub repository URLs into standardized Model Context Protocol server endpoints using pattern-matching and subdomain routing. GitMCP operates as a Cloudflare Workers application that exposes repository-specific MCP servers at predictable URLs (gitmcp.io/{owner}/{repo} or {owner}.gitmcp.io/{repo}), enabling AI assistants to connect to any GitHub project without manual configuration. The system maintains a ToolIndex that serves as the central coordinator for all repository-specific and common tools, dynamically generating MCP tool definitions based on repository content.","intents":["I want to connect Claude or Cursor to a GitHub repository without setting up infrastructure","I need to expose a repository as an MCP server that multiple AI assistants can access","I want to generate MCP endpoints on-demand for any public GitHub project"],"best_for":["AI assistant developers integrating with GitHub repositories","Teams using Claude, Cursor, or Copilot who want grounded context","Solo developers prototyping LLM agents with real codebase access"],"limitations":["Requires public GitHub repositories — private repos need authentication setup","Serverless architecture on Cloudflare Workers may have cold-start latency for first requests","URL routing patterns are fixed (subdomain or path-based) — custom routing not supported"],"requires":["Public GitHub repository URL","MCP-compatible AI assistant (Claude, Cursor, Copilot, or custom client)","Network access to gitmcp.io or self-hosted Cloudflare Workers deployment"],"input_types":["GitHub repository URL (string)","Repository owner and name (string)"],"output_types":["MCP server endpoint URL (string)","MCP tool definitions (JSON schema)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_1","uri":"capability://memory.knowledge.intelligent.documentation.fetching.with.fallback.priority","name":"intelligent-documentation-fetching-with-fallback-priority","description":"Implements a smart documentation discovery pipeline that prioritizes llms.txt → AI-optimized documentation → README.md with intelligent fallback logic. The system fetches repository documentation from GitHub using the GitHub API, applies content prioritization rules, and caches results to minimize API calls. This ensures AI assistants receive the most relevant, human-curated documentation first, reducing hallucinations by grounding responses in actual project documentation rather than training data.","intents":["I want Claude to read my project's llms.txt file first before falling back to README","I need the AI to access the most relevant documentation for a repository automatically","I want to ensure AI responses are based on official docs, not hallucinated information"],"best_for":["Open-source maintainers who want AI assistants to reference official documentation","Teams building AI agents that need grounded, hallucination-free responses","Projects with multiple documentation sources (llms.txt, docs/, README.md)"],"limitations":["Fallback chain is fixed (llms.txt → docs → README) — custom priority ordering not supported","Large documentation files (>1MB) may be truncated or cached incompletely","GitHub API rate limits apply (60 req/hour unauthenticated, 5000 req/hour authenticated)"],"requires":["Public GitHub repository with at least one documentation file","GitHub API access (unauthenticated or authenticated with token)","Cloudflare Workers KV or external storage for caching"],"input_types":["GitHub repository URL (string)","Repository owner and name (string)"],"output_types":["Documentation content (markdown or plaintext)","Documentation metadata (source file, size, last updated)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_10","uri":"capability://automation.workflow.cloudflare.workers.serverless.deployment","name":"cloudflare-workers-serverless-deployment","description":"Deploys GitMCP as a serverless application on Cloudflare Workers, eliminating infrastructure management and providing global edge distribution. The system uses Wrangler configuration (wrangler.jsonc) to define worker routes, environment variables, and service bindings (KV storage, Vectorize, FalkorDB). Deployment is automated through Cloudflare's deployment pipeline, with automatic scaling and zero cold-start latency through edge caching. This architecture enables GitMCP to serve requests from locations near users with minimal latency.","intents":["I want to deploy GitMCP without managing servers or infrastructure","I need global edge distribution for low-latency MCP access","I want automatic scaling without capacity planning"],"best_for":["Teams without DevOps expertise","Projects requiring global distribution","Startups and small teams with limited infrastructure budget"],"limitations":["Cloudflare Workers have CPU time limits (~30 seconds per request) — long-running operations may timeout","Memory limits are strict (~128MB) — very large repositories may exceed memory","Cold-start latency is minimal but first request to a worker may be slower","Vendor lock-in to Cloudflare ecosystem — migration to other platforms requires significant refactoring","Pricing is usage-based — high-traffic deployments may become expensive"],"requires":["Cloudflare account with Workers enabled","Wrangler CLI installed (Node.js 18+)","GitHub repository for source code","Cloudflare KV, Vectorize, and FalkorDB subscriptions (optional but recommended)"],"input_types":["Wrangler configuration (TOML/JSON)","Worker code (TypeScript/JavaScript)","Environment variables (string key-value pairs)"],"output_types":["Deployed worker URL (string)","Deployment logs (text)","Worker metrics (JSON)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_11","uri":"capability://safety.moderation.context.aware.ai.hallucination.reduction","name":"context-aware-ai-hallucination-reduction","description":"Reduces AI hallucinations by providing grounded, real-time access to repository documentation and code through MCP tools. Instead of relying on training data, AI assistants can query actual repository content (documentation, code, dependencies) through the MCP interface. The system ensures responses are based on current repository state rather than outdated or incorrect training data. This is achieved through the combination of documentation fetching, semantic search, and code analysis capabilities that provide authoritative sources for AI responses.","intents":["I want Claude to answer questions about my repository based on actual code, not hallucinations","I need the AI to reference current documentation instead of making up API details","I want to verify that AI responses are grounded in real repository content"],"best_for":["Teams using AI assistants for code understanding and documentation","Projects where hallucinations are costly (security-sensitive code, critical systems)","Organizations building AI-powered developer tools"],"limitations":["Hallucination reduction depends on AI assistant's ability to use tools — some models may ignore tool results","Coverage is limited to repository content — AI may still hallucinate about external dependencies or third-party libraries","Real-time updates require fresh API calls — cached content may be stale","AI may still hallucinate if tools don't provide complete information"],"requires":["MCP-compatible AI assistant that supports tool use","Repository with comprehensive documentation and code","Network access to GitMCP server"],"input_types":["Natural language question about repository (string)","Repository context (GitHub URL)"],"output_types":["Grounded AI response with tool references (text)","Source citations (documentation files, code locations)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_2","uri":"capability://search.retrieval.semantic.search.through.documentation.with.vectorize","name":"semantic-search-through-documentation-with-vectorize","description":"Provides semantic search capabilities over repository documentation using Cloudflare Vectorize for embeddings generation and vector similarity search. The system processes documentation content into embeddings, stores them in a vector database, and enables AI assistants to find relevant documentation sections through natural language queries rather than keyword matching. This allows context-aware retrieval where queries like 'how do I authenticate' can find relevant sections even if they don't contain those exact words.","intents":["I want Claude to search documentation semantically, not just by keywords","I need to find relevant code examples and documentation sections by meaning","I want the AI to understand that 'login' and 'authenticate' refer to the same concept"],"best_for":["Large repositories with extensive documentation (1000+ pages)","Projects where keyword search fails due to domain-specific terminology","Teams building AI agents that need semantic understanding of documentation"],"limitations":["Vectorize embeddings have fixed dimensionality (384 or 1536 depending on model) — no custom embedding models","Semantic search adds ~100-200ms latency per query due to embedding generation","Vector database updates are asynchronous — newly added documentation may not be searchable immediately","Requires Cloudflare Vectorize subscription — not available in free tier"],"requires":["Cloudflare Vectorize API access and subscription","Documentation content in text format (markdown, plaintext, or extracted from code)","Minimum 100 tokens of documentation for meaningful embeddings"],"input_types":["Natural language query (string)","Documentation content (markdown, plaintext)"],"output_types":["Ranked list of relevant documentation sections (JSON with similarity scores)","Embedding vectors (float arrays)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_3","uri":"capability://code.generation.editing.code.graph.analysis.with.falkordb","name":"code-graph-analysis-with-falkordb","description":"Analyzes repository code structure and relationships using FalkorDB graph database integration, enabling AI assistants to understand code dependencies, function calls, and module relationships. The system builds a code graph from repository files, stores it in FalkorDB, and exposes graph queries through MCP tools. This allows AI assistants to answer questions like 'what functions call this method' or 'what are the dependencies of this module' by traversing the code graph rather than searching raw files.","intents":["I want Claude to understand how functions and modules are connected in my codebase","I need the AI to find all callers of a specific function without searching raw text","I want to analyze code dependencies and impact of changes using AI"],"best_for":["Large codebases (10k+ lines) where dependency analysis is critical","Teams building AI-powered code review or refactoring tools","Projects with complex module hierarchies and cross-file dependencies"],"limitations":["Graph construction is language-specific — only supports languages with AST parsers (JavaScript, Python, Go, etc.)","Graph updates require re-indexing entire repository — incremental updates not supported","FalkorDB queries have timeout limits (~5 seconds) — very large graphs may timeout","Memory overhead for storing graph in FalkorDB scales with codebase size"],"requires":["FalkorDB API access and authentication","Repository code in supported language (JavaScript, TypeScript, Python, Go, Java, etc.)","Minimum 100 lines of code for meaningful graph structure"],"input_types":["Repository code files (source code in supported languages)","Graph query (natural language or graph query syntax)"],"output_types":["Code graph structure (nodes and edges representing functions, modules, dependencies)","Query results (list of related functions, dependency chains, impact analysis)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_4","uri":"capability://tool.use.integration.repository.handler.system.with.specialization","name":"repository-handler-system-with-specialization","description":"Implements a pluggable repository handler architecture that supports both generic and specialized handlers for different repository types. The system uses a handler registry that routes requests to appropriate handlers based on repository characteristics (e.g., ThreejsRepoHandler for three.js, GenericHandler for dynamic repositories). Each handler implements repository-specific optimizations like custom documentation processing, code analysis strategies, or tool generation logic. This allows GitMCP to provide tailored experiences for popular projects while maintaining fallback support for any GitHub repository.","intents":["I want GitMCP to handle my specialized repository type with custom logic","I need different documentation processing for different project types","I want to extend GitMCP with custom handlers for my repository"],"best_for":["Maintainers of popular open-source projects who want custom MCP integration","Teams extending GitMCP with domain-specific handlers","Projects with non-standard documentation structures or code organization"],"limitations":["Handler registration is hardcoded in ToolIndex — no dynamic handler discovery","Specialized handlers require custom TypeScript code — no configuration-based customization","Handler selection is based on repository name/owner — no content-based detection","Each new handler adds complexity to the codebase and deployment"],"requires":["TypeScript knowledge to implement custom handlers","Understanding of MCP tool schema and GitMCP handler interface","Access to GitMCP source code repository for contributing handlers"],"input_types":["Repository metadata (owner, name, URL)","Repository content (files, documentation, code)"],"output_types":["Handler instance (specialized or generic)","Repository-specific MCP tools (JSON schema)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_5","uri":"capability://tool.use.integration.multi.ai.assistant.compatibility.via.mcp.protocol","name":"multi-ai-assistant-compatibility-via-mcp-protocol","description":"Provides standardized MCP protocol compatibility enabling GitMCP to work with 8+ AI assistants (Claude, Cursor, Copilot, custom clients) without modification. The system implements the Model Context Protocol specification, exposing tools through a standard JSON schema that any MCP-compatible client can consume. This abstraction layer ensures that repository context is accessible to any AI assistant that supports MCP, regardless of the underlying LLM or client implementation.","intents":["I want to use the same repository context across Claude, Cursor, and Copilot","I need my custom AI agent to access GitHub repositories via MCP","I want to avoid vendor lock-in by using a standard protocol"],"best_for":["Teams using multiple AI assistants (Claude + Cursor + Copilot)","Developers building custom MCP clients or agents","Organizations standardizing on MCP for AI integrations"],"limitations":["MCP protocol version compatibility depends on client implementation — older clients may not support all features","Tool schema is standardized but tool capabilities vary by client (some clients may not support all tool types)","Authentication and authorization are client-specific — GitMCP doesn't enforce access control"],"requires":["MCP-compatible AI assistant or client (Claude 3.5+, Cursor, Copilot, custom implementation)","MCP client library or native MCP support in the AI assistant","Network access to GitMCP server endpoint"],"input_types":["MCP tool request (JSON with tool name and arguments)","Repository context (GitHub URL or owner/repo)"],"output_types":["MCP tool response (JSON with tool results)","Tool definitions (MCP schema)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_6","uri":"capability://tool.use.integration.github.api.integration.with.rate.limit.handling","name":"github-api-integration-with-rate-limit-handling","description":"Integrates with GitHub API to fetch repository content, metadata, and documentation with built-in rate limit handling and caching strategies. The system makes authenticated and unauthenticated GitHub API calls, respects rate limit headers, and implements exponential backoff for retries. Caching is performed at multiple levels (Cloudflare KV, in-memory) to minimize API calls and improve performance. This allows GitMCP to reliably access any public GitHub repository while staying within API quotas.","intents":["I want GitMCP to fetch repository content without hitting GitHub API rate limits","I need reliable access to GitHub repositories even under high load","I want to cache repository data to reduce API calls"],"best_for":["High-traffic MCP servers serving many concurrent users","Teams with limited GitHub API quotas","Projects requiring reliable, cached access to repository data"],"limitations":["Unauthenticated API calls are limited to 60 requests/hour — authenticated calls allow 5000 requests/hour","Cache invalidation is time-based (TTL) — no real-time updates for changed repository content","GitHub API has endpoint-specific rate limits — some operations may be slower than others","Private repositories require GitHub authentication token — not supported in public GitMCP instance"],"requires":["Public GitHub repository","GitHub API access (unauthenticated or with personal access token)","Cloudflare KV storage for caching (or alternative cache backend)"],"input_types":["GitHub repository URL (string)","GitHub API endpoint (string)","GitHub authentication token (optional, string)"],"output_types":["Repository content (files, metadata, documentation)","API response (JSON)","Cache status (hit/miss, TTL)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_7","uri":"capability://automation.workflow.web.interface.url.conversion.and.subdomain.routing","name":"web-interface-url-conversion-and-subdomain-routing","description":"Provides a web interface that converts GitHub URLs into MCP server endpoints and handles both subdomain-based ({owner}.gitmcp.io/{repo}) and path-based (gitmcp.io/{owner}/{repo}) routing. The system uses pattern matching to extract repository metadata from URLs, validates GitHub repository existence, and generates shareable MCP endpoint URLs. The frontend (built with Remix) displays the generated endpoint and provides copy-to-clipboard functionality for easy sharing with AI assistants.","intents":["I want to convert a GitHub URL into an MCP endpoint URL quickly","I need to share a repository's MCP endpoint with teammates","I want to see the MCP endpoint for a repository without manual configuration"],"best_for":["Non-technical users who want to generate MCP endpoints without CLI","Teams sharing repository context across AI assistants","Quick prototyping and testing of MCP integration"],"limitations":["URL conversion is one-way — no reverse lookup from MCP endpoint to GitHub URL","Subdomain routing requires DNS configuration — path-based routing is more reliable","URL validation is basic — doesn't verify repository actually exists on GitHub","No authentication required — any GitHub URL can be converted (privacy concern for private repos)"],"requires":["GitHub repository URL (public or private)","Web browser with JavaScript enabled","Network access to gitmcp.io"],"input_types":["GitHub repository URL (string, e.g., https://github.com/owner/repo)"],"output_types":["MCP endpoint URL (string, e.g., https://gitmcp.io/owner/repo)","Subdomain variant (string, e.g., https://owner.gitmcp.io/repo)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_8","uri":"capability://data.processing.analysis.documentation.processing.pipeline.with.content.extraction","name":"documentation-processing-pipeline-with-content-extraction","description":"Implements a multi-stage documentation processing pipeline that extracts, normalizes, and structures content from various documentation formats (markdown, plaintext, code comments). The system parses documentation files, extracts metadata (title, description, code examples), and converts them into a standardized format suitable for AI consumption. This pipeline includes deduplication, formatting normalization, and optional content filtering to ensure documentation is clean and AI-friendly.","intents":["I want my documentation automatically processed and formatted for AI consumption","I need to extract code examples and structured information from markdown docs","I want to normalize documentation across different formats and styles"],"best_for":["Projects with large, complex documentation in multiple formats","Teams wanting to improve AI assistant understanding of documentation","Documentation-heavy projects (frameworks, libraries, platforms)"],"limitations":["Processing pipeline is optimized for markdown and plaintext — binary formats (PDF, images) not supported","Content extraction is heuristic-based — may miss or misinterpret complex documentation structures","Processing adds latency (~100-500ms per document) — large documentation sets may be slow","Metadata extraction is limited to standard markdown frontmatter — custom metadata formats not supported"],"requires":["Documentation in markdown or plaintext format","Minimum 100 characters of content per document","Standard markdown structure (headings, code blocks, links)"],"input_types":["Documentation files (markdown, plaintext)","Raw documentation content (string)"],"output_types":["Processed documentation (normalized markdown)","Extracted metadata (JSON with title, description, examples)","Structured content (sections, code blocks, links)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-idosal--git-mcp__cap_9","uri":"capability://tool.use.integration.tool.schema.generation.and.validation","name":"tool-schema-generation-and-validation","description":"Generates and validates MCP tool schemas dynamically based on repository content and handler specifications. The system creates JSON schemas for each tool (e.g., 'search_documentation', 'analyze_code'), validates schema correctness against MCP specification, and exposes tools through the MCP protocol. Tool schemas include input parameters, output types, and descriptions that enable AI assistants to understand how to use each tool. The system ensures schema consistency and compatibility across different repository handlers.","intents":["I want to automatically generate MCP tool schemas for my repository","I need to ensure my tools are compatible with MCP clients","I want to validate that my tool definitions are correct"],"best_for":["Developers building MCP servers and tools","Teams extending GitMCP with custom tools","Projects requiring strict schema validation"],"limitations":["Schema generation is based on handler implementation — no automatic schema inference from code","Validation is against MCP specification — custom schema extensions may not be supported","Schema updates require redeployment — no hot-reloading of tool definitions","Complex tool parameters may require manual schema definition"],"requires":["MCP specification knowledge","Repository handler implementation","JSON schema validation library"],"input_types":["Handler tool definitions (TypeScript/JavaScript objects)","Repository metadata (owner, name, type)"],"output_types":["MCP tool schema (JSON schema)","Validation results (boolean + error messages)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Public GitHub repository URL","MCP-compatible AI assistant (Claude, Cursor, Copilot, or custom client)","Network access to gitmcp.io or self-hosted Cloudflare Workers deployment","Public GitHub repository with at least one documentation file","GitHub API access (unauthenticated or authenticated with token)","Cloudflare Workers KV or external storage for caching","Cloudflare account with Workers enabled","Wrangler CLI installed (Node.js 18+)","GitHub repository for source code","Cloudflare KV, Vectorize, and FalkorDB subscriptions (optional but recommended)"],"failure_modes":["Requires public GitHub repositories — private repos need authentication setup","Serverless architecture on Cloudflare Workers may have cold-start latency for first requests","URL routing patterns are fixed (subdomain or path-based) — custom routing not supported","Fallback chain is fixed (llms.txt → docs → README) — custom priority ordering not supported","Large documentation files (>1MB) may be truncated or cached incompletely","GitHub API rate limits apply (60 req/hour unauthenticated, 5000 req/hour authenticated)","Cloudflare Workers have CPU time limits (~30 seconds per request) — long-running operations may timeout","Memory limits are strict (~128MB) — very large repositories may exceed memory","Cold-start latency is minimal but first request to a worker may be slower","Vendor lock-in to Cloudflare ecosystem — migration to other platforms requires significant refactoring","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6339821717481424,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-03-13T01:21:48Z"},"community":{"stars":8014,"forks":709,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=idosal--git-mcp","compare_url":"https://unfragile.ai/compare?artifact=idosal--git-mcp"}},"signature":"CJaMT5poG6suNyfgYriPT/BPxJh9vz8acBZkpCYsFzHDyYd6FR9gilU3vDieLz9Yovwxi4wUxsq28qHBXUWPDA==","signedAt":"2026-06-20T06:55:48.581Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/idosal--git-mcp","artifact":"https://unfragile.ai/idosal--git-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=idosal--git-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}