{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-idosal-git-mcp","slug":"mcp-idosal-git-mcp","name":"git-mcp","type":"mcp","url":"https://github.com/idosal/git-mcp","page_url":"https://unfragile.ai/mcp-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_mcp-idosal-git-mcp__cap_0","uri":"capability://tool.use.integration.mcp.compliant.repository.tool.exposure.via.serverless.workers","name":"mcp-compliant repository tool exposure via serverless workers","description":"Exposes GitHub repositories as standardized Model Context Protocol servers running on Cloudflare Workers, transforming repository data into AI-accessible tools without requiring local installation. The system uses URL pattern matching to route requests to repository-specific handlers (ThreejsRepoHandler, GenericHandler) that dynamically generate MCP-compatible tool schemas, enabling Claude, Copilot, Cursor, and other AI assistants to invoke repository operations through a unified protocol interface.","intents":["Connect an AI assistant to a GitHub repository without installing local tooling","Expose repository documentation and code as callable tools for LLM agents","Support multiple AI platforms (Claude, Copilot, Cursor) with a single MCP server","Enable dynamic repository selection through generic documentation endpoints"],"best_for":["AI agent developers building multi-tool workflows","Teams integrating GitHub repositories with Claude or Copilot","LLM application builders needing standardized repository access"],"limitations":["Serverless execution model limits long-running operations; Cloudflare Workers have 30-second timeout constraints","No built-in persistence for tool state across requests; requires external storage for stateful operations","MCP protocol version compatibility depends on client implementation; older AI assistants may not support all tool features"],"requires":["GitHub repository URL (public or private with token)","MCP-compatible AI assistant (Claude 3.5+, Copilot with MCP support, Cursor)","Cloudflare Workers deployment environment or gitmcp.io hosted instance"],"input_types":["GitHub repository URLs","MCP tool invocation requests (JSON-RPC format)","Repository metadata (owner, repo name)"],"output_types":["MCP tool definitions (JSON schema)","Tool execution results (structured JSON)","Documentation and code snippets"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_1","uri":"capability://memory.knowledge.intelligent.documentation.prioritization.with.fallback.resolution","name":"intelligent documentation prioritization with fallback resolution","description":"Implements a three-tier documentation fetching strategy that prioritizes llms.txt (AI-optimized format) → AI-specific documentation → README.md, automatically selecting the most appropriate documentation source for LLM consumption. The system uses GitHub API to detect file presence and content, applying intelligent fallback logic to ensure AI assistants always receive relevant, well-formatted documentation even when preferred formats are unavailable.","intents":["Ensure AI assistants receive documentation in the most LLM-friendly format available","Automatically discover and prioritize AI-optimized documentation files","Gracefully degrade to standard README when specialized formats don't exist","Reduce hallucinations by grounding AI responses in actual project documentation"],"best_for":["Repository maintainers wanting to optimize AI assistant access","LLM application builders needing reliable documentation retrieval","Teams with heterogeneous documentation formats across projects"],"limitations":["Fallback chain is fixed; no customization per repository type without code changes","Large documentation files (>1MB) may exceed Cloudflare Workers memory constraints","GitHub API rate limits (60 req/hour unauthenticated, 5000/hour authenticated) can throttle documentation fetching for high-volume usage"],"requires":["GitHub repository with at least one of: llms.txt, AI docs, or README.md","GitHub API access (public repos work without token; private repos require GITHUB_TOKEN env var)","Cloudflare Workers KV or external cache for storing fetched documentation"],"input_types":["GitHub repository URL","Repository owner and name"],"output_types":["Markdown or plain text documentation","Structured documentation metadata (source file, format, size)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_10","uri":"capability://data.processing.analysis.documentation.processing.pipeline.with.format.detection.and.normalization","name":"documentation processing pipeline with format detection and normalization","description":"Implements a multi-stage documentation processing pipeline that detects file formats (markdown, plain text, HTML), normalizes content for LLM consumption, and extracts structured metadata (headings, code blocks, links). The pipeline handles various documentation sources (README.md, llms.txt, custom AI docs) and applies format-specific transformations to ensure consistent, LLM-optimized output regardless of source format.","intents":["Normalize documentation from multiple sources into a consistent format for LLM consumption","Extract structured metadata from documentation (headings, code examples, links)","Handle various documentation formats (markdown, plain text, HTML) transparently","Optimize documentation for token efficiency in LLM context windows"],"best_for":["Repositories with heterogeneous documentation formats","Teams wanting to optimize documentation for LLM consumption","Projects with large documentation that needs token-efficient summarization"],"limitations":["Format detection is heuristic-based; may misidentify formats with ambiguous content","Normalization may lose formatting nuances (e.g., custom markdown extensions)","Large documentation files (>1MB) may exceed processing time limits in serverless environment","Metadata extraction is format-specific; some documentation types may not support all metadata types"],"requires":["Documentation in supported formats (markdown, plain text, HTML)","Cloudflare Workers environment with sufficient CPU time for processing"],"input_types":["Raw documentation files (markdown, plain text, HTML)","File metadata (filename, MIME type)"],"output_types":["Normalized documentation (markdown or plain text)","Structured metadata (JSON with headings, code blocks, links)","Token count estimates for LLM context planning"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_11","uri":"capability://tool.use.integration.tool.schema.generation.with.parameter.validation.and.type.safety","name":"tool schema generation with parameter validation and type safety","description":"Generates MCP-compliant tool schemas with full parameter validation, type definitions, and usage examples, ensuring AI assistants can invoke tools correctly with proper input validation. The system creates JSON schemas for each tool, specifying required/optional parameters, parameter types, constraints, and examples, enabling AI assistants to understand tool capabilities and invoke them with correct arguments.","intents":["Define tool parameters and constraints in a format AI assistants can understand","Validate tool invocation parameters before execution","Provide usage examples and parameter descriptions to AI assistants","Enable type-safe tool invocation across different AI platforms"],"best_for":["Complex tools with many parameters and constraints","Teams building AI agents that need reliable tool invocation","Projects requiring strict parameter validation"],"limitations":["JSON schema generation is static; complex parameter dependencies cannot be expressed","Parameter validation is schema-based; runtime validation logic must be implemented separately","Schema changes require tool redefinition; no versioning for backward compatibility","Large schemas may exceed MCP protocol limits; complex tools may need to be split"],"requires":["Tool definitions with parameter specifications","JSON schema generation library (built into GitMCP)"],"input_types":["Tool definitions (name, description, parameters)","Parameter specifications (type, required, constraints)"],"output_types":["MCP tool schema (JSON)","Parameter validation rules","Usage examples"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_12","uri":"capability://memory.knowledge.real.time.repository.content.access.without.local.cloning","name":"real-time repository content access without local cloning","description":"Enables AI assistants to access repository content (files, code, documentation) via GitHub API without requiring local repository clones, reducing setup time and storage overhead. The system fetches file contents on-demand via GitHub API, caches frequently accessed files in KV, and streams large files to avoid memory exhaustion, allowing AI assistants to work with repositories of any size.","intents":["Access repository files without cloning the repository locally","Reduce setup time and storage requirements for AI agent workflows","Work with large repositories that would be impractical to clone","Access private repositories without exposing credentials to AI assistants"],"best_for":["Large repositories where cloning is impractical","AI agents needing quick access to specific files","Teams with limited storage or bandwidth"],"limitations":["GitHub API rate limits constrain file access volume; high-frequency file access may hit limits","File access is slower than local disk access; ~50-200ms latency per file fetch","Binary files and large files (>1MB) may be difficult to process; text-only optimization","No support for uncommitted changes or local branches; only committed code is accessible"],"requires":["GitHub API access (public repos work without authentication)","GITHUB_TOKEN for private repository access","Cloudflare Workers KV for caching frequently accessed files"],"input_types":["Repository owner and name","File paths (relative to repository root)"],"output_types":["File contents (text or base64-encoded)","File metadata (size, encoding, last modified)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_2","uri":"capability://search.retrieval.semantic.search.over.repository.documentation.via.vector.embeddings","name":"semantic search over repository documentation via vector embeddings","description":"Integrates Cloudflare Vectorize to generate embeddings for repository documentation, enabling semantic search queries that find relevant content by meaning rather than keyword matching. The system processes documentation text into vector embeddings, stores them in Vectorize, and executes cosine-similarity searches to return contextually relevant documentation snippets when AI assistants query the repository.","intents":["Search repository documentation by semantic meaning, not just keywords","Find relevant code examples and API documentation for a given query","Reduce context window usage by retrieving only semantically relevant documentation","Enable natural language queries against repository knowledge bases"],"best_for":["Large repositories with extensive documentation","Teams building RAG-augmented LLM agents over codebases","Projects where keyword search produces too many false positives"],"limitations":["Vectorize integration requires Cloudflare Workers Pro plan; not available on free tier","Embedding generation adds ~100-500ms latency per search query depending on documentation size","Vector index updates are not real-time; documentation changes require re-indexing (batch operation)","Semantic search quality depends on embedding model quality; may miss domain-specific terminology without fine-tuning"],"requires":["Cloudflare Vectorize API access (Pro plan minimum)","Documentation content in text format (extracted from markdown, code comments, etc.)","Cloudflare Workers environment with Vectorize binding configured"],"input_types":["Natural language search queries (text)","Documentation content (markdown, plain text)"],"output_types":["Ranked list of documentation snippets (JSON with similarity scores)","Relevant code examples and API references"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_3","uri":"capability://data.processing.analysis.code.graph.analysis.and.repository.structure.indexing.via.falkordb","name":"code graph analysis and repository structure indexing via falkordb","description":"Integrates FalkorDB graph database to index repository code structure, enabling queries that traverse code relationships (imports, function calls, class hierarchies) and analyze code patterns. The system builds a code graph from GitHub API responses, storing nodes (files, functions, classes) and edges (dependencies, calls), allowing AI assistants to understand code organization and answer structural questions without parsing source files directly.","intents":["Query code structure and dependencies without downloading the entire repository","Find all usages of a function or class across the codebase","Understand module dependencies and import chains","Analyze code architecture and identify circular dependencies"],"best_for":["Large codebases where full parsing is expensive","AI agents needing to understand code relationships for refactoring or analysis","Teams building code intelligence tools on top of LLMs"],"limitations":["FalkorDB integration requires external service connection; adds network latency (~50-200ms per query)","Graph accuracy depends on GitHub API's code analysis capabilities; complex language features may not be fully indexed","No real-time graph updates; code changes require re-indexing via batch jobs","Graph queries are limited to relationships exposed by GitHub API; low-level control flow analysis not available"],"requires":["FalkorDB instance (self-hosted or cloud service)","GitHub API access with sufficient rate limits for code analysis","Repository with indexable code (supported languages: Python, JavaScript, TypeScript, Java, Go, etc.)"],"input_types":["Repository URL","Code structure queries (natural language or graph query syntax)"],"output_types":["Graph query results (nodes and edges in JSON)","Code relationship maps (dependency graphs, call chains)","Structural analysis (circular dependencies, unused code)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_4","uri":"capability://tool.use.integration.repository.specific.handler.specialization.with.dynamic.tool.generation","name":"repository-specific handler specialization with dynamic tool generation","description":"Implements a handler registry pattern where specialized handlers (ThreejsRepoHandler, GenericHandler) generate repository-specific MCP tools tailored to each project's structure and conventions. The ToolIndex coordinator selects appropriate handlers based on repository metadata, generating custom tool schemas that expose repository-specific operations (e.g., Three.js example browsing, build system queries) alongside common tools (documentation search, code lookup).","intents":["Generate AI-accessible tools customized to a repository's unique structure and conventions","Expose repository-specific operations (build commands, example browsing, test running) as callable tools","Support different tool sets for different project types without code duplication","Enable AI assistants to understand and interact with project-specific workflows"],"best_for":["Framework and library maintainers wanting to optimize AI assistant integration","Teams building specialized AI agents for specific project types","Large organizations with heterogeneous repository structures"],"limitations":["Adding new specialized handlers requires code changes and redeployment; no runtime handler registration","Handler selection is based on repository metadata heuristics; may misclassify projects and apply wrong handler","Tool schema generation is synchronous; complex repositories may exceed Cloudflare Workers CPU time limits","No versioning for tool schemas; breaking changes to tools affect all connected AI assistants immediately"],"requires":["Repository metadata accessible via GitHub API (owner, repo name, topics, description)","Custom handler implementation for specialized repository types","Cloudflare Workers deployment with handler registry configuration"],"input_types":["Repository URL and metadata","Handler selection criteria (repository type, language, framework)"],"output_types":["MCP tool definitions (JSON schema)","Repository-specific tool implementations","Tool metadata (description, parameters, examples)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_5","uri":"capability://automation.workflow.url.pattern.routing.and.subdomain.based.repository.selection","name":"url pattern routing and subdomain-based repository selection","description":"Implements sophisticated URL pattern matching to route requests to appropriate repository handlers, supporting both path-based (gitmcp.io/{owner}/{repo}) and subdomain-based (owner.gitmcp.io/{repo}) repository selection. The routing system extracts repository metadata from URLs, validates against GitHub, and selects specialized handlers, enabling flexible repository addressing and dynamic repository selection through generic endpoints.","intents":["Route AI assistant requests to the correct repository handler based on URL patterns","Support multiple URL formats for the same repository (path-based and subdomain-based)","Enable dynamic repository selection without pre-registering repositories","Simplify MCP server configuration by deriving repository context from URLs"],"best_for":["Multi-repository MCP deployments serving many projects","Teams wanting flexible repository addressing without configuration","Hosted MCP services supporting arbitrary GitHub repositories"],"limitations":["Subdomain routing requires wildcard DNS configuration; not suitable for all hosting environments","URL parsing adds ~5-10ms latency per request; complex patterns may impact performance","No caching of repository metadata; each request validates against GitHub API (subject to rate limits)","URL pattern conflicts may cause requests to route to wrong handlers; requires careful pattern ordering"],"requires":["Cloudflare Workers or similar serverless platform with URL routing capabilities","GitHub API access to validate repository existence and metadata","DNS configuration supporting wildcard subdomains (for subdomain-based routing)"],"input_types":["HTTP request URLs","Repository owner and name (extracted from URL)"],"output_types":["Routed requests to appropriate handler","Repository metadata (owner, repo, handler type)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_6","uri":"capability://tool.use.integration.github.api.integration.with.authentication.and.rate.limit.handling","name":"github api integration with authentication and rate limit handling","description":"Integrates GitHub API for repository data fetching, documentation retrieval, and code analysis, with support for both public and private repositories via token-based authentication. The system manages GitHub API rate limits (60 req/hour unauthenticated, 5000/hour authenticated), implements request batching and caching to minimize API calls, and handles authentication via GITHUB_TOKEN environment variable for private repository access.","intents":["Fetch repository documentation and code from GitHub without cloning","Access private repositories with proper authentication","Retrieve repository metadata (topics, description, language) for handler selection","Analyze code structure and dependencies via GitHub API"],"best_for":["Teams integrating with private GitHub repositories","High-volume MCP deployments needing efficient API usage","Organizations with GitHub Enterprise requiring custom authentication"],"limitations":["GitHub API rate limits constrain request volume; 5000 req/hour authenticated limit may be insufficient for high-traffic deployments","API responses are paginated; large repositories require multiple requests to fetch complete data","GitHub API does not expose low-level code analysis (AST, control flow); limited to file-level operations","Authentication via environment variables is not suitable for multi-tenant deployments; requires per-user token management"],"requires":["GitHub API access (public repos work without authentication)","GITHUB_TOKEN environment variable for private repository access","Cloudflare Workers environment with environment variable support"],"input_types":["Repository owner and name","GitHub API endpoints (REST API v3)"],"output_types":["Repository metadata (JSON)","File contents (raw or base64-encoded)","Code structure and dependency information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_7","uri":"capability://memory.knowledge.cloudflare.workers.kv.based.caching.and.storage.layer","name":"cloudflare workers kv-based caching and storage layer","description":"Uses Cloudflare Workers KV (key-value store) as a distributed cache for repository documentation, code snippets, and API responses, reducing GitHub API calls and improving response latency. The system implements cache invalidation strategies (TTL-based expiration, manual invalidation) and stores frequently accessed data (documentation, code graphs, tool schemas) in KV for sub-millisecond retrieval across global edge locations.","intents":["Cache repository documentation to reduce GitHub API calls and improve response speed","Store code analysis results (graphs, dependency maps) for quick retrieval","Distribute cached data globally via Cloudflare edge network","Implement cache invalidation when repository content changes"],"best_for":["High-traffic MCP deployments serving many concurrent AI assistants","Repositories with stable documentation and infrequent code changes","Global deployments needing low-latency access to cached data"],"limitations":["KV has eventual consistency model; recently updated data may not be immediately visible across all edge locations","KV storage is limited to 25MB per key; large repositories may require splitting data across multiple keys","Cache invalidation is manual or TTL-based; no automatic invalidation on GitHub webhook events without additional infrastructure","KV pricing scales with storage and operations; high-volume deployments may incur significant costs"],"requires":["Cloudflare Workers KV binding configured in wrangler.jsonc","Cache key naming strategy to avoid collisions across repositories","TTL configuration for cache expiration (typically 1-24 hours for documentation)"],"input_types":["Repository metadata (owner, repo name)","Cache keys (derived from repository and content type)"],"output_types":["Cached documentation and code snippets","Cache hit/miss metadata","Stored API responses"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_8","uri":"capability://tool.use.integration.multi.ai.assistant.protocol.compatibility.and.tool.invocation","name":"multi-ai-assistant protocol compatibility and tool invocation","description":"Implements MCP protocol compatibility with 8+ AI assistants (Claude, Copilot, Cursor, etc.), handling tool invocation requests in MCP JSON-RPC format and returning results in assistant-specific formats. The system abstracts away protocol differences between assistants, exposing a unified tool interface that each client can invoke according to its MCP implementation, enabling the same repository tools to work across heterogeneous AI platforms.","intents":["Use the same MCP server with multiple AI assistants without modification","Invoke repository tools from Claude, Copilot, Cursor, and other MCP-compatible assistants","Handle tool invocation requests in MCP JSON-RPC format","Return tool results in formats compatible with each AI assistant"],"best_for":["Teams using multiple AI assistants and wanting unified repository access","Organizations standardizing on MCP for AI tool integration","Developers building AI agents that need to work across platforms"],"limitations":["MCP protocol support varies across AI assistants; older versions may not support all tool features","Tool result formatting must be compatible with each assistant's MCP implementation; complex results may require custom serialization","No built-in protocol version negotiation; incompatibilities may cause silent failures","Assistant-specific features (e.g., Claude's vision capabilities) cannot be leveraged through generic MCP interface"],"requires":["MCP-compatible AI assistant (Claude 3.5+, Copilot with MCP support, Cursor, etc.)","MCP client configuration pointing to gitmcp.io server URL","MCP protocol version compatibility (typically 2024-11 or later)"],"input_types":["MCP JSON-RPC tool invocation requests","Tool parameters (JSON)"],"output_types":["MCP JSON-RPC tool results","Structured data (JSON) or text responses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-idosal-git-mcp__cap_9","uri":"capability://automation.workflow.web.interface.for.url.to.mcp.server.conversion.and.configuration","name":"web interface for url-to-mcp-server conversion and configuration","description":"Provides a web UI (built with Remix/React) that converts GitHub repository URLs into MCP server endpoints, displays MCP configuration instructions for different AI assistants, and offers a chat interface for testing repository tools. The interface generates platform-specific setup guides (Claude, Copilot, Cursor) and allows users to preview tool availability before connecting to their AI assistant.","intents":["Convert a GitHub URL into an MCP server endpoint without technical knowledge","View MCP configuration instructions for different AI assistants","Test repository tools through a web-based chat interface","Preview available tools before connecting to an AI assistant"],"best_for":["Non-technical users wanting to connect repositories to AI assistants","Teams evaluating GitMCP before full integration","Documentation and onboarding purposes"],"limitations":["Web UI is read-only for tool testing; cannot execute tools that modify repositories","Chat interface is limited to testing; not suitable for production AI agent workflows","UI is hosted on gitmcp.io; no self-hosted web interface option provided","Configuration instructions may become outdated as AI assistants update their MCP implementations"],"requires":["Web browser with JavaScript support","GitHub repository URL (public or private with token)"],"input_types":["GitHub repository URLs","Natural language queries for tool testing"],"output_types":["MCP server endpoint URLs","Configuration instructions (markdown)","Tool test results (chat interface)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["GitHub repository URL (public or private with token)","MCP-compatible AI assistant (Claude 3.5+, Copilot with MCP support, Cursor)","Cloudflare Workers deployment environment or gitmcp.io hosted instance","GitHub repository with at least one of: llms.txt, AI docs, or README.md","GitHub API access (public repos work without token; private repos require GITHUB_TOKEN env var)","Cloudflare Workers KV or external cache for storing fetched documentation","Documentation in supported formats (markdown, plain text, HTML)","Cloudflare Workers environment with sufficient CPU time for processing","Tool definitions with parameter specifications","JSON schema generation library (built into GitMCP)"],"failure_modes":["Serverless execution model limits long-running operations; Cloudflare Workers have 30-second timeout constraints","No built-in persistence for tool state across requests; requires external storage for stateful operations","MCP protocol version compatibility depends on client implementation; older AI assistants may not support all tool features","Fallback chain is fixed; no customization per repository type without code changes","Large documentation files (>1MB) may exceed Cloudflare Workers memory constraints","GitHub API rate limits (60 req/hour unauthenticated, 5000/hour authenticated) can throttle documentation fetching for high-volume usage","Format detection is heuristic-based; may misidentify formats with ambiguous content","Normalization may lose formatting nuances (e.g., custom markdown extensions)","Large documentation files (>1MB) may exceed processing time limits in serverless environment","Metadata extraction is format-specific; some documentation types may not support all metadata types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6339821717481424,"quality":0.5,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","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=mcp-idosal-git-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-idosal-git-mcp"}},"signature":"mPHIfsgWTegX8aX9bFNZJ3msaW8/DuVmn++F2rGr88PSG1OR6RsKuWJ+0XdzhPQvrdDEbmMjt7sK915pBgTWDA==","signedAt":"2026-06-19T18:01:51.931Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-idosal-git-mcp","artifact":"https://unfragile.ai/mcp-idosal-git-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-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"}}