{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-modelcontextprotocol-servers","slug":"mcp-modelcontextprotocol-servers","name":"servers","type":"mcp","url":"https://github.com/modelcontextprotocol/servers","page_url":"https://unfragile.ai/mcp-modelcontextprotocol-servers","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-modelcontextprotocol-servers__cap_0","uri":"capability://tool.use.integration.mcp.server.protocol.implementation.with.json.rpc.transport","name":"mcp server protocol implementation with json-rpc transport","description":"Implements the Model Context Protocol as a standardized JSON-RPC 2.0 server that exposes capabilities (Tools, Resources, Prompts, Roots) to LLM clients through bidirectional message passing. Uses transport-agnostic architecture supporting stdio, HTTP, and WebSocket transports, with automatic request/response routing and error handling via the MCP SDK. The protocol enables clients to discover and invoke server capabilities through a well-defined capability negotiation handshake.","intents":["Build a server that exposes tools and resources to Claude or other LLM clients","Create a standardized interface for LLM-to-tool communication without custom protocols","Enable multiple transport mechanisms (stdio, HTTP, WebSocket) without changing server code","Implement capability discovery so clients know what tools and resources are available"],"best_for":["Teams building LLM agent backends that need standardized tool integration","Developers creating Claude plugins or integrations with Anthropic's ecosystem","Organizations standardizing on MCP for LLM-to-system communication"],"limitations":["JSON-RPC overhead adds ~50-100ms per request/response cycle compared to direct function calls","Transport layer must be configured per deployment (stdio for local, HTTP for remote)","No built-in authentication/authorization — security must be implemented at transport or application layer","Synchronous request/response model may not suit streaming or long-polling use cases"],"requires":["TypeScript SDK (@modelcontextprotocol/sdk) or Python SDK (mcp package)","Node.js 18+ (TypeScript) or Python 3.9+ (Python)","MCP client implementation (Claude, custom client, or MCP client library)"],"input_types":["JSON-RPC requests with method names and parameters","Tool invocation requests with typed arguments","Resource URIs and prompt requests"],"output_types":["JSON-RPC responses with tool results","Resource content (text, binary, structured data)","Prompt templates and completions"],"categories":["tool-use-integration","protocol-implementation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_1","uri":"capability://tool.use.integration.tool.definition.and.invocation.with.schema.based.validation","name":"tool definition and invocation with schema-based validation","description":"Enables servers to define tools as JSON Schema-validated functions that LLM clients can discover and invoke. Tools are registered with the MCP server using the SDK's tool registry, which validates input parameters against their schemas before execution and returns typed results. The schema-based approach allows clients to understand tool capabilities, required/optional parameters, and return types without documentation, enabling automatic tool selection and parameter binding by LLM agents.","intents":["Define a tool that an LLM can call with validated parameters","Let LLM clients automatically discover what tools are available and their signatures","Validate tool inputs before execution to prevent invalid calls","Build LLM agents that can reliably invoke tools with correct parameter types"],"best_for":["Developers building LLM agents that need type-safe tool invocation","Teams integrating multiple tools into a single MCP server","Organizations requiring parameter validation before tool execution"],"limitations":["JSON Schema validation adds ~10-20ms per tool invocation","Complex nested schemas may be difficult for LLMs to reason about — simpler schemas perform better","No built-in rate limiting or quota management per tool","Tool execution errors must be handled by the server — no automatic retry logic"],"requires":["MCP SDK (TypeScript or Python)","JSON Schema definitions for tool parameters","Tool implementation function matching the schema signature"],"input_types":["JSON Schema object definitions","Tool parameter objects matching the schema","Tool invocation requests from MCP clients"],"output_types":["Tool execution results (text, structured data, or binary)","Error responses with error codes and messages","Tool metadata including name, description, and schema"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_10","uri":"capability://tool.use.integration.mcp.sdk.with.typescript.and.python.bindings.for.server.development","name":"mcp sdk with typescript and python bindings for server development","description":"Provides official SDKs in TypeScript and Python that abstract MCP protocol details and provide high-level APIs for building MCP servers. The SDKs handle JSON-RPC message routing, transport management, capability registration, and error handling, allowing developers to focus on implementing business logic. The TypeScript SDK uses class-based server definitions with decorators for capability registration, while the Python SDK uses similar patterns with Python conventions. Both SDKs support multiple transport mechanisms (stdio, HTTP, WebSocket) through a pluggable transport layer.","intents":["Build MCP servers without manually implementing JSON-RPC protocol details","Register tools, resources, prompts, and roots using high-level APIs","Support multiple transport mechanisms without changing server code","Integrate MCP servers into existing TypeScript or Python applications"],"best_for":["TypeScript/JavaScript developers building MCP servers","Python developers integrating MCP into existing applications","Teams standardizing on MCP across multiple server implementations"],"limitations":["SDK abstractions add ~50-100ms overhead per request compared to raw protocol implementation","TypeScript SDK requires Node.js 18+ — not suitable for browser-based servers","Python SDK requires Python 3.9+ — older Python versions not supported","Custom transport implementations require extending SDK base classes"],"requires":["TypeScript SDK: Node.js 18+, npm or yarn","Python SDK: Python 3.9+, pip","Basic understanding of async/await (TypeScript) or asyncio (Python)"],"input_types":["Tool definitions (name, description, schema)","Resource metadata (name, description, MIME type)","Prompt templates (text with parameter placeholders)","Root directory configurations"],"output_types":["MCP server instances ready for client connections","JSON-RPC message handlers","Transport-specific server implementations"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_11","uri":"capability://tool.use.integration.transport.abstraction.layer.with.stdio.http.and.websocket.support","name":"transport abstraction layer with stdio, http, and websocket support","description":"Implements a pluggable transport layer that allows MCP servers to communicate over multiple protocols (stdio for local processes, HTTP for remote clients, WebSocket for bidirectional web communication) without changing server code. The transport layer handles protocol-specific details like message framing, connection management, and error handling, exposing a unified interface to the server implementation. This enables the same server code to be deployed in different environments (CLI, web service, embedded) by simply changing the transport configuration.","intents":["Deploy the same MCP server code over different transport mechanisms","Support local process communication (stdio) for CLI tools","Enable remote server access via HTTP or WebSocket","Build servers that work in multiple deployment environments"],"best_for":["Teams deploying MCP servers across multiple environments","Developers building servers that need to support both local and remote clients","Organizations standardizing on MCP with flexible deployment options"],"limitations":["Transport abstraction adds ~20-50ms overhead per request due to protocol translation","HTTP transport requires request/response semantics — not suitable for streaming","WebSocket transport requires persistent connections — may not work through some proxies","Stdio transport is limited to local process communication — not suitable for remote access"],"requires":["MCP SDK (TypeScript or Python)","Transport-specific dependencies (http module for HTTP, ws for WebSocket)","Server configuration specifying transport type and parameters"],"input_types":["Transport type specification (stdio, HTTP, WebSocket)","Transport configuration (port, host, path)","MCP protocol messages"],"output_types":["Transport-specific connections","Protocol-compliant message routing","Error handling and connection management"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_12","uri":"capability://tool.use.integration.capability.discovery.and.negotiation.with.client.handshake","name":"capability discovery and negotiation with client handshake","description":"Implements MCP protocol handshake that allows clients to discover what capabilities (Tools, Resources, Prompts, Roots) a server exposes before invoking them. The handshake includes server metadata, protocol version negotiation, and capability listings with full schemas. Clients can query the server's capabilities and use this information to determine what operations are available, enabling dynamic tool selection and parameter binding by LLM agents. The implementation ensures version compatibility and allows graceful degradation when clients and servers support different protocol versions.","intents":["Let LLM clients discover what tools and resources a server provides","Enable clients to understand tool schemas and parameter requirements","Negotiate protocol versions between client and server","Build dynamic agents that adapt to available server capabilities"],"best_for":["Developers building flexible LLM agents that adapt to available tools","Teams deploying multiple MCP servers with varying capabilities","Organizations requiring dynamic capability discovery"],"limitations":["Capability discovery adds ~100-200ms to initial connection setup","Large capability lists may exceed message size limits","No built-in capability versioning — breaking changes require protocol version bump","Clients must cache capability information — dynamic capability changes require reconnection"],"requires":["MCP SDK (TypeScript or Python)","Server implementation with registered capabilities","MCP client implementation supporting capability discovery"],"input_types":["Client protocol version","Client capabilities and requirements","Handshake requests"],"output_types":["Server metadata (name, version)","Protocol version agreement","Capability listings with schemas","Server configuration and limits"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_13","uri":"capability://safety.moderation.error.handling.and.response.validation.with.typed.error.codes","name":"error handling and response validation with typed error codes","description":"Implements comprehensive error handling across the MCP protocol with typed error codes, error messages, and optional error data. Servers can return structured errors for invalid requests, tool execution failures, resource access errors, and protocol violations. The error handling includes automatic validation of tool parameters against schemas, resource access checks, and graceful error propagation to clients. Clients can parse error codes to determine error types and implement appropriate recovery strategies.","intents":["Return structured errors when tool invocation fails","Validate tool parameters and return validation errors","Handle resource access errors gracefully","Enable clients to implement error recovery strategies"],"best_for":["Developers building robust MCP servers with comprehensive error handling","Teams implementing error recovery in LLM agents","Organizations requiring detailed error diagnostics"],"limitations":["Error validation adds ~10-20ms per request","Complex error scenarios may require custom error types","Error messages are limited to string format — no structured error data in base protocol","Clients must implement error parsing — no automatic error recovery"],"requires":["MCP SDK (TypeScript or Python)","Error handling implementation in server code","Client implementation that parses error responses"],"input_types":["Tool invocation requests","Resource access requests","Invalid protocol messages"],"output_types":["Error codes (INVALID_REQUEST, TOOL_ERROR, RESOURCE_NOT_FOUND, etc.)","Error messages","Optional error data"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_2","uri":"capability://memory.knowledge.resource.exposure.and.content.serving.with.uri.based.access","name":"resource exposure and content serving with uri-based access","description":"Allows servers to expose resources (files, documents, data) through a URI-based interface that clients can request by name. Resources are registered with metadata (name, description, MIME type) and content is served on-demand when clients request a specific resource URI. This enables LLM clients to access server-side data without direct filesystem access, with support for text, binary, and structured content types. The URI scheme allows servers to implement custom resource resolution logic (e.g., database queries, API calls) behind a simple resource interface.","intents":["Expose files or documents from a server so LLM clients can read them","Serve dynamic content (database records, API responses) as resources","Let LLM clients discover available resources and their content types","Implement access control by filtering resources based on client permissions"],"best_for":["Servers that need to share documents or data with LLM clients","Teams implementing knowledge bases or document stores accessible to LLMs","Organizations requiring controlled access to server-side resources"],"limitations":["No built-in caching — large resources are re-fetched on each client request","Resource listing is static at server startup — dynamic resource discovery requires server restart or custom polling","No streaming support for large resources — entire content must fit in memory","MIME type handling is client-dependent — servers must ensure correct type declaration"],"requires":["MCP SDK with resource registration support","Resource content available at server runtime (files, database, API)","URI scheme and naming convention for resource identification"],"input_types":["Resource URI strings","Resource metadata (name, description, MIME type)","Resource content (text, binary, or structured data)"],"output_types":["Resource content in declared MIME type","Resource metadata and availability list","Error responses for missing or inaccessible resources"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_3","uri":"capability://text.generation.language.prompt.template.definition.and.completion.with.context.injection","name":"prompt template definition and completion with context injection","description":"Enables servers to define reusable prompt templates that LLM clients can request and use for specific tasks. Prompts are registered with the MCP server and can include dynamic parameters that clients provide at invocation time. The server can inject context, examples, or instructions into prompts before returning them to clients, allowing centralized prompt management and versioning. This capability supports multi-turn conversations where prompts can be updated server-side without client changes.","intents":["Define reusable prompts that LLM clients can request by name","Inject dynamic context or examples into prompts at request time","Centralize prompt management so updates don't require client changes","Build prompt libraries that multiple clients can share and version"],"best_for":["Teams managing prompt templates across multiple LLM applications","Organizations requiring centralized prompt versioning and governance","Developers building multi-client systems where prompts need to be synchronized"],"limitations":["Prompt context injection adds ~20-50ms per request","No built-in A/B testing or prompt variant management","Prompts are static templates — complex dynamic logic requires server-side implementation","No automatic prompt optimization or evaluation — quality depends on manual curation"],"requires":["MCP SDK with prompt registration support","Prompt template definitions (text with optional parameter placeholders)","Context data available at server runtime for injection"],"input_types":["Prompt name/identifier","Parameter values for template substitution","Context data for injection"],"output_types":["Completed prompt text with injected context","Prompt metadata (name, description, parameters)","Prompt template definitions"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_4","uri":"capability://tool.use.integration.filesystem.server.with.sandboxed.directory.access.and.path.validation","name":"filesystem server with sandboxed directory access and path validation","description":"Provides a reference implementation of an MCP server that exposes filesystem operations (read, write, list, search) within a sandboxed root directory. The filesystem server validates all paths against the root directory to prevent directory traversal attacks, implements recursive directory listing with filtering, and supports file operations through MCP tools. The implementation demonstrates security best practices including path canonicalization, root directory enforcement, and operation-level access control, making it suitable as a template for building secure file-serving servers.","intents":["Allow LLM clients to read and write files within a controlled directory","Implement secure filesystem access that prevents directory traversal attacks","Enable LLM agents to search and list files in a sandboxed environment","Build a reference implementation for secure file-serving MCP servers"],"best_for":["Developers building LLM agents that need filesystem access","Teams implementing secure file-serving backends for LLM applications","Organizations learning MCP server patterns through reference implementations"],"limitations":["Single root directory constraint — cannot access files outside the configured root","No permission-based access control — all operations are allowed within the root directory","File size limits may apply depending on implementation — large files may cause memory issues","No atomic multi-file operations — each operation is independent"],"requires":["TypeScript SDK (@modelcontextprotocol/sdk)","Node.js 18+","Configured root directory with appropriate filesystem permissions","MCP client implementation"],"input_types":["File paths (relative to root directory)","File content (text or binary)","Search patterns and filters"],"output_types":["File content (text or binary)","Directory listings with file metadata","Search results with file paths and content"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_5","uri":"capability://tool.use.integration.git.repository.operations.server.with.multi.repo.support.and.path.validation","name":"git repository operations server with multi-repo support and path validation","description":"Provides a reference MCP server implementation that exposes Git operations (clone, status, diff, log, commit) as tools that LLM clients can invoke. The server supports multiple repository roots, validates repository paths to prevent escape attacks, and implements operations like branch listing, file history, and repository status. The implementation demonstrates how to wrap command-line Git operations (via subprocess calls) into MCP tools with proper error handling and output parsing, making it a template for building Git-aware LLM agents.","intents":["Enable LLM agents to query Git repository state and history","Allow LLM clients to perform Git operations (commit, branch, diff) programmatically","Build agents that can understand code changes and repository context","Implement a reference pattern for wrapping CLI tools as MCP servers"],"best_for":["Developers building code-aware LLM agents","Teams implementing AI-assisted Git workflows","Organizations learning how to wrap CLI tools as MCP servers"],"limitations":["Subprocess-based Git calls add ~200-500ms per operation due to process spawning overhead","No built-in Git credential management — requires environment configuration","Large repositories may timeout on operations like full history retrieval","Concurrent Git operations on the same repository may cause conflicts"],"requires":["Python SDK (mcp package)","Python 3.9+","Git installed and available in PATH","Repository paths configured at server startup"],"input_types":["Repository paths","Git command parameters (branch names, commit messages, file paths)","Diff and log filters"],"output_types":["Git command output (status, log, diff)","Repository metadata (branches, commits, authors)","File history and change information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_6","uri":"capability://tool.use.integration.http.fetch.server.with.request.response.handling.and.content.type.support","name":"http fetch server with request/response handling and content type support","description":"Provides a reference MCP server that exposes HTTP fetch operations as tools, allowing LLM clients to make web requests and retrieve content. The server handles HTTP methods (GET, POST, etc.), manages request headers and authentication, parses response content based on MIME types, and implements error handling for network failures. The implementation demonstrates how to safely expose external API access to LLM clients while managing timeouts, redirects, and content encoding.","intents":["Enable LLM agents to fetch and parse web content","Allow LLM clients to call external APIs and integrate responses","Build agents that can research information from the web","Implement a reference pattern for wrapping HTTP clients as MCP servers"],"best_for":["Developers building web-aware LLM agents","Teams implementing AI-assisted research and data gathering","Organizations integrating LLMs with external APIs"],"limitations":["Network latency adds 500ms-5s per request depending on target server","No built-in request caching — repeated requests to the same URL are re-fetched","Large response bodies may exceed memory limits or timeout","No automatic retry logic for failed requests — clients must implement retry strategies"],"requires":["Python SDK (mcp package)","Python 3.9+","Network connectivity to target servers","Optional: API keys or authentication credentials for protected endpoints"],"input_types":["HTTP URLs","HTTP methods (GET, POST, etc.)","Request headers and authentication","Request body content"],"output_types":["HTTP response status codes","Response headers","Response body content (text, JSON, HTML, binary)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_7","uri":"capability://memory.knowledge.memory.server.with.persistent.key.value.storage.and.context.management","name":"memory server with persistent key-value storage and context management","description":"Provides a reference MCP server that implements persistent key-value storage accessible to LLM clients through MCP tools. The server stores and retrieves arbitrary data (strings, JSON objects) with get/set operations, supports data persistence across server restarts, and allows LLM agents to maintain state and context between conversations. The implementation demonstrates how to build stateful MCP servers that can remember information across multiple client interactions.","intents":["Enable LLM agents to store and retrieve persistent state","Build conversational agents that remember context across sessions","Implement shared memory that multiple clients can access","Create a reference pattern for stateful MCP servers"],"best_for":["Developers building multi-turn LLM agents with memory","Teams implementing conversational AI with persistent context","Organizations learning how to build stateful MCP servers"],"limitations":["In-memory storage limits scalability — large datasets may cause memory issues","No built-in access control — all clients can read/write all keys","No transaction support — concurrent writes may cause data loss","Persistence mechanism (file-based) may not be suitable for high-frequency updates"],"requires":["TypeScript SDK (@modelcontextprotocol/sdk)","Node.js 18+","Filesystem access for persistence (if file-based storage is used)"],"input_types":["Key names (strings)","Values (strings or JSON objects)","Get/set/delete operations"],"output_types":["Stored values","Operation success/failure status","Key listings"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_8","uri":"capability://planning.reasoning.sequential.thinking.server.with.step.by.step.reasoning.and.planning","name":"sequential thinking server with step-by-step reasoning and planning","description":"Provides a reference MCP server that implements structured reasoning through sequential thinking tools. The server allows LLM clients to break down complex problems into steps, track reasoning progress, and build up solutions incrementally. The implementation demonstrates how to expose reasoning primitives as MCP tools, enabling LLM agents to use explicit step-by-step planning rather than single-shot responses. This pattern supports longer reasoning chains and more transparent decision-making processes.","intents":["Enable LLM agents to break down complex problems into steps","Build agents that show their reasoning process transparently","Implement planning and task decomposition for complex workflows","Create a reference pattern for reasoning-focused MCP servers"],"best_for":["Developers building reasoning-heavy LLM agents","Teams implementing transparent AI decision-making","Organizations requiring explainable AI outputs"],"limitations":["Sequential reasoning adds latency — multiple steps take longer than single-shot responses","No automatic step optimization — agents may take inefficient paths","Step tracking requires memory — long reasoning chains may exceed context limits","No built-in validation of reasoning correctness — agents may follow invalid logic"],"requires":["TypeScript SDK (@modelcontextprotocol/sdk)","Node.js 18+","LLM client that supports multi-turn interactions"],"input_types":["Problem statements","Step descriptions and reasoning","Step results and observations"],"output_types":["Step tracking and progress","Intermediate results","Final conclusions and reasoning chains"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-servers__cap_9","uri":"capability://tool.use.integration.everything.server.with.multi.capability.demonstration.and.transport.testing","name":"everything server with multi-capability demonstration and transport testing","description":"Provides a comprehensive reference MCP server that demonstrates all MCP capabilities (Tools, Resources, Prompts, Roots) in a single implementation. The server serves as both a learning resource and a testing tool for MCP clients, implementing example tools for various operations, sample resources, prompt templates, and root directory configurations. The implementation is designed to showcase how different MCP primitives work together and to provide a testbed for validating MCP client implementations.","intents":["Learn how to implement all MCP capabilities in a single server","Test MCP client implementations against a comprehensive server","Understand how Tools, Resources, Prompts, and Roots interact","Use as a reference for building production MCP servers"],"best_for":["Developers learning MCP server development","Teams testing MCP client implementations","Organizations evaluating MCP before building production servers"],"limitations":["Example implementations may not reflect production-grade error handling","Capabilities are simplified for demonstration — real servers may need more complex logic","No performance optimizations — suitable for testing, not high-load scenarios","Limited documentation of implementation choices — requires reading source code"],"requires":["TypeScript SDK (@modelcontextprotocol/sdk)","Node.js 18+","MCP client implementation"],"input_types":["Tool invocation requests","Resource requests","Prompt requests","Root directory queries"],"output_types":["Tool results","Resource content","Prompt templates","Root directory information"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["TypeScript SDK (@modelcontextprotocol/sdk) or Python SDK (mcp package)","Node.js 18+ (TypeScript) or Python 3.9+ (Python)","MCP client implementation (Claude, custom client, or MCP client library)","MCP SDK (TypeScript or Python)","JSON Schema definitions for tool parameters","Tool implementation function matching the schema signature","TypeScript SDK: Node.js 18+, npm or yarn","Python SDK: Python 3.9+, pip","Basic understanding of async/await (TypeScript) or asyncio (Python)","Transport-specific dependencies (http module for HTTP, ws for WebSocket)"],"failure_modes":["JSON-RPC overhead adds ~50-100ms per request/response cycle compared to direct function calls","Transport layer must be configured per deployment (stdio for local, HTTP for remote)","No built-in authentication/authorization — security must be implemented at transport or application layer","Synchronous request/response model may not suit streaming or long-polling use cases","JSON Schema validation adds ~10-20ms per tool invocation","Complex nested schemas may be difficult for LLMs to reason about — simpler schemas perform better","No built-in rate limiting or quota management per tool","Tool execution errors must be handled by the server — no automatic retry logic","SDK abstractions add ~50-100ms overhead per request compared to raw protocol implementation","TypeScript SDK requires Node.js 18+ — not suitable for browser-based servers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.888872100117324,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"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":"2026-05-03T14:23:34.855Z","last_commit":"2026-04-17T22:59:54Z"},"community":{"stars":84956,"forks":10572,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-modelcontextprotocol-servers","compare_url":"https://unfragile.ai/compare?artifact=mcp-modelcontextprotocol-servers"}},"signature":"IZKX9HAuu5Mk6mHJ+5ro1AjRIjLOa+AQsfvRDSKg+m5lYwgLL+qT01GhfAGvYqUkP+crH5Xh6ZwU6Td5lFY5Cw==","signedAt":"2026-06-21T04:25:40.676Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-modelcontextprotocol-servers","artifact":"https://unfragile.ai/mcp-modelcontextprotocol-servers","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-modelcontextprotocol-servers","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"}}