{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-modelcontextprotocol-io","slug":"modelcontextprotocol-io","name":"modelcontextprotocol.io","type":"mcp","url":"https://modelcontextprotocol.io/introduction","page_url":"https://unfragile.ai/modelcontextprotocol-io","categories":["mcp-servers"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-modelcontextprotocol-io__cap_0","uri":"capability://tool.use.integration.standardized.protocol.for.ai.tool.integration","name":"standardized-protocol-for-ai-tool-integration","description":"MCP defines a bidirectional protocol standard that allows AI applications (clients) to discover, invoke, and interact with external tools and data sources (servers) through a USB-C-like standardized interface. The protocol abstracts away implementation details of individual tools, enabling a single client to work with heterogeneous tool ecosystems without custom integration code for each tool. Servers expose capabilities via a registry that clients query to understand available operations, parameters, and schemas.","intents":["Connect Claude, ChatGPT, or other AI applications to custom tools without writing provider-specific integration code","Build a single MCP server that multiple AI clients can discover and use interchangeably","Create a standardized way for AI agents to access databases, APIs, and local services","Enable tool discovery and capability negotiation between AI systems and external services"],"best_for":["AI application developers building multi-tool agent systems","Enterprise teams standardizing AI integrations across multiple LLM providers","Open-source tool maintainers wanting to expose their tools to AI applications","Teams migrating from custom REST/webhook integrations to a standardized protocol"],"limitations":["Protocol specification only — no built-in authentication/authorization model documented; security implementation delegated to individual servers","No standardized rate limiting or quota system defined at protocol level; each server implements its own constraints","Requires both client and server to implement MCP; legacy tools need wrapper servers","Transport mechanism (HTTP, stdio, WebSocket) not specified in introduction; implementation-dependent","No built-in persistence, caching, or state management — stateless request/response model"],"requires":["MCP-compatible client (Claude, ChatGPT, VS Code, Cursor, or custom implementation)","MCP server implementation (open-source or custom-built)","Network connectivity between client and server (local or remote)","Understanding of tool/resource schema definition (JSON Schema format inferred)"],"input_types":["tool invocation requests with parameters","resource queries and filters","capability discovery requests"],"output_types":["tool execution results","resource data (structured or unstructured)","capability metadata and schemas"],"categories":["tool-use-integration","protocol-standard"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_1","uri":"capability://tool.use.integration.bidirectional.tool.invocation.framework","name":"bidirectional-tool-invocation-framework","description":"MCP enables AI applications to both read data from external systems (passive access) and perform actions/mutations (active tool use) through a unified protocol. Servers expose tools as callable operations with defined input schemas and return types; clients invoke these tools with parameters and receive structured results. The framework handles parameter validation, error propagation, and result serialization without requiring the AI application to understand the underlying tool implementation.","intents":["Allow Claude or other AI to search databases, query APIs, or read files without embedding API credentials in prompts","Enable AI agents to perform write operations (create calendar events, update databases, modify files) through standardized tool interfaces","Build AI workflows that chain multiple tool invocations with conditional logic based on results","Expose internal company tools to AI applications in a secure, schema-validated manner"],"best_for":["AI agent developers building multi-step workflows with external tool dependencies","Enterprise teams exposing internal APIs and databases to AI applications","Tool developers wanting to make their tools AI-accessible without custom integrations","Teams building AI-powered automation that requires both read and write access to systems"],"limitations":["Tool invocation is synchronous request/response only — no streaming or async job tracking documented","No built-in transaction support or rollback mechanisms for multi-step tool chains","Error handling model not specified in introduction; individual servers define error semantics","No standardized timeout, retry, or circuit-breaker patterns at protocol level","Parameter validation delegated to individual tool implementations; no centralized schema enforcement"],"requires":["MCP server with tool definitions (schemas, input/output types)","MCP client capable of parsing tool schemas and invoking tools","Tool implementation with defined input parameters and return types","Network connectivity between client and server"],"input_types":["tool name (string identifier)","tool parameters (JSON object matching schema)","optional context or metadata"],"output_types":["tool result (structured data, text, or binary)","error messages with codes","execution metadata (latency, resource usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_2","uri":"capability://memory.knowledge.resource.data.source.abstraction","name":"resource-data-source-abstraction","description":"MCP abstracts external data sources (databases, file systems, APIs, services like Google Calendar or Notion) as 'resources' that AI applications can query and access. Servers define resources with URIs, metadata, and access patterns; clients can discover available resources, read their contents, and in some cases modify them. The abstraction decouples the AI application from knowing how to authenticate, query, or parse each individual data source — the server handles all integration logic.","intents":["Give Claude or other AI access to company databases, file systems, or knowledge bases without embedding credentials in prompts","Enable AI to search and retrieve information from external services (Google Calendar, Notion, Figma) through a unified interface","Build AI applications that can access multiple heterogeneous data sources without custom parsing logic","Expose internal data resources to AI with fine-grained access control and audit logging"],"best_for":["Enterprise teams centralizing AI access to internal data sources","AI application developers building knowledge-aware agents","Teams integrating AI with SaaS platforms (Notion, Google Workspace, Figma, etc.)","Organizations needing to control which data resources AI applications can access"],"limitations":["Resource discovery mechanism not documented in introduction; unclear how clients enumerate available resources","No standardized pagination, filtering, or query language defined — each resource type implements its own access patterns","Authentication/authorization model not specified; security delegated to individual servers","No built-in caching or change notification system; clients must poll for updates","Resource modification (write) capabilities not detailed; unclear which resources support mutations"],"requires":["MCP server with resource definitions (URIs, metadata, access patterns)","MCP client capable of querying and reading resources","Network connectivity to data sources (direct or through server proxy)","Appropriate credentials/permissions for accessing resources (managed by server)"],"input_types":["resource URI (string identifier)","query parameters or filters (optional)","read/write operation type"],"output_types":["resource content (text, structured data, binary)","resource metadata (size, type, modification time)","error messages if resource not found or access denied"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_3","uri":"capability://tool.use.integration.capability.discovery.and.schema.negotiation","name":"capability-discovery-and-schema-negotiation","description":"MCP clients can query servers to discover what tools and resources are available, along with their input/output schemas, descriptions, and constraints. Servers expose a capability registry that clients use to understand what operations are possible before invoking them. This enables dynamic tool selection, parameter validation, and graceful degradation when tools are unavailable — the AI application can adapt its behavior based on what the server actually exposes.","intents":["Dynamically discover what tools a server exposes without hardcoding tool names in the AI application","Validate tool parameters against schemas before invoking to catch errors early","Build AI agents that adapt their behavior based on available tools and resources","Enable servers to add/remove tools without requiring client code changes"],"best_for":["AI agent developers building flexible, tool-agnostic workflows","Teams deploying MCP servers that evolve over time (adding/removing tools)","Enterprise environments where tool availability varies by user/context","Tool developers wanting to expose capabilities with rich metadata"],"limitations":["Schema format not specified in introduction; unclear if JSON Schema, OpenAPI, or custom format is used","No versioning mechanism documented for tool schemas; unclear how breaking changes are handled","Discovery is synchronous only — no subscription model for capability changes","No standardized way to express tool dependencies or prerequisites","Capability metadata (descriptions, examples, tags) format not documented"],"requires":["MCP server with capability registry implementation","MCP client capable of parsing and interpreting capability schemas","Network connectivity to query server capabilities","Schema parsing library (format-dependent)"],"input_types":["capability discovery request (no parameters or optional filters)","optional capability type filter (tools vs. resources)"],"output_types":["list of available tools with schemas","list of available resources with metadata","tool/resource descriptions and usage examples","input/output type definitions"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_4","uri":"capability://tool.use.integration.multi.client.ecosystem.support","name":"multi-client-ecosystem-support","description":"MCP is designed as a protocol standard that multiple AI clients (Claude, ChatGPT, VS Code, Cursor, custom applications) can implement and use interchangeably. A single MCP server can serve multiple different clients without modification; clients can connect to multiple servers and aggregate their capabilities. This enables an ecosystem where tools and data sources are decoupled from specific AI applications, creating network effects as more clients and servers adopt the standard.","intents":["Build an MCP server once and make it available to Claude, ChatGPT, VS Code, and other AI applications","Use the same tools and data sources across multiple AI applications without reimplementation","Create a marketplace or registry of MCP servers that any compatible client can discover and use","Standardize tool integrations across an organization regardless of which AI application is used"],"best_for":["Tool and service developers wanting to reach multiple AI application ecosystems","Enterprise teams standardizing on MCP across different AI platforms","Open-source communities building shared tool ecosystems","Organizations evaluating multiple AI providers and wanting tool portability"],"limitations":["Requires adoption by multiple clients and servers to create network effects; ecosystem still emerging","No standardized server registry or discovery mechanism documented; unclear how clients find servers","Compatibility across client implementations not guaranteed; each client may support different MCP versions","No standardized authentication/authorization between clients and servers; each implementation handles security differently","Tool behavior may vary slightly across clients due to different prompt engineering or execution models"],"requires":["MCP-compatible client implementation (Claude, ChatGPT, VS Code, Cursor, or custom)","MCP server implementation","Network connectivity between client and server","Shared understanding of MCP protocol version and capabilities"],"input_types":["server connection details (address, credentials)","client capability declarations"],"output_types":["server capability list","tool/resource access across multiple clients"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_5","uri":"capability://tool.use.integration.local.and.remote.server.connectivity","name":"local-and-remote-server-connectivity","description":"MCP supports both local server connections (running on the same machine as the client, e.g., stdio-based communication) and remote server connections (over network protocols). This enables flexible deployment patterns: developers can run MCP servers locally for development/testing, while production deployments can use remote servers with proper authentication and scaling. The protocol abstracts away transport details, allowing the same server implementation to work in both scenarios.","intents":["Run MCP servers locally during development without network overhead or authentication complexity","Deploy MCP servers remotely with proper security, scaling, and monitoring for production use","Connect AI applications to both local tools (development databases, file systems) and remote services (production APIs)","Enable hybrid deployments where some tools run locally and others run remotely"],"best_for":["Development teams building and testing MCP servers locally","Enterprise deployments requiring remote, scalable MCP server infrastructure","Teams with hybrid local/remote tool ecosystems","Organizations needing to isolate tool execution (local vs. remote) for security/compliance"],"limitations":["Transport mechanism (HTTP, WebSocket, gRPC, stdio) not specified in introduction; implementation-dependent","No standardized authentication/TLS configuration documented for remote connections","Latency differences between local and remote servers not addressed; no optimization guidance","No built-in load balancing or failover for remote servers","Local server security model not documented; unclear how local servers are protected from unauthorized access"],"requires":["MCP server implementation (local or remote)","MCP client capable of connecting to local/remote servers","For local: same machine or local network access","For remote: network connectivity, authentication credentials, TLS/encryption (if required)"],"input_types":["server connection configuration (address, port, credentials)","transport protocol specification"],"output_types":["successful connection confirmation","server capabilities and tools","tool invocation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_6","uri":"capability://tool.use.integration.open.source.server.ecosystem","name":"open-source-server-ecosystem","description":"MCP is positioned as an open-source protocol with example servers and SDKs available for building custom servers. The documentation references 'Example Servers' and 'Example Clients' (not included in provided content) that developers can use as templates. This enables a community-driven ecosystem where developers can build and share MCP servers for various tools and services, similar to how open-source package managers create network effects.","intents":["Build custom MCP servers for internal tools and services using provided SDKs and examples","Contribute MCP servers to the open-source community for others to use","Learn MCP implementation patterns by studying example servers","Extend existing MCP servers with custom functionality"],"best_for":["Open-source developers contributing to the MCP ecosystem","Enterprise teams building custom MCP servers for internal tools","Tool maintainers wanting to expose their tools via MCP","Developers learning MCP implementation patterns"],"limitations":["Example servers and SDKs not included in provided documentation; unclear what languages/frameworks are supported","No standardized testing framework or validation tools documented for MCP servers","Community governance model not documented; unclear how servers are vetted/maintained","No standardized server registry or package manager for discovering/installing MCP servers","Documentation for building servers referenced but not provided in introduction"],"requires":["MCP SDK for desired programming language (availability unknown)","Understanding of MCP protocol specification (not included in introduction)","Development environment for chosen language","Access to example servers and documentation"],"input_types":["tool/service API specifications","authentication credentials for services","resource/tool definitions"],"output_types":["MCP server implementation","tool/resource schemas","server deployment artifacts"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-modelcontextprotocol-io__cap_7","uri":"capability://planning.reasoning.ai.agent.skill.composition","name":"ai-agent-skill-composition","description":"MCP enables building AI agents by composing multiple tools and resources as 'skills' that the agent can invoke. The protocol provides the infrastructure for agents to discover available skills, reason about which skills to use for a given task, invoke them with appropriate parameters, and chain results across multiple skill invocations. This enables complex multi-step workflows where agents can autonomously decide which tools to use and in what order.","intents":["Build AI agents that can autonomously choose and invoke tools based on task requirements","Create multi-step workflows where agents chain tool invocations and adapt based on results","Enable agents to reason about tool availability and gracefully handle missing capabilities","Compose complex agent behaviors from simpler, reusable tool skills"],"best_for":["AI agent developers building autonomous workflow systems","Teams creating AI-powered automation that requires multi-step reasoning","Organizations building AI assistants with access to multiple tools","Developers implementing chain-of-thought or planning-based agents"],"limitations":["Agent reasoning patterns not documented in introduction; unclear how agents decide which tools to use","No standardized tool chaining or workflow orchestration model defined","Error recovery and fallback strategies not documented","No built-in memory or state management for multi-step workflows","Tool invocation ordering and dependency management delegated to agent implementation"],"requires":["MCP server with tool definitions","MCP client with agent reasoning capabilities","LLM or reasoning engine for tool selection and parameter generation","Tool schema understanding for parameter validation"],"input_types":["user task or goal (natural language)","available tools and resources","previous tool results (for chaining)"],"output_types":["tool invocation sequence","final agent result","reasoning trace (optional)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude, ChatGPT, VS Code, Cursor, or custom implementation)","MCP server implementation (open-source or custom-built)","Network connectivity between client and server (local or remote)","Understanding of tool/resource schema definition (JSON Schema format inferred)","MCP server with tool definitions (schemas, input/output types)","MCP client capable of parsing tool schemas and invoking tools","Tool implementation with defined input parameters and return types","Network connectivity between client and server","MCP server with resource definitions (URIs, metadata, access patterns)","MCP client capable of querying and reading resources"],"failure_modes":["Protocol specification only — no built-in authentication/authorization model documented; security implementation delegated to individual servers","No standardized rate limiting or quota system defined at protocol level; each server implements its own constraints","Requires both client and server to implement MCP; legacy tools need wrapper servers","Transport mechanism (HTTP, stdio, WebSocket) not specified in introduction; implementation-dependent","No built-in persistence, caching, or state management — stateless request/response model","Tool invocation is synchronous request/response only — no streaming or async job tracking documented","No built-in transaction support or rollback mechanisms for multi-step tool chains","Error handling model not specified in introduction; individual servers define error semantics","No standardized timeout, retry, or circuit-breaker patterns at protocol level","Parameter validation delegated to individual tool implementations; no centralized schema enforcement","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:18.053Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=modelcontextprotocol-io","compare_url":"https://unfragile.ai/compare?artifact=modelcontextprotocol-io"}},"signature":"YariHDIa/YJofLsA7k1MpsH5KkA3edAQt9w42e9zTRUSKU+UEQuMGENAKryhtzg1Mb7/UZSMNEiU8EGsloyWDg==","signedAt":"2026-06-19T20:09:52.505Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/modelcontextprotocol-io","artifact":"https://unfragile.ai/modelcontextprotocol-io","verify":"https://unfragile.ai/api/v1/verify?slug=modelcontextprotocol-io","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"}}