{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol","slug":"mcp-modelcontextprotocol-modelcontextprotocol","name":"modelcontextprotocol","type":"mcp","url":"https://github.com/modelcontextprotocol/modelcontextprotocol","page_url":"https://unfragile.ai/mcp-modelcontextprotocol-modelcontextprotocol","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_0","uri":"capability://tool.use.integration.json.rpc.2.0.message.protocol.with.bidirectional.request.response.semantics","name":"json-rpc 2.0 message protocol with bidirectional request-response semantics","description":"Implements a standardized JSON-RPC 2.0 message system enabling bidirectional communication between MCP clients and servers through structured request/response/notification patterns. Messages are serialized as JSON with explicit method names, parameters, and correlation IDs, allowing both clients and servers to initiate requests and handle asynchronous responses. The protocol supports notifications (fire-and-forget messages) and distinguishes between successful responses and error objects with standardized error codes.","intents":["Define a language-agnostic message format that any client or server implementation can parse and generate","Enable bidirectional communication where both client and server can initiate requests without polling","Ensure message correlation and ordering across async operations","Standardize error handling and response validation across heterogeneous implementations"],"best_for":["Protocol designers building cross-language ecosystems","SDK developers implementing MCP clients or servers in any language","Teams integrating multiple AI models and tool providers"],"limitations":["JSON serialization overhead compared to binary protocols — adds ~5-15% message size","No built-in compression or streaming for large payloads — requires external wrapping","Synchronous request-response model may add latency for deeply nested async chains","No native support for multiplexing multiple concurrent requests on a single connection without explicit ID management"],"requires":["JSON parser/serializer in target language","Transport layer supporting bidirectional message delivery (stdio, HTTP, WebSocket, etc.)","Specification version 2024-11-05 or later"],"input_types":["JSON objects with method, params, id, jsonrpc fields"],"output_types":["JSON objects with result, error, or id fields"],"categories":["tool-use-integration","protocol-specification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_1","uri":"capability://tool.use.integration.transport.layer.abstraction.with.stdio.http.and.websocket.support","name":"transport layer abstraction with stdio, http, and websocket support","description":"Provides a pluggable transport abstraction that decouples the JSON-RPC message protocol from underlying communication channels. Implementations can use stdio (for local process spawning), HTTP with Server-Sent Events (for request-response patterns), or WebSocket (for persistent bidirectional connections). Each transport handles framing, connection lifecycle, and error recovery independently while maintaining identical JSON-RPC semantics at the protocol layer.","intents":["Deploy MCP servers as local CLI tools that clients spawn and communicate with via stdin/stdout","Integrate MCP into web applications using HTTP/SSE without maintaining persistent connections","Build real-time collaborative AI systems using WebSocket for low-latency bidirectional messaging","Support multiple deployment topologies (local, cloud, edge) without changing protocol implementation"],"best_for":["Developers building MCP servers that need to run in containerized or serverless environments","Web application teams integrating AI capabilities without WebSocket infrastructure","Teams deploying MCP across heterogeneous infrastructure (local dev, cloud, edge devices)"],"limitations":["Stdio transport limited to single client-server pair per process — no multiplexing","HTTP/SSE adds request-response latency compared to persistent WebSocket connections","Transport selection is static at initialization — no runtime switching between transports","Stdio transport requires explicit process lifecycle management (spawning, cleanup, signal handling)"],"requires":["Node.js 18+ for reference implementations","For stdio: ability to spawn child processes","For HTTP: HTTP server framework (Express, Fastify, etc.)","For WebSocket: WebSocket library (ws, socket.io, etc.)"],"input_types":["JSON-RPC messages as text streams (stdio), HTTP POST bodies, or WebSocket frames"],"output_types":["JSON-RPC messages as text streams (stdio), HTTP responses, or WebSocket frames"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_10","uri":"capability://automation.workflow.mcp.inspector.debugging.tool.with.message.introspection.and.replay","name":"mcp inspector debugging tool with message introspection and replay","description":"Provides a debugging tool (MCP Inspector) that intercepts and displays all JSON-RPC messages exchanged between clients and servers in real-time. The tool shows request/response pairs, message timing, error details, and payload contents. It supports message filtering, search, and export for offline analysis. The inspector can replay messages to reproduce issues and validate server behavior under specific conditions.","intents":["Debug MCP client-server communication by inspecting all exchanged messages","Diagnose protocol errors and understand why operations fail","Replay messages to reproduce issues and validate fixes","Analyze performance characteristics by examining message timing and sizes"],"best_for":["Developers building MCP clients or servers who need to debug communication issues","Teams troubleshooting integration problems between MCP components","Protocol maintainers validating implementation correctness"],"limitations":["Inspector adds overhead to message processing — not suitable for production monitoring","Message replay is manual — no automated test generation from captured messages","Inspector output is text-based — no graphical visualization of message flows","Replay functionality is limited to deterministic operations — stateful operations may fail on replay"],"requires":["Node.js 18+ for reference implementation","Access to MCP client and server processes","Ability to configure client/server to use inspector transport"],"input_types":["JSON-RPC messages from client and server"],"output_types":["Formatted message display with timing, payload, and error information"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_11","uri":"capability://tool.use.integration.sdk.tier.system.with.reference.implementations.across.multiple.languages","name":"sdk tier system with reference implementations across multiple languages","description":"Establishes a tiered SDK ecosystem with reference implementations in TypeScript (Tier 1), Python (Tier 1), and community implementations in Rust, Go, Java, and others. Tier 1 SDKs provide complete protocol support with conformance testing; Tier 2 SDKs provide core functionality with limited features; Tier 3 SDKs are community-maintained with variable quality. Each SDK tier is documented with feature matrices showing which protocol features are supported.","intents":["Provide language-native SDKs for building MCP clients and servers without manual protocol implementation","Establish quality standards for SDK implementations through tiering","Enable rapid prototyping in preferred programming languages","Support ecosystem growth by documenting community SDK contributions"],"best_for":["Developers building MCP clients or servers in TypeScript, Python, or other supported languages","Teams evaluating SDK quality and feature completeness before adoption","Language communities contributing new SDK implementations"],"limitations":["Tier 1 SDKs (TypeScript, Python) may lag behind protocol specification in new features","Community SDKs (Tier 2, Tier 3) have variable quality and maintenance status","SDK feature matrices are manually maintained — may become outdated","No automated testing across SDKs — interoperability issues may not be detected"],"requires":["Language runtime (Node.js for TypeScript, Python 3.9+ for Python, etc.)","SDK package manager (npm, pip, cargo, etc.)"],"input_types":["SDK configuration (transport, authorization, capabilities)"],"output_types":["MCP client or server instance with protocol support"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_12","uri":"capability://search.retrieval.server.registry.and.discovery.with.capability.indexing","name":"server registry and discovery with capability indexing","description":"Maintains a centralized registry of ~2,000 MCP servers with indexed capabilities (tools, resources, prompts) enabling discovery by feature. The registry includes server metadata (name, description, author), capability descriptions, and links to documentation. Servers can be discovered by searching for specific tool names, resource types, or prompt templates. The registry supports filtering by language, deployment model (local, cloud), and maintenance status.","intents":["Discover available MCP servers that provide specific tools or capabilities","Find servers that integrate with particular APIs or data sources","Evaluate server quality and maintenance status before integration","Build server recommendations based on user requirements"],"best_for":["Developers looking for MCP servers that provide specific capabilities","Teams building AI applications that need to integrate multiple tools","Organizations evaluating MCP ecosystem maturity and available options"],"limitations":["Registry is manually curated — not all available servers may be listed","Server metadata is self-reported — no automated validation of claimed capabilities","Registry does not include performance metrics or reliability data","Discovery is keyword-based — no semantic search for similar capabilities"],"requires":["Access to server registry (web interface or API)","Server metadata in standardized format"],"input_types":["Search queries (tool names, resource types, keywords)"],"output_types":["Server listings with metadata, capabilities, and documentation links"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_13","uri":"capability://automation.workflow.specification.enhancement.process.sep.with.community.governance","name":"specification enhancement process (sep) with community governance","description":"Establishes a formal process for proposing, discussing, and implementing protocol enhancements through Specification Enhancement Proposals (SEPs). SEPs are reviewed by maintainers and community members, discussed in working groups, and merged into the specification only after consensus. The process includes templates, voting procedures, and explicit criteria for acceptance. All SEPs are archived and versioned alongside protocol specifications.","intents":["Enable community input on protocol evolution while maintaining quality standards","Document rationale for protocol design decisions through SEP discussions","Ensure backward compatibility by requiring explicit discussion of breaking changes","Build consensus around new features before implementation"],"best_for":["Protocol maintainers managing community contributions and protocol evolution","Community members proposing new features or improvements","Teams evaluating protocol stability and governance maturity"],"limitations":["SEP process adds overhead to protocol evolution — simple fixes may be delayed","Consensus-based decision making can be slow — contentious proposals may stall","No explicit timeline for SEP review — acceptance time is variable","SEP voting is informal — no automated voting or quorum requirements"],"requires":["GitHub account for proposing and discussing SEPs","Participation in community discussions (Discord, GitHub issues)"],"input_types":["SEP proposal with title, motivation, design, and implementation details"],"output_types":["SEP discussion thread, review feedback, and acceptance/rejection decision"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_2","uri":"capability://tool.use.integration.capability.negotiation.and.feature.discovery.via.connection.handshake","name":"capability negotiation and feature discovery via connection handshake","description":"Implements a capability negotiation protocol during client-server connection initialization where both parties exchange supported features, protocol versions, and implementation details through initialize and initialized messages. Clients declare their sampling capabilities, prompt support, and other features; servers declare available tools, resources, prompts, and extensions. This enables graceful degradation and feature-aware behavior without requiring out-of-band configuration.","intents":["Discover what tools, resources, and prompts a server exposes without manual configuration","Negotiate protocol version compatibility and detect unsupported features before attempting operations","Enable clients to adapt behavior based on server capabilities (e.g., only use sampling if server supports it)","Support protocol evolution by allowing new features to be added without breaking existing implementations"],"best_for":["Teams building heterogeneous MCP ecosystems with multiple server implementations","Developers creating adaptive clients that adjust behavior based on available capabilities","Protocol maintainers evolving MCP with backward compatibility requirements"],"limitations":["Capability negotiation is one-time at connection initialization — no dynamic capability updates","Clients must handle servers that declare capabilities but fail to implement them correctly","No built-in versioning for individual capabilities — only protocol-level versioning","Capability discovery is synchronous and blocking — adds latency to connection establishment"],"requires":["Both client and server must implement initialize/initialized message handlers","Specification version 2024-11-05 or later","Clients must handle missing or unexpected capability fields gracefully"],"input_types":["initialize request with client capabilities (sampling, prompts, etc.)","initialized response with server capabilities (tools, resources, prompts, extensions)"],"output_types":["Structured capability objects describing supported features and their parameters"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_3","uri":"capability://tool.use.integration.tool.definition.and.invocation.with.schema.based.parameter.validation","name":"tool definition and invocation with schema-based parameter validation","description":"Defines a tool system where servers expose callable functions with JSON Schema parameter definitions and descriptions. Clients can list available tools via tools/list, inspect their schemas, and invoke them via tools/call with structured parameters. The schema validation is performed by clients before invocation and by servers during execution, ensuring type safety and clear error reporting. Tools support optional descriptions, icons, and input schemas for UI generation.","intents":["Allow AI models to discover and invoke server-provided functions with type-safe parameters","Generate UI forms or command-line interfaces from tool schemas without manual specification","Validate tool parameters before invocation to catch errors early","Support complex nested parameters and conditional schemas for advanced use cases"],"best_for":["Developers building AI agents that need to call external tools and APIs","Teams creating tool marketplaces or registries with standardized interfaces","Builders of multi-step workflows that chain tool outputs as inputs"],"limitations":["JSON Schema validation is client-side responsibility — servers must re-validate to prevent malicious input","No built-in support for streaming tool responses — responses must be complete before returning","Tool invocation is synchronous — long-running tools block the connection","No native support for tool versioning or deprecation — requires manual version management"],"requires":["JSON Schema validator in client implementation","Server must implement tools/list and tools/call message handlers","Tool names must be unique within a server instance"],"input_types":["Tool name (string) and parameters (JSON object matching schema)"],"output_types":["Tool result (JSON object or text), or error object with error code and message"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_4","uri":"capability://memory.knowledge.resource.exposure.and.content.streaming.with.uri.based.addressing","name":"resource exposure and content streaming with uri-based addressing","description":"Implements a resource system where servers expose files, documents, or data blobs via URI-based addressing (e.g., file://path/to/file, database://query/result). Clients can list available resources via resources/list, read resource content via resources/read, and subscribe to resource updates via resources/subscribe. Resources support MIME types, descriptions, and optional annotations. The protocol supports both complete content delivery and streaming for large resources.","intents":["Allow AI models to access files, databases, and other data sources without direct filesystem access","Enable real-time synchronization of resource changes (e.g., file edits, database updates) to clients","Provide a unified interface for accessing heterogeneous data sources (files, APIs, databases)","Support large file access without loading entire contents into memory"],"best_for":["Developers building AI agents that need to read and analyze documents or code","Teams creating collaborative editing systems with real-time resource synchronization","Builders of knowledge management systems integrating multiple data sources"],"limitations":["Resource URIs are server-specific — no standardized URI scheme across implementations","No built-in support for resource write operations — read-only by design","Streaming is optional and implementation-dependent — clients must handle both streaming and non-streaming responses","Resource subscriptions require persistent connections — not suitable for HTTP/SSE transports"],"requires":["Server must implement resources/list and resources/read message handlers","For subscriptions: persistent transport (WebSocket or stdio)","Clients must handle MIME type negotiation and content encoding"],"input_types":["Resource URI (string) and optional range parameters for partial reads"],"output_types":["Resource content (text or binary), MIME type, and optional metadata"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_5","uri":"capability://text.generation.language.prompt.template.system.with.dynamic.argument.substitution.and.composition","name":"prompt template system with dynamic argument substitution and composition","description":"Defines a prompt system where servers expose reusable prompt templates with named arguments, descriptions, and optional icons. Clients can list available prompts via prompts/list, retrieve prompt content via prompts/get with argument substitution, and receive prompt updates via prompts/updated notifications. Prompts support composition (referencing other prompts), conditional sections, and formatting hints for UI rendering.","intents":["Reuse common prompt patterns across multiple AI interactions without duplication","Dynamically generate prompts with context-specific arguments (user names, dates, file paths)","Discover available prompt templates and their expected arguments","Update prompts server-side and have clients automatically use the latest version"],"best_for":["Teams building prompt management systems with centralized prompt libraries","Developers creating AI agents that need to adapt prompts based on context","Organizations standardizing prompt engineering across multiple AI applications"],"limitations":["Prompt composition is server-side only — no client-side template merging","No built-in support for conditional logic in prompts — requires manual implementation","Prompt arguments are untyped strings — no schema validation for argument values","No versioning for individual prompts — only protocol-level versioning"],"requires":["Server must implement prompts/list and prompts/get message handlers","Clients must handle argument substitution and formatting"],"input_types":["Prompt name (string) and arguments (key-value map of strings)"],"output_types":["Rendered prompt text with arguments substituted"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_6","uri":"capability://text.generation.language.sampling.api.for.client.side.llm.inference.with.streaming.responses","name":"sampling api for client-side llm inference with streaming responses","description":"Enables servers to request LLM inference from clients via sampling/create messages, allowing servers to generate text, code, or structured data using the client's LLM. Servers provide a prompt (or messages array), model selection, and sampling parameters (temperature, max_tokens, stop sequences). Clients stream responses back via content blocks, supporting text, tool use, and image content types. This inverts the typical client-server relationship, enabling servers to leverage client-side LLM capabilities.","intents":["Allow MCP servers to generate text or code using the client's LLM without hosting their own model","Enable servers to perform multi-step reasoning by sampling intermediate outputs","Support tool use within server-initiated sampling (server requests tool calls from client's LLM)","Stream long-form responses from client LLM back to server without buffering"],"best_for":["Developers building MCP servers that need LLM capabilities without model hosting costs","Teams creating multi-agent systems where agents delegate reasoning to client LLMs","Builders of specialized tools that enhance LLM outputs (e.g., code formatters, summarizers)"],"limitations":["Sampling is client-initiated only — servers cannot directly invoke LLMs, only request sampling","Model selection is client-dependent — servers cannot guarantee specific model availability","Streaming responses require persistent connections — not suitable for HTTP/SSE transports","No built-in support for multi-turn conversations — each sampling request is independent"],"requires":["Client must implement sampling/create message handler","Client must have access to LLM API or local model","Server must handle streaming content blocks and stop reasons"],"input_types":["Prompt text or messages array, model name, sampling parameters (temperature, max_tokens, stop_sequences)"],"output_types":["Streamed content blocks (text, tool_use, image), stop reason, and usage statistics"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_7","uri":"capability://automation.workflow.task.system.for.asynchronous.operation.tracking.and.cancellation","name":"task system for asynchronous operation tracking and cancellation","description":"Provides a task abstraction for long-running operations where clients can create tasks via tasks/create, poll progress via tasks/progress, and cancel via tasks/cancel. Tasks have unique IDs, progress tracking (percentage, status messages), and result delivery. The system supports both polling and event-based progress updates, enabling clients to monitor server-side operations without blocking. Tasks can be nested and support explicit cancellation with cleanup semantics.","intents":["Track long-running server operations (file processing, model training, batch analysis) without blocking","Provide progress feedback to users during extended operations","Cancel in-flight operations and ensure proper resource cleanup","Support nested task hierarchies for complex multi-step workflows"],"best_for":["Developers building MCP servers that perform long-running operations","Teams creating interactive AI applications with progress feedback","Builders of batch processing systems that need operation tracking"],"limitations":["Task system is optional and not all servers implement it — clients must handle missing task support","Progress updates are server-dependent — no standardized progress format","Polling-based progress tracking adds latency compared to event-based updates","No built-in support for task persistence — tasks are lost on server restart"],"requires":["Server must implement tasks/create, tasks/progress, and tasks/cancel message handlers (optional)","Clients must handle task IDs and poll for progress","Task IDs must be unique within server instance"],"input_types":["Task creation parameters (operation type, arguments), task ID for progress/cancel requests"],"output_types":["Task ID, progress percentage, status messages, and final result"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_8","uri":"capability://safety.moderation.oauth.2.1.authorization.framework.with.token.management.and.validation","name":"oauth 2.1 authorization framework with token management and validation","description":"Implements OAuth 2.1 authorization for MCP clients connecting to servers, supporting multiple client registration methods (static credentials, dynamic registration, PKCE flow). Clients obtain access tokens via authorization endpoints, include tokens in connection headers, and refresh tokens when expired. Servers validate tokens against issuer endpoints and enforce scope-based access control. The framework supports both bearer tokens and custom authorization schemes.","intents":["Secure MCP connections between clients and servers with standardized OAuth 2.1 flows","Manage client credentials and token lifecycle without hardcoding secrets","Enforce scope-based access control for sensitive operations","Support dynamic client registration for zero-configuration deployments"],"best_for":["Teams deploying MCP in production environments requiring authentication","Developers building multi-tenant MCP systems with per-client access control","Organizations integrating MCP with existing OAuth 2.1 identity providers"],"limitations":["OAuth 2.1 support is optional — not all servers implement authorization","Token validation requires network calls to issuer endpoints — adds latency to connection establishment","No built-in support for token caching — clients must implement caching to avoid repeated validation","Scope definitions are server-specific — no standardized scope names across implementations"],"requires":["OAuth 2.1 authorization server with token endpoint","Client must support OAuth 2.1 flows (authorization code, PKCE, client credentials)","Server must validate tokens against issuer endpoint"],"input_types":["Client ID, client secret (or PKCE code verifier), authorization code"],"output_types":["Access token, refresh token, token expiration time"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-modelcontextprotocol-modelcontextprotocol__cap_9","uri":"capability://automation.workflow.specification.versioning.and.conformance.testing.framework","name":"specification versioning and conformance testing framework","description":"Maintains versioned protocol specifications (2024-11-05, 2025-03-26, 2025-06-18, 2025-11-25) with explicit changelog documentation and conformance test suites. Each version is published as a JSON Schema definition (schema.json) and TypeScript type definitions (schema.ts) that serve as the single source of truth. Conformance tests validate that implementations correctly handle all protocol features, error cases, and edge cases. The framework supports semantic versioning with breaking changes documented explicitly.","intents":["Ensure all MCP implementations conform to the same specification and interoperate correctly","Track protocol evolution and breaking changes across versions","Validate new implementations against reference test suites","Generate documentation and type definitions automatically from schema definitions"],"best_for":["SDK developers implementing MCP clients or servers in new languages","Protocol maintainers evolving MCP with backward compatibility requirements","Teams validating MCP implementations before production deployment"],"limitations":["Conformance tests are optional — not all implementations run them","Schema definitions are TypeScript-first — other languages require manual translation","Breaking changes require major version bumps — no minor version compatibility guarantees","Conformance tests cover protocol features but not performance characteristics"],"requires":["JSON Schema validator for schema validation","TypeScript compiler for type checking (optional)","Test runner for conformance test execution"],"input_types":["JSON-RPC messages, protocol version strings"],"output_types":["Conformance test results, schema validation errors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["JSON parser/serializer in target language","Transport layer supporting bidirectional message delivery (stdio, HTTP, WebSocket, etc.)","Specification version 2024-11-05 or later","Node.js 18+ for reference implementations","For stdio: ability to spawn child processes","For HTTP: HTTP server framework (Express, Fastify, etc.)","For WebSocket: WebSocket library (ws, socket.io, etc.)","Node.js 18+ for reference implementation","Access to MCP client and server processes","Ability to configure client/server to use inspector transport"],"failure_modes":["JSON serialization overhead compared to binary protocols — adds ~5-15% message size","No built-in compression or streaming for large payloads — requires external wrapping","Synchronous request-response model may add latency for deeply nested async chains","No native support for multiplexing multiple concurrent requests on a single connection without explicit ID management","Stdio transport limited to single client-server pair per process — no multiplexing","HTTP/SSE adds request-response latency compared to persistent WebSocket connections","Transport selection is static at initialization — no runtime switching between transports","Stdio transport requires explicit process lifecycle management (spawning, cleanup, signal handling)","Inspector adds overhead to message processing — not suitable for production monitoring","Message replay is manual — no automated test generation from captured messages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6578292530068082,"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":"2026-05-03T14:23:38.364Z","last_commit":"2026-04-30T09:49:54Z"},"community":{"stars":7990,"forks":1484,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-modelcontextprotocol-modelcontextprotocol","compare_url":"https://unfragile.ai/compare?artifact=mcp-modelcontextprotocol-modelcontextprotocol"}},"signature":"lnz0k4VUFomAec1Da/Gmw8q0yddR1DM1nniRmftAf4yUJKsgHUr6iIK9IOnmS2mt6XLQlKNpHQTNLDW73htMDg==","signedAt":"2026-06-20T14:47:46.692Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-modelcontextprotocol-modelcontextprotocol","artifact":"https://unfragile.ai/mcp-modelcontextprotocol-modelcontextprotocol","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-modelcontextprotocol-modelcontextprotocol","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"}}