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The server handles bidirectional communication via stdio, SSE, or WebSocket transports, managing request/response routing, error handling, and protocol versioning according to the MCP specification.","intents":["I need to expose my tools and data sources to Claude or other MCP-compatible AI clients","I want to build a standardized integration layer that works with multiple AI applications without rewriting adapters","I need to implement the MCP server-side protocol to handle tool discovery and invocation from AI agents"],"best_for":["Teams building integrations between proprietary tools and Claude/LLM applications","Developers creating reusable tool ecosystems that need to work across multiple AI platforms","Organizations standardizing on MCP for AI agent infrastructure"],"limitations":["Requires client-side MCP support — not compatible with non-MCP AI platforms","Transport layer selection (stdio vs SSE vs WebSocket) must be chosen at deployment time","No built-in authentication — security must be implemented at the transport or application layer"],"requires":["MCP client implementation (Claude, custom LLM application, or MCP-compatible tool)","Node.js runtime or equivalent JavaScript environment","Understanding of JSON-RPC 2.0 protocol semantics"],"input_types":["JSON-RPC 2.0 requests","Tool invocation payloads with typed arguments","Resource URIs and prompt templates"],"output_types":["JSON-RPC 2.0 responses","Tool execution results with structured data","Resource content and prompt completions"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_1","uri":"capability://tool.use.integration.tool.definition.and.schema.based.invocation.registry","name":"tool definition and schema-based invocation registry","description":"Manages a registry of callable tools with JSON Schema definitions for argument validation and type coercion. Tools are declared with input schemas, output descriptions, and execution handlers; the server validates incoming invocation requests against schemas before dispatching to handler functions, ensuring type safety and providing schema introspection to clients for dynamic UI generation.","intents":["I want to expose a set of functions to Claude with strict type validation and clear documentation","I need Claude to understand the exact parameters and return types of my tools without trial-and-error","I want to automatically generate tool documentation and UI hints from my tool definitions"],"best_for":["Developers building agent-accessible APIs with strict input validation requirements","Teams needing self-documenting tool interfaces that work across multiple AI clients","Applications requiring type-safe tool invocation with automatic schema validation"],"limitations":["Schema validation adds latency (~5-50ms per invocation depending on schema complexity)","Complex nested schemas may be difficult for LLMs to reason about — flattening often required","No built-in versioning for tool schemas — breaking changes require client coordination"],"requires":["JSON Schema draft 7 or later knowledge","Tool handler functions that match declared signatures","MCP client capable of parsing and respecting tool schemas"],"input_types":["JSON Schema object definitions","Tool handler function signatures","Invocation payloads matching declared schemas"],"output_types":["Validated tool invocation results","Schema introspection responses","Type-coerced argument objects"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_2","uri":"capability://memory.knowledge.resource.serving.and.uri.based.content.retrieval","name":"resource serving and uri-based content retrieval","description":"Implements the MCP resources capability, allowing servers to expose static or dynamic content (files, database records, API responses) via URI-based addressing. Clients request resources by URI, the server resolves the URI to a handler, executes any necessary retrieval logic, and returns content with MIME type metadata. Supports resource listing with filtering and pagination for discovery.","intents":["I want Claude to access specific files, documents, or data records from my system without uploading them","I need to provide Claude with read-only access to a knowledge base or document repository","I want to expose dynamic content (e.g., database queries, API responses) as resources that Claude can reference"],"best_for":["Applications providing Claude with access to proprietary documents or knowledge bases","Systems needing to expose file systems or databases to AI agents with fine-grained access control","Teams building RAG-like systems where Claude retrieves content on-demand via MCP resources"],"limitations":["Resource content is fetched synchronously — large files or slow backends may timeout","No built-in caching — repeated resource requests incur full retrieval cost each time","URI scheme and resolution logic must be custom-implemented per application"],"requires":["URI scheme design and handler implementation","Content retrieval backend (file system, database, API, etc.)","MIME type detection for returned content"],"input_types":["Resource URIs (custom scheme, e.g., 'file://path', 'db://table/id')","Resource listing queries with optional filters"],"output_types":["Resource content (text, binary, structured data)","Resource metadata (MIME type, size, modification time)","Resource lists with pagination tokens"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_3","uri":"capability://text.generation.language.prompt.template.management.and.completion","name":"prompt template management and completion","description":"Manages reusable prompt templates that clients can invoke with variable substitution. 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Enables centralized prompt versioning and A/B testing without client-side template management.","intents":["I want to manage prompt templates server-side so I can update them without redeploying clients","I need Claude to use standardized prompts for specific tasks (e.g., code review, summarization) with consistent formatting","I want to version and test different prompt variations without modifying client code"],"best_for":["Teams managing multiple AI agents with shared prompt libraries","Applications requiring prompt versioning and A/B testing capabilities","Organizations centralizing prompt engineering and governance"],"limitations":["Template rendering is server-side only — clients cannot customize prompts locally","Complex conditional logic in templates requires custom template language implementation","No built-in audit trail for prompt changes — requires external logging"],"requires":["Template storage backend (file system, database, or in-memory)","Template rendering engine (Handlebars, Jinja2, or custom)","Variable substitution logic matching template syntax"],"input_types":["Template names (string identifiers)","Variable bindings (key-value objects)","Template definitions with placeholder syntax"],"output_types":["Rendered prompt strings","Template metadata (version, description, arguments)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_4","uri":"capability://planning.reasoning.bidirectional.request.handling.with.client.initiated.sampling","name":"bidirectional request handling with client-initiated sampling","description":"Implements MCP's sampling capability, allowing the server to request the client (AI application) to perform LLM sampling (model inference) and return results. The server sends a sampling request with a prompt and parameters, the client executes the LLM call, and returns the completion. Enables server-side agents to delegate reasoning tasks to the client's model without maintaining separate model connections.","intents":["I want my MCP server to ask Claude to reason about or analyze data and return the result","I need my server-side logic to leverage the client's LLM without running my own model","I want to build server-side agents that can request model inference from the connected AI application"],"best_for":["Server-side agents that need to delegate reasoning to the client's LLM","Applications building complex multi-step workflows with model inference at intermediate steps","Teams avoiding the cost and complexity of running separate LLM instances on the server"],"limitations":["Sampling requests are synchronous — server blocks until client returns completion","Client must support the sampling capability — not all MCP clients implement it","No control over model selection or parameters — client determines LLM and settings","Adds round-trip latency for each sampling request (typically 1-10 seconds)"],"requires":["MCP client with sampling capability support","Well-formed prompt and sampling parameters (temperature, max_tokens, etc.)","Understanding of LLM output formats and error handling"],"input_types":["Prompt text or messages array","Sampling parameters (temperature, top_p, max_tokens, stop sequences)","Model hints (optional client-side model selection)"],"output_types":["LLM completion text","Stop reason (end_turn, max_tokens, stop_sequence)","Token usage metadata"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_5","uri":"capability://tool.use.integration.transport.abstraction.with.stdio.sse.and.websocket.support","name":"transport abstraction with stdio, sse, and websocket support","description":"Provides pluggable transport layer supporting stdio (for local CLI integration), Server-Sent Events (for HTTP long-polling), and WebSocket (for persistent bidirectional connections). The transport layer handles message framing, connection lifecycle, and error recovery; the core MCP protocol logic is transport-agnostic. Enables deployment flexibility without changing server code.","intents":["I want to run this MCP server locally via stdio for CLI tool integration","I need to expose this MCP server over HTTP for web-based clients","I want to deploy this server with WebSocket for low-latency bidirectional communication"],"best_for":["Teams needing flexible deployment options (local, HTTP, WebSocket) from a single codebase","Applications integrating MCP servers into existing HTTP infrastructure","Developers building CLI tools that need MCP server capabilities"],"limitations":["Stdio transport limited to local machine — no remote access without tunneling","SSE transport is HTTP-only, unidirectional — requires polling for server-to-client messages","WebSocket transport requires persistent connection — may not work behind certain proxies","Transport selection is deployment-time decision — cannot switch at runtime"],"requires":["Node.js runtime with appropriate transport support (stdio built-in, SSE/WebSocket via libraries)","HTTP server for SSE/WebSocket transports","Client support for chosen transport (stdio requires local execution, SSE/WebSocket require HTTP client)"],"input_types":["JSON-RPC 2.0 messages","Transport-specific framing (CRLF for stdio, HTTP headers for SSE, WebSocket frames)"],"output_types":["JSON-RPC 2.0 responses","Transport-specific framing and error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_0n3x-tech-cpcmcp__cap_6","uri":"capability://safety.moderation.error.handling.and.protocol.compliant.error.responses","name":"error handling and protocol-compliant error responses","description":"Implements JSON-RPC 2.0 error response handling, mapping application errors to protocol-compliant error objects with error codes, messages, and optional data. Distinguishes between protocol errors (invalid requests), server errors (handler exceptions), and client errors (invalid arguments), returning appropriate HTTP status codes and error structures. Enables clients to programmatically handle different error categories.","intents":["I want my MCP server to return structured errors that clients can parse and handle programmatically","I need to distinguish between client errors (bad input) and server errors (handler failure) in my responses","I want to provide detailed error information without exposing sensitive implementation details"],"best_for":["Teams building robust MCP integrations with comprehensive error handling","Applications requiring detailed error diagnostics for debugging and monitoring","Systems needing to distinguish error categories for different recovery strategies"],"limitations":["Error details must be carefully curated to avoid information leakage","Stack traces and internal errors should not be exposed to clients — requires filtering","Custom error codes must be documented for client-side handling"],"requires":["Understanding of JSON-RPC 2.0 error specification","Error categorization logic (protocol vs. application vs. validation errors)","Logging and monitoring for error tracking"],"input_types":["Exception objects from handler execution","Validation errors from schema checking","Protocol-level errors (malformed requests, missing fields)"],"output_types":["JSON-RPC 2.0 error responses with code, message, and data","HTTP status codes (400 for client errors, 500 for server errors)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude, custom LLM application, or MCP-compatible tool)","Node.js runtime or equivalent JavaScript environment","Understanding of JSON-RPC 2.0 protocol semantics","JSON Schema draft 7 or later knowledge","Tool handler functions that match declared signatures","MCP client capable of parsing and respecting tool schemas","URI scheme design and handler implementation","Content retrieval backend (file system, database, API, etc.)","MIME type detection for returned content","Template storage backend (file system, database, or in-memory)"],"failure_modes":["Requires client-side MCP support — not compatible with non-MCP AI platforms","Transport layer selection (stdio vs SSE vs WebSocket) must be chosen at deployment time","No built-in authentication — security must be implemented at the transport or application layer","Schema validation adds latency (~5-50ms per invocation depending on schema complexity)","Complex nested schemas may be difficult for LLMs to reason about — flattening often required","No built-in versioning for tool schemas — breaking changes require client coordination","Resource content is fetched synchronously — large files or slow backends may timeout","No built-in caching — repeated resource requests incur full retrieval cost each time","URI scheme and resolution logic must be custom-implemented per application","Template rendering is server-side only — clients cannot customize prompts locally","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:19:51.141Z","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=0n3x-tech-cpcmcp","compare_url":"https://unfragile.ai/compare?artifact=0n3x-tech-cpcmcp"}},"signature":"7O9mz9kLvZnT2jYo9kLpiRh2hv6HDuyB0e8xp6vd/glBu5YqrwW961FX4QOVhLkSzjZXaXmjbb/mBRJ0aEq4Ag==","signedAt":"2026-06-20T18:49:08.860Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/0n3x-tech-cpcmcp","artifact":"https://unfragile.ai/0n3x-tech-cpcmcp","verify":"https://unfragile.ai/api/v1/verify?slug=0n3x-tech-cpcmcp","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"}}