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Uses JSON-RPC 2.0 transport layer with request/response semantics for tool registration, resource exposure, and prompt templating. Handles bidirectional communication patterns where the server can both respond to client requests and initiate server-to-client notifications.","intents":["I need to expose my custom tools and data sources to Claude or other MCP-compatible AI models","I want to build a standardized integration layer that works across multiple AI applications without reimplementing for each one","I need to enable AI models to access my internal APIs and databases through a secure, schema-validated protocol"],"best_for":["Teams building internal tool ecosystems that need to integrate with multiple AI models","Developers creating reusable integrations that should work across Claude, other LLMs, and AI agents","Organizations standardizing on MCP for consistent AI-powered automation across departments"],"limitations":["MCP is still evolving — breaking changes possible in minor versions before 1.0 stabilization","No built-in authentication/authorization — relies on transport layer (TLS, API keys) for security","Synchronous request/response model may not suit real-time streaming use cases without additional buffering","Limited to JSON serialization — binary data requires base64 encoding, increasing payload size"],"requires":["MCP client library compatible with the server implementation language","Network connectivity between MCP client and server (local socket, HTTP, or stdio transport)","Understanding of JSON-RPC 2.0 protocol and MCP specification (v0.x or later)"],"input_types":["JSON-RPC requests with method names and parameters","Tool invocation payloads with typed arguments","Resource URIs and prompt template variables"],"output_types":["JSON-RPC responses with results or errors","Tool execution results (structured JSON or text)","Resource content (text, code, structured data)","Prompt template outputs"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_1","uri":"capability://tool.use.integration.tool.definition.and.schema.based.function.calling","name":"tool definition and schema-based function calling","description":"Exposes custom tools through MCP's tool registry with JSON Schema definitions for input validation and type safety. Each tool includes a name, description, input schema (with required/optional parameters), and handler implementation. The server validates incoming tool calls against the schema before execution, ensuring type correctness and preventing malformed invocations. Supports nested object schemas, arrays, and enum constraints for rich parameter validation.","intents":["I want to expose my Python/Node functions as callable tools that Claude can invoke with type-safe parameters","I need to define complex tool schemas with nested objects and validation rules so AI models understand exactly what parameters are required","I want to ensure tool invocations fail gracefully with clear error messages when parameters don't match the schema"],"best_for":["Developers building AI agents that need to call custom business logic or APIs","Teams creating domain-specific tool libraries (e.g., database queries, file operations, external API calls)","Organizations standardizing tool definitions across multiple AI applications"],"limitations":["Schema validation is JSON Schema only — no support for custom validation logic beyond type constraints","Tool execution is synchronous — long-running tools block the server until completion","No built-in retry logic or timeout handling — must be implemented in tool handlers","Schema complexity is limited by JSON Schema expressiveness — some domain-specific constraints require custom validation code"],"requires":["JSON Schema knowledge for defining tool input schemas","Handler implementation in the server's native language (Python, Node.js, etc.)","MCP client that supports tool calling (e.g., Claude with tools_use capability)"],"input_types":["Tool invocation requests with method name and parameters","JSON-serialized parameter values matching the tool's input schema"],"output_types":["Tool execution results (JSON, text, or structured data)","Error responses with validation or execution failure details"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_2","uri":"capability://memory.knowledge.resource.exposure.and.uri.based.content.serving","name":"resource exposure and uri-based content serving","description":"Exposes arbitrary resources (files, database records, API responses) through MCP's resource system using URI-based addressing. Resources are registered with a URI template, MIME type, and content handler. Clients request resources by URI, and the server retrieves or generates the content on demand. Supports templated URIs with variables (e.g., `file:///{path}`, `db:///{table}/{id}`) for dynamic content resolution. Resources can be text, binary, or structured data.","intents":["I want to give Claude access to files in my codebase or knowledge base without copying them into the conversation context","I need to expose database records or API responses as resources that Claude can reference and retrieve on demand","I want to implement lazy-loading of large documents so only requested content is sent to the AI model"],"best_for":["Teams building AI agents that need access to large codebases or document repositories","Developers creating knowledge-base integrations where documents are retrieved on-demand","Organizations exposing internal databases or APIs to AI models through a standardized resource interface"],"limitations":["Resource content is fetched synchronously — no streaming or chunked delivery for large files","No built-in caching — repeated requests for the same resource trigger new fetches","URI templating is limited to simple variable substitution — no complex query logic","Binary resources require base64 encoding, increasing payload size by ~33%"],"requires":["Resource handler implementation for each resource type (file system, database, API, etc.)","URI template definition matching the resource addressing scheme","MCP client that supports resource retrieval (e.g., Claude with resources capability)"],"input_types":["Resource URI requests with optional parameters","URI template variables for dynamic content resolution"],"output_types":["Resource content (text, code, JSON, binary data)","MIME type metadata","Error responses for missing or inaccessible resources"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_3","uri":"capability://text.generation.language.prompt.template.definition.and.variable.substitution","name":"prompt template definition and variable substitution","description":"Provides a prompt templating system where reusable prompt templates are registered with variable placeholders and optional descriptions. Templates support variable substitution with context-aware defaults and validation. When invoked, the server resolves variables (from client input, tool outputs, or resource content) and returns the rendered prompt. Supports nested templates and conditional logic through variable references.","intents":["I want to define reusable prompt templates that Claude can invoke to generate consistent outputs for specific tasks","I need to create domain-specific prompts that incorporate dynamic data (e.g., user context, database records) without manual string concatenation","I want to version and manage prompts centrally so changes propagate to all AI applications using them"],"best_for":["Teams standardizing on prompt engineering best practices across multiple AI applications","Developers building AI agents that need consistent, reusable prompts for specific domains","Organizations managing prompt templates as versioned artifacts with audit trails"],"limitations":["Template logic is limited to variable substitution — no conditional branching or loops","No built-in prompt optimization or cost estimation — templates are rendered as-is","Variable resolution is synchronous — cannot fetch missing variables from external sources during rendering","No versioning or rollback mechanism — template changes affect all clients immediately"],"requires":["Prompt template definitions with variable placeholders","Variable values provided by the client or resolved from resources/tools","MCP client that supports prompt template invocation"],"input_types":["Prompt template names and variable values","Context data for variable substitution"],"output_types":["Rendered prompt text ready for AI model input","Metadata about template variables and their values"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_4","uri":"capability://tool.use.integration.client.capability.negotiation.and.feature.discovery","name":"client capability negotiation and feature discovery","description":"Implements MCP's initialization handshake where the server and client exchange capability information (supported tools, resources, prompts, sampling methods). The server advertises its capabilities through the `initialize` response, and the client declares its supported features. This enables graceful degradation when clients don't support certain MCP features (e.g., older clients without sampling support). The server can conditionally expose capabilities based on client capabilities.","intents":["I want my MCP server to work with both old and new MCP clients by advertising capabilities and adapting to what clients support","I need to know what features a connected client supports so I can decide which tools or resources to expose","I want to ensure backward compatibility as MCP evolves without breaking existing integrations"],"best_for":["Teams maintaining MCP servers that need to support multiple client versions","Developers building MCP clients that need to discover server capabilities at runtime","Organizations standardizing on MCP across teams with varying client versions"],"limitations":["Capability negotiation happens once at connection time — no dynamic capability changes after initialization","No versioning mechanism for individual capabilities — all-or-nothing feature support","Client capability declarations are not validated — clients can claim support they don't actually have","No fallback mechanism if a client claims support but fails to execute a capability"],"requires":["MCP client and server both implementing the initialization protocol","Understanding of MCP capability types (tools, resources, prompts, sampling, etc.)"],"input_types":["Client initialization request with declared capabilities","Server capability advertisement in initialization response"],"output_types":["Capability metadata (tool names, resource URI patterns, prompt names)","Protocol version information","Server implementation details"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_5","uri":"capability://text.generation.language.sampling.and.model.invocation.through.mcp","name":"sampling and model invocation through mcp","description":"Enables the MCP server to request the client (typically an AI model or agent framework) to invoke a language model for text generation, reasoning, or decision-making. The server sends a sampling request with a prompt, model parameters (temperature, max_tokens, stop sequences), and optional system context. The client handles the actual model invocation and returns the generated text. This reverses the typical client-server relationship, allowing servers to leverage AI capabilities without embedding a model.","intents":["I want my MCP server to request Claude or another model to generate text or make decisions without embedding the model myself","I need to invoke a language model from within a tool handler to generate content or analyze data","I want to build a multi-turn reasoning loop where the server orchestrates model invocations based on tool outputs"],"best_for":["Developers building MCP servers that need to leverage AI reasoning without embedding a model","Teams creating complex agent workflows where the server orchestrates multiple model invocations","Organizations building tool ecosystems where tools themselves need to invoke language models"],"limitations":["Sampling requests are synchronous — the server blocks until the model responds","No streaming support — entire model output is buffered before returning to the server","Model selection is delegated to the client — the server cannot specify which model to use","No built-in cost tracking or rate limiting — the client controls resource consumption"],"requires":["MCP client that supports sampling (e.g., Claude desktop, agent frameworks)","Model access through the client (API keys, local model, etc.)","Understanding of model parameters (temperature, max_tokens, stop sequences)"],"input_types":["Sampling requests with prompt text and model parameters","System context or instructions for the model","Stop sequences and token limits"],"output_types":["Generated text from the language model","Stop reason (max_tokens, stop_sequence, end_turn, etc.)","Token usage information"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_26apr2004-project-01__cap_6","uri":"capability://tool.use.integration.transport.abstraction.with.multiple.protocol.support","name":"transport abstraction with multiple protocol support","description":"Abstracts the underlying transport mechanism, supporting multiple protocols for client-server communication: stdio (for local processes), HTTP (for network clients), and WebSocket (for real-time bidirectional communication). The server implementation handles protocol-specific details (serialization, connection management, error handling) while exposing a unified MCP message interface. Clients can connect via their preferred transport without the server needing to know the details.","intents":["I want to run my MCP server as a local subprocess that Claude can communicate with via stdio","I need to expose my MCP server over HTTP so remote clients can access it","I want to support real-time bidirectional communication between my MCP server and multiple clients"],"best_for":["Developers building MCP servers that need to work with Claude desktop (stdio) and web clients (HTTP/WebSocket)","Teams deploying MCP servers in containerized environments with network isolation","Organizations building multi-client MCP ecosystems with heterogeneous transport requirements"],"limitations":["Stdio transport is limited to local processes — no network communication","HTTP transport requires polling or WebSocket upgrade for bidirectional communication","WebSocket requires additional infrastructure (reverse proxy, TLS termination) for production use","Transport-specific error handling may differ (e.g., connection timeouts, protocol errors)"],"requires":["Transport implementation for the chosen protocol (stdio, HTTP, WebSocket)","Network configuration for HTTP/WebSocket (ports, TLS certificates, reverse proxy)","MCP client compatible with the chosen transport"],"input_types":["MCP messages in JSON-RPC format","Transport-specific framing (stdio: newline-delimited JSON, HTTP: request bodies, WebSocket: frames)"],"output_types":["MCP responses in JSON-RPC format","Transport-specific framing and headers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["MCP client library compatible with the server implementation language","Network connectivity between MCP client and server (local socket, HTTP, or stdio transport)","Understanding of JSON-RPC 2.0 protocol and MCP specification (v0.x or later)","JSON Schema knowledge for defining tool input schemas","Handler implementation in the server's native language (Python, Node.js, etc.)","MCP client that supports tool calling (e.g., Claude with tools_use capability)","Resource handler implementation for each resource type (file system, database, API, etc.)","URI template definition matching the resource addressing scheme","MCP client that supports resource retrieval (e.g., Claude with resources capability)","Prompt template definitions with variable placeholders"],"failure_modes":["MCP is still evolving — breaking changes possible in minor versions before 1.0 stabilization","No built-in authentication/authorization — relies on transport layer (TLS, API keys) for security","Synchronous request/response model may not suit real-time streaming use cases without additional buffering","Limited to JSON serialization — binary data requires base64 encoding, increasing payload size","Schema validation is JSON Schema only — no support for custom validation logic beyond type constraints","Tool execution is synchronous — long-running tools block the server until completion","No built-in retry logic or timeout handling — must be implemented in tool handlers","Schema complexity is limited by JSON Schema expressiveness — some domain-specific constraints require custom validation code","Resource content is fetched synchronously — no streaming or chunked delivery for large files","No built-in caching — repeated requests for the same resource trigger new fetches","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.38999999999999996,"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":"2026-05-03T15:19:41.319Z","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=26apr2004-project-01","compare_url":"https://unfragile.ai/compare?artifact=26apr2004-project-01"}},"signature":"cPJI9GYeJjoLn6crTg+/mlf2tRPCXeRYY1rPfsRwFK/O1quy4jKsG3IzGq5+rNWCYIQTYRcqh/v/tUtGqpuIBw==","signedAt":"2026-06-20T06:21:09.490Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/26apr2004-project-01","artifact":"https://unfragile.ai/26apr2004-project-01","verify":"https://unfragile.ai/api/v1/verify?slug=26apr2004-project-01","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"}}