{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-unifai","slug":"unifai","name":"UnifAI","type":"mcp","url":"https://github.com/unifai-network/unifai-mcp-server","page_url":"https://unfragile.ai/unifai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-unifai__cap_0","uri":"capability://tool.use.integration.dynamic.tool.discovery.and.registry.via.unifai.network","name":"dynamic tool discovery and registry via unifai network","description":"Discovers and maintains a dynamic registry of available tools by querying the UnifAI Network, enabling MCP servers to access tools without pre-configuration. The system queries a centralized network index to retrieve tool metadata, schemas, and endpoints, then caches and updates this registry at runtime. This allows tools to be added or removed from the network without requiring server restarts or code changes.","intents":["I want my MCP server to automatically discover new tools added to the UnifAI Network without redeployment","I need to query what tools are available in the network before deciding which ones to call","I want to avoid hardcoding tool endpoints and instead resolve them dynamically at runtime"],"best_for":["teams building extensible MCP-based agent systems","developers integrating with decentralized tool networks","organizations wanting to decouple tool availability from server configuration"],"limitations":["Network latency for tool discovery queries adds ~100-500ms per discovery cycle","Requires network connectivity to UnifAI Network — offline operation not supported","Tool registry consistency depends on network propagation delays; newly registered tools may not be immediately discoverable","No built-in caching strategy specification — cache invalidation timing is implementation-dependent"],"requires":["Node.js 16+ or Python 3.8+","Network connectivity to UnifAI Network endpoints","Valid UnifAI Network credentials or API key for authentication","MCP protocol support (Claude SDK, LangChain, or compatible client)"],"input_types":["tool query parameters (name, category, capability filters)","network endpoint configuration"],"output_types":["tool metadata (name, description, schema, endpoint)","tool availability status","structured tool registry (JSON/YAML)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_1","uri":"capability://tool.use.integration.schema.based.tool.invocation.with.multi.provider.support","name":"schema-based tool invocation with multi-provider support","description":"Executes tools discovered from the UnifAI Network by marshaling function calls through standardized JSON schemas and routing to the appropriate provider endpoints. The system validates input parameters against tool schemas, handles authentication per-provider, and manages response serialization back to the calling MCP client. Supports heterogeneous tool implementations (REST APIs, gRPC, native functions) through a unified invocation interface.","intents":["I want to call a tool from the UnifAI Network and get back structured results without knowing its implementation details","I need to validate that my tool inputs match the expected schema before sending them to the tool provider","I want to handle authentication and error responses consistently across tools from different providers"],"best_for":["AI agents that need to call diverse external tools","MCP server implementations integrating with tool networks","developers building tool aggregation platforms"],"limitations":["Schema validation only checks structure — semantic validation (e.g., valid email format) requires custom validators","Provider-specific error responses are passed through without normalization — clients must handle provider-specific error formats","No built-in retry logic or circuit breaker — transient failures require client-side handling","Timeout handling is provider-dependent; no unified timeout enforcement across all tool types"],"requires":["Tool schema in JSON Schema format","Provider authentication credentials (API keys, OAuth tokens, etc.)","Network connectivity to tool provider endpoints","MCP client that supports tool calling (Claude, LangChain agents, etc.)"],"input_types":["JSON-serialized function arguments matching tool schema","authentication tokens/credentials","tool identifier (name or network address)"],"output_types":["JSON-serialized tool response","error objects with provider-specific error codes","execution metadata (latency, provider, status)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_2","uri":"capability://tool.use.integration.mcp.protocol.bridging.for.tool.network.access","name":"mcp protocol bridging for tool network access","description":"Implements the Model Context Protocol (MCP) server interface to expose UnifAI Network tools as MCP resources and tools, enabling any MCP-compatible client (Claude, LangChain, custom agents) to discover and invoke network tools. The server translates between MCP's resource/tool model and UnifAI's tool registry, handling MCP message serialization, request routing, and response formatting according to the MCP specification.","intents":["I want to use Claude or my LangChain agent to call tools from the UnifAI Network without custom integration code","I need to expose UnifAI tools as standard MCP resources so any MCP client can access them","I want to build an MCP server that bridges between my existing tools and the UnifAI Network"],"best_for":["Claude users wanting to extend Claude's capabilities with UnifAI tools","LangChain developers integrating tool networks into agent workflows","teams building MCP-compatible AI applications"],"limitations":["MCP protocol overhead adds ~50-200ms per request due to serialization and message framing","Streaming responses are limited by MCP's request-response model — long-running tool executions require polling or websocket extensions","Tool discovery through MCP is read-only — cannot register new tools to UnifAI Network through MCP interface","MCP client must support the specific MCP version implemented; version mismatches cause incompatibility"],"requires":["MCP-compatible client (Claude, LangChain 0.1+, or custom implementation)","Node.js 16+ or Python 3.8+","MCP protocol version 2024-01 or later","Network connectivity between MCP client and server"],"input_types":["MCP protocol messages (JSON-RPC 2.0 format)","tool invocation requests with parameters","resource list queries"],"output_types":["MCP tool definitions (schema + metadata)","MCP resource listings","tool execution results formatted as MCP responses","error responses in MCP format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_3","uri":"capability://search.retrieval.tool.capability.filtering.and.semantic.search","name":"tool capability filtering and semantic search","description":"Searches the UnifAI Network tool registry using semantic queries and capability filters to find relevant tools for a given task. The system accepts natural language descriptions or structured capability requirements, queries the network index (likely using embeddings or keyword matching), and returns ranked results with relevance scores. Filters can be applied by category, provider, required permissions, or execution constraints.","intents":["I want to find tools in the UnifAI Network that can perform a specific task (e.g., 'send an email' or 'generate an image')","I need to filter available tools by category, provider, or required permissions before selecting one","I want to search for tools using natural language descriptions rather than exact tool names"],"best_for":["AI agents that need to dynamically select tools based on task requirements","developers building tool discovery UIs","teams managing large tool ecosystems with hundreds of tools"],"limitations":["Semantic search quality depends on embedding model quality — may return irrelevant results for ambiguous queries","No multi-language support — searches must be in the language tools are registered in (likely English)","Filtering is limited to indexed metadata — cannot search tool implementation details or code","Search results are not real-time; newly registered tools may take minutes to appear in search results"],"requires":["UnifAI Network connectivity","Query string or structured filter parameters","Optional: embedding model for semantic search (may be provided by UnifAI)"],"input_types":["natural language query string","structured filter object (category, provider, permissions)","capability requirements (input/output types)"],"output_types":["ranked list of tool metadata","relevance scores per result","tool availability and provider information"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_4","uri":"capability://automation.workflow.tool.execution.context.and.state.management","name":"tool execution context and state management","description":"Manages execution context for tool calls including parameter binding, state tracking across multi-step tool chains, and result caching. The system maintains execution state (current tool, parameters, intermediate results) and provides context to subsequent tool calls, enabling sequential tool composition. Implements optional result caching to avoid redundant tool invocations with identical parameters.","intents":["I want to call multiple tools in sequence where later tools depend on results from earlier tools","I need to cache tool results to avoid calling the same tool twice with identical parameters","I want to track execution state and debug what parameters were passed to each tool in a chain"],"best_for":["agents executing multi-step workflows with tool dependencies","applications with expensive tool calls that benefit from caching","debugging and monitoring tool execution chains"],"limitations":["State is not persisted across server restarts — in-memory state is lost on failure","Caching is parameter-based only — cannot detect semantic equivalence (e.g., 'John' vs 'john')","No distributed state management — state is local to a single MCP server instance","Cache invalidation is manual or time-based; no automatic invalidation when tool implementations change"],"requires":["MCP server instance with state management enabled","Optional: external state store (Redis, database) for persistence","Tool execution context passed between calls"],"input_types":["tool execution results from previous calls","parameter bindings and variable substitutions","cache configuration (TTL, size limits)"],"output_types":["execution context object with state history","cached results (if available)","execution trace with timing and parameters"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_5","uri":"capability://tool.use.integration.provider.authentication.and.credential.management","name":"provider authentication and credential management","description":"Manages authentication credentials for tools from different providers, supporting multiple auth schemes (API keys, OAuth 2.0, mTLS, custom headers). The system stores credentials securely (encrypted at rest), handles token refresh for OAuth flows, and injects appropriate credentials into tool invocation requests. Supports per-user credentials and per-tool credential overrides.","intents":["I want to securely store API keys for tools without exposing them in logs or configuration files","I need to handle OAuth token refresh automatically when calling tools that require OAuth","I want different users to use their own credentials when calling shared tools"],"best_for":["multi-tenant MCP servers serving multiple users","applications integrating with third-party APIs requiring authentication","security-conscious teams managing sensitive credentials"],"limitations":["Credential storage security depends on underlying system security — no guarantee against privileged access","OAuth token refresh requires network connectivity to auth provider — offline operation not supported","No built-in credential rotation — manual credential updates required","Credential scope is limited to MCP server instance — cannot share credentials across multiple servers"],"requires":["Secure credential storage backend (environment variables, secrets manager, encrypted database)","Provider-specific auth configuration (OAuth endpoints, API key format, etc.)","Optional: OAuth 2.0 client credentials for token refresh flows"],"input_types":["credential type (API key, OAuth token, mTLS cert)","provider identifier","user identifier (for per-user credentials)"],"output_types":["authenticated request headers/parameters","token refresh status","credential validation results"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_6","uri":"capability://automation.workflow.tool.error.handling.and.graceful.degradation","name":"tool error handling and graceful degradation","description":"Handles tool execution errors with provider-specific error parsing, fallback strategies, and graceful degradation. The system catches tool invocation failures, parses provider-specific error responses, attempts retries with exponential backoff, and can fall back to alternative tools or cached results. Provides detailed error context to agents for decision-making.","intents":["I want my agent to retry a failed tool call automatically instead of failing immediately","I need to understand why a tool call failed and what the provider's error message was","I want to fall back to an alternative tool if my preferred tool is unavailable"],"best_for":["production agents requiring high reliability","applications integrating with unreliable or rate-limited APIs","teams building resilient tool orchestration systems"],"limitations":["Retry logic is generic — cannot detect non-retryable errors (auth failures, invalid parameters) without provider-specific configuration","Exponential backoff may exceed agent timeout limits for slow-recovering services","Fallback tool selection is manual — no automatic selection of semantically equivalent tools","Error context is provider-specific — agents must handle different error formats per provider"],"requires":["Tool provider error response format documentation","Retry configuration (max attempts, backoff strategy)","Optional: alternative tools for fallback"],"input_types":["tool execution error response","retry configuration","fallback tool list"],"output_types":["parsed error object with standardized fields","retry attempt count and backoff delay","fallback tool recommendation","detailed error context for logging"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_7","uri":"capability://automation.workflow.tool.usage.monitoring.and.analytics","name":"tool usage monitoring and analytics","description":"Tracks tool invocation metrics (latency, success rate, error rate, cost) and provides analytics dashboards or exportable reports. The system logs each tool call with parameters, results, execution time, and provider information, enabling usage analysis and cost tracking. Supports filtering by tool, provider, user, or time range.","intents":["I want to see which tools are being used most frequently and by whom","I need to track the cost of tool invocations to understand API spending","I want to identify slow or unreliable tools based on execution metrics"],"best_for":["teams managing shared tool infrastructure","applications with cost-sensitive tool usage","organizations requiring audit trails for compliance"],"limitations":["Logging overhead adds latency to tool execution — may impact performance for latency-sensitive applications","Analytics are historical only — no real-time alerting on anomalies","Cost tracking requires provider-specific cost models — may be inaccurate for complex pricing schemes","No built-in data retention policy — logs can grow unbounded without manual cleanup"],"requires":["Logging backend (file system, database, or cloud logging service)","Optional: analytics dashboard or BI tool for visualization","Provider cost configuration for cost tracking"],"input_types":["tool invocation events (tool name, parameters, result, latency)","user/tenant identifier","provider information"],"output_types":["usage statistics (count, latency percentiles, error rate)","cost reports (total cost, cost per tool, cost per user)","execution logs with full context","analytics dashboard data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unifai__cap_8","uri":"capability://automation.workflow.tool.composition.and.workflow.templating","name":"tool composition and workflow templating","description":"Enables definition of reusable tool workflows as templates that compose multiple tools with conditional logic, loops, and data transformations. Templates are defined declaratively (likely in YAML or JSON) and executed by the MCP server, handling parameter passing between tools and managing execution flow. Supports variable substitution, conditional branches, and error handling within workflows.","intents":["I want to define a reusable workflow that calls multiple tools in sequence without writing custom agent code","I need to compose tools with conditional logic (if tool A succeeds, call tool B; otherwise call tool C)","I want to transform data between tool calls (e.g., extract a field from tool A's result and pass it to tool B)"],"best_for":["teams building repetitive multi-tool workflows","non-technical users defining tool compositions","applications with complex tool orchestration requirements"],"limitations":["Template language expressiveness is limited — complex logic may require custom code","No visual workflow editor — templates must be written in text format","Debugging template execution is difficult — error messages may not pinpoint the failing step","Template versioning and rollback are manual — no built-in version control"],"requires":["Template definition in supported format (YAML, JSON, or custom DSL)","Tool definitions for all tools used in the workflow","MCP server with workflow execution support"],"input_types":["workflow template (declarative definition)","workflow input parameters","tool execution results (passed between steps)"],"output_types":["workflow execution result","execution trace with step-by-step results","error details if workflow fails"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or Python 3.8+","Network connectivity to UnifAI Network endpoints","Valid UnifAI Network credentials or API key for authentication","MCP protocol support (Claude SDK, LangChain, or compatible client)","Tool schema in JSON Schema format","Provider authentication credentials (API keys, OAuth tokens, etc.)","Network connectivity to tool provider endpoints","MCP client that supports tool calling (Claude, LangChain agents, etc.)","MCP-compatible client (Claude, LangChain 0.1+, or custom implementation)","MCP protocol version 2024-01 or later"],"failure_modes":["Network latency for tool discovery queries adds ~100-500ms per discovery cycle","Requires network connectivity to UnifAI Network — offline operation not supported","Tool registry consistency depends on network propagation delays; newly registered tools may not be immediately discoverable","No built-in caching strategy specification — cache invalidation timing is implementation-dependent","Schema validation only checks structure — semantic validation (e.g., valid email format) requires custom validators","Provider-specific error responses are passed through without normalization — clients must handle provider-specific error formats","No built-in retry logic or circuit breaker — transient failures require client-side handling","Timeout handling is provider-dependent; no unified timeout enforcement across all tool types","MCP protocol overhead adds ~50-200ms per request due to serialization and message framing","Streaming responses are limited by MCP's request-response model — long-running tool executions require polling or websocket extensions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.050Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=unifai","compare_url":"https://unfragile.ai/compare?artifact=unifai"}},"signature":"9zc05vHq7Zp64jJ9fT2gOP2V841pkFHK7YI7xIeZo+9I68vEbhhT9Ns6AEpPa9fvuBwI737uP2CZPvbcQzyLAQ==","signedAt":"2026-06-22T01:51:28.869Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/unifai","artifact":"https://unfragile.ai/unifai","verify":"https://unfragile.ai/api/v1/verify?slug=unifai","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"}}