{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-grll-mcpadapt","slug":"mcp-grll-mcpadapt","name":"mcpadapt","type":"mcp","url":"https://github.com/grll/mcpadapt","page_url":"https://unfragile.ai/mcp-grll-mcpadapt","categories":["mcp-servers"],"tags":["langchain","llamaindex","modelcontextprotocol","pydantic-ai","smolagents","swarm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-grll-mcpadapt__cap_0","uri":"capability://tool.use.integration.mcp.server.connection.and.lifecycle.management.with.dual.transport.protocols","name":"mcp server connection and lifecycle management with dual transport protocols","description":"Manages bidirectional connections to MCP servers using an adapter pattern that abstracts both StdIO (local subprocess) and SSE (remote HTTP) transport layers. The MCPAdapt class acts as a context manager that establishes connections, negotiates protocol handshakes, maintains connection state, and gracefully closes resources. Supports both synchronous and asynchronous operation patterns through separate code paths, enabling integration with frameworks that require specific concurrency models.","intents":["Connect to local MCP servers running as subprocesses without managing stdio pipes manually","Connect to remote MCP servers over HTTP SSE without implementing protocol negotiation","Manage connection lifecycle automatically with context managers to prevent resource leaks","Switch between local and remote MCP servers with identical API surface"],"best_for":["Teams building multi-framework agent systems that need abstracted server connectivity","Developers integrating 650+ existing MCP servers without writing transport-specific code","Organizations deploying both local and cloud-based MCP server architectures"],"limitations":["No built-in connection pooling or multiplexing — each MCPAdapt instance maintains separate connections","SSE connections lack automatic reconnection logic — network interruptions require manual re-initialization","Synchronous operation blocks the event loop for async frameworks, requiring separate adapter implementations","No connection timeout configuration exposed in public API — uses framework defaults"],"requires":["Python 3.10+","mcp >= 1.2.0 library installed","For StdIO: MCP server executable in PATH or absolute path provided","For SSE: HTTP-accessible MCP server endpoint with valid SSL certificates"],"input_types":["server_params dict with 'command' (StdIO) or 'url' (SSE)","environment variables for server configuration"],"output_types":["Connected MCPAdapt instance with initialized tool registry","Tool list with JSON schema specifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_1","uri":"capability://tool.use.integration.framework.agnostic.tool.schema.transformation.and.adaptation","name":"framework-agnostic tool schema transformation and adaptation","description":"Implements a ToolAdapter interface that defines abstract methods for converting MCP tool specifications (JSON schemas with input/output types) into framework-specific tool formats. Each supported framework (Smolagents, LangChain, CrewAI, Google GenAI) has a concrete adapter that translates MCP's canonical tool schema into that framework's expected tool definition structure, parameter validation rules, and execution signatures. The transformation preserves tool semantics while conforming to each framework's tool calling conventions.","intents":["Convert MCP tool schemas to Smolagents tool format without manual schema rewriting","Adapt MCP tools to LangChain's BaseTool interface with proper parameter binding","Transform tool specifications for CrewAI agents with role-based access control","Map MCP tools to Google GenAI's function calling schema format"],"best_for":["Developers building agents with multiple frameworks who want to reuse MCP tool definitions","Teams migrating between agentic frameworks without rewriting tool integrations","Organizations standardizing on MCP as a tool definition format across heterogeneous agent systems"],"limitations":["Adapter implementations are framework-specific — adding new framework support requires new adapter class","Tool schema validation is delegated to target framework — incompatible schemas may fail at runtime rather than adaptation time","No automatic parameter type coercion — frameworks must handle type mismatches between MCP schema and runtime values","Async/sync mismatch: Smolagents and CrewAI adapters are sync-only, while LangChain and Google GenAI support both"],"requires":["Python 3.10+","Target framework installed (smolagents, langchain, crewai, or google-generativeai)","MCP tool schema with valid JSON schema format","pydantic >= 2.10.6 for schema validation"],"input_types":["MCP tool specification dict with name, description, inputSchema","Tool execution function/callable"],"output_types":["Framework-specific tool object (BaseTool, Tool, etc.)","Callable with framework-expected signature"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_10","uri":"capability://tool.use.integration.local.mcp.server.lifecycle.management.via.stdio.transport","name":"local mcp server lifecycle management via stdio transport","description":"Manages local MCP servers running as subprocesses using the StdIO (standard input/output) transport protocol. MCPAdapt spawns the server process, establishes bidirectional communication through stdin/stdout pipes, and handles process lifecycle events (startup, shutdown, crashes). The StdIO transport is the standard for local MCP servers, enabling integration with tools like Claude Desktop and local development environments.","intents":["Run local MCP servers as subprocesses without managing process creation manually","Communicate with local MCP servers via stdin/stdout pipes","Handle server process crashes and restart scenarios","Integrate with local development MCP servers for testing"],"best_for":["Developers building agents with local MCP servers","Development and testing environments where servers run locally","Teams using MCP servers implemented in Python, Node.js, or other languages"],"limitations":["StdIO transport is blocking — server process must be responsive or agent hangs","No automatic server restart — if server crashes, agent must be restarted","Server process inherits parent process environment — may cause unexpected behavior with environment variables","Large tool outputs over StdIO may cause pipe buffer overflow — no flow control mechanism","Windows compatibility may be limited for some server implementations"],"requires":["MCP server executable in PATH or absolute path","Python 3.10+","mcp >= 1.2.0 with StdIO transport support"],"input_types":["server_params dict with 'command' key specifying server executable and arguments"],"output_types":["Connected MCPAdapt instance with tools from local server","Bidirectional communication channel to server"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_11","uri":"capability://tool.use.integration.remote.mcp.server.connectivity.via.sse.transport","name":"remote mcp server connectivity via sse transport","description":"Connects to remote MCP servers using the Server-Sent Events (SSE) HTTP transport protocol, enabling integration with cloud-hosted or network-accessible MCP servers. MCPAdapt establishes HTTP connections to the server endpoint, negotiates the MCP protocol over SSE, and maintains the connection for tool invocation. This enables integration with MCP servers that don't run locally, such as cloud services or remote development environments.","intents":["Connect to cloud-hosted MCP servers without running local processes","Integrate with remote MCP servers over HTTP/HTTPS","Use MCP tools from network-accessible servers in distributed systems","Enable multi-tenant access to shared MCP server instances"],"best_for":["Cloud-based agents that need to access remote MCP servers","Distributed systems where MCP servers are centralized","Teams using shared MCP server instances across multiple agents"],"limitations":["Network latency adds 50-500ms per tool invocation compared to local servers","No automatic reconnection — network interruptions require manual re-initialization","SSE connections are unidirectional for server-to-client — requires polling for bidirectional communication","No built-in authentication beyond HTTP headers — must implement custom auth if needed","Remote server availability is a dependency — agent fails if server is down"],"requires":["HTTP-accessible MCP server endpoint with valid SSL certificates","Python 3.10+","mcp >= 1.2.0 with SSE transport support","Network connectivity to remote server"],"input_types":["server_params dict with 'url' key specifying server HTTP endpoint"],"output_types":["Connected MCPAdapt instance with tools from remote server","HTTP connection to server"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_2","uri":"capability://tool.use.integration.multi.server.tool.aggregation.and.deduplication","name":"multi-server tool aggregation and deduplication","description":"Enables connecting to multiple MCP servers simultaneously and aggregating their tool catalogs into a unified tool registry. The MCPAdapt class maintains a list of server connections and merges tool definitions from all servers, with built-in deduplication logic to handle tools with identical names across different servers. Tools are exposed as a flat list to the target framework, allowing agents to discover and invoke tools from any connected server without explicit server selection.","intents":["Connect to multiple MCP servers and use all their tools in a single agent","Aggregate tools from specialized servers (e.g., file system, web search, database) into one tool set","Handle tool name collisions when multiple servers provide tools with the same name","Dynamically add or remove MCP servers without restarting the agent"],"best_for":["Agents requiring diverse tool capabilities from multiple specialized MCP servers","Microservice architectures where tools are distributed across multiple MCP server instances","Development workflows that compose tools from different teams or vendors"],"limitations":["No built-in tool namespacing — tool name collisions require manual resolution or server-side renaming","Tool discovery is eager (all tools loaded at initialization) — no lazy loading for large tool catalogs","No cross-server tool dependencies — tools cannot invoke other tools from different servers","Deduplication is name-based only — semantically identical tools with different names are treated as separate"],"requires":["Python 3.10+","Multiple MCP servers configured with distinct server_params","mcp >= 1.2.0"],"input_types":["List of server_params dicts, each specifying a different MCP server"],"output_types":["Unified tool list with tools from all servers","Framework-specific tool objects merged into single registry"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_3","uri":"capability://automation.workflow.synchronous.and.asynchronous.operation.mode.selection","name":"synchronous and asynchronous operation mode selection","description":"Provides dual code paths for synchronous and asynchronous execution, allowing MCPAdapt to integrate with frameworks that have different concurrency requirements. The library exposes both sync context managers and async context managers (mcptools), and framework adapters implement sync/async variants based on framework capabilities. This enables the same MCP server connections to be used in blocking (Smolagents, CrewAI) or non-blocking (LangChain, Google GenAI) frameworks without code duplication.","intents":["Use MCP servers with synchronous frameworks like Smolagents without async/await complexity","Use MCP servers with async frameworks like LangChain without blocking the event loop","Choose sync or async mode based on framework requirements at initialization time","Avoid context switching overhead by matching framework concurrency model"],"best_for":["Teams using multiple agentic frameworks with different concurrency models","Developers building agents that must integrate with both blocking and async code","Organizations standardizing on MCP while maintaining framework diversity"],"limitations":["Smolagents and CrewAI adapters are sync-only — no async support even if underlying MCP server supports it","Async code paths add complexity and potential for deadlocks if sync/async boundaries are crossed incorrectly","No automatic mode detection — developers must explicitly choose sync or async context manager","Mixing sync and async calls in same application requires careful thread/event loop management"],"requires":["Python 3.10+ with asyncio support for async mode","Framework that supports chosen concurrency model (sync or async)","mcp >= 1.2.0 with async transport support"],"input_types":["Framework type selection (smolagents, langchain, crewai, google-genai)","Sync/async context manager choice"],"output_types":["Sync context manager returning MCPAdapt instance","Async context manager returning MCPAdapt instance"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_4","uri":"capability://tool.use.integration.tool.invocation.execution.with.mcp.server.rpc.dispatch","name":"tool invocation execution with mcp server rpc dispatch","description":"Executes tool calls by dispatching Remote Procedure Calls (RPCs) to the connected MCP server using the tool name and input parameters. When a framework invokes a tool, MCPAdapt marshals the parameters into the MCP protocol format, sends the call to the server, waits for the response, and returns the result to the framework. This decouples tool execution from the agent framework — the agent doesn't need to know whether tools are implemented locally or remotely on the MCP server.","intents":["Execute MCP tools from agent frameworks without implementing tool logic in the agent code","Invoke remote tools on MCP servers as if they were local functions","Handle tool execution errors and timeouts transparently","Support tools with complex input validation and output transformation"],"best_for":["Agents that need to invoke tools implemented on remote MCP servers","Microservice architectures where tool logic is centralized on MCP servers","Development workflows where tool implementations are maintained separately from agent code"],"limitations":["Network latency for remote MCP servers adds 50-500ms per tool invocation","No built-in caching of tool results — repeated calls with same parameters hit the server each time","Tool execution errors on MCP server are propagated as exceptions — no automatic retry logic","Large tool outputs (>10MB) may exceed protocol limits or cause memory issues","No streaming support for long-running tool executions — agent must wait for complete result"],"requires":["Active MCP server connection via MCPAdapt context manager","Tool name matching exactly between MCP server definition and invocation","Input parameters matching MCP tool schema (type, required fields, constraints)"],"input_types":["Tool name (string)","Tool input parameters (dict matching JSON schema)"],"output_types":["Tool result (JSON-serializable value)","Error message if tool execution fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_5","uri":"capability://data.processing.analysis.json.schema.resolution.and.tool.parameter.validation","name":"json schema resolution and tool parameter validation","description":"Resolves JSON schema $ref pointers in MCP tool definitions using the jsonref library, enabling tools to use modular schema definitions with shared type definitions. Validates tool input parameters against the resolved schema before execution, catching type mismatches and missing required fields early. This ensures that tools receive well-formed inputs and that schema references don't cause runtime failures when tools are invoked.","intents":["Use MCP tools with complex nested schemas that reference shared type definitions","Validate tool inputs before sending to MCP server to catch errors early","Support tools with reusable schema components via $ref pointers","Provide clear error messages when tool inputs don't match schema"],"best_for":["MCP servers with complex tool schemas using $ref for code reuse","Teams building tools with strict input validation requirements","Agents that need early validation feedback before remote tool invocation"],"limitations":["Circular $ref pointers are not detected — may cause infinite loops during resolution","$ref resolution is eager (at initialization) — large schemas with many references add startup latency","No support for JSON schema draft versions beyond 2020-12 — newer features may not validate correctly","Validation errors don't include suggestions for fixing invalid inputs — only report what's wrong"],"requires":["jsonref >= 1.1.0 library installed","pydantic >= 2.10.6 for schema validation","Valid JSON schema format in MCP tool definitions"],"input_types":["MCP tool schema dict with potential $ref pointers","Tool input parameters dict"],"output_types":["Resolved schema with all $ref pointers expanded","Validation result (pass/fail with error details)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_6","uri":"capability://tool.use.integration.smolagents.framework.tool.integration.with.synchronous.execution","name":"smolagents framework tool integration with synchronous execution","description":"Adapts MCP tools to Smolagents' tool format by implementing the SmolAgentsAdapter class that converts MCP tool schemas into Smolagents Tool objects with proper input/output type annotations. Smolagents tools are synchronous callables that receive parsed input parameters and return results as strings or structured data. The adapter handles the impedance mismatch between MCP's JSON schema format and Smolagents' Python type annotation system.","intents":["Use MCP tools with Smolagents agents without rewriting tool definitions","Integrate 650+ MCP tools into Smolagents workflows","Maintain type safety in Smolagents tool definitions from MCP schemas"],"best_for":["Smolagents users who want to leverage the MCP ecosystem","Teams building lightweight agents that don't require async support","Developers migrating from custom tool definitions to MCP-based tools"],"limitations":["Smolagents adapter is synchronous only — no async/await support","Tool output must be serializable to string for Smolagents — complex objects may require custom serialization","No support for streaming tool outputs — Smolagents expects complete results","Type annotations are inferred from JSON schema — some Python types may not map correctly"],"requires":["smolagents library installed","Python 3.10+","MCP tool with valid JSON schema"],"input_types":["MCP tool specification","Tool execution function"],"output_types":["Smolagents Tool object","String or structured result"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_7","uri":"capability://tool.use.integration.langchain.framework.tool.integration.with.dual.sync.async.support","name":"langchain framework tool integration with dual sync/async support","description":"Adapts MCP tools to LangChain's BaseTool interface by implementing the LangchainAdapter class that converts MCP tool schemas into LangChain Tool objects with proper parameter binding and execution. LangChain tools support both synchronous (_run) and asynchronous (_arun) execution paths, enabling non-blocking tool invocation in async agents. The adapter maps MCP's JSON schema to LangChain's Pydantic-based tool parameter models.","intents":["Use MCP tools with LangChain agents in both sync and async contexts","Integrate MCP tools into LangChain's tool calling infrastructure","Support async tool execution for high-concurrency LangChain applications","Leverage LangChain's tool validation and error handling"],"best_for":["LangChain users building async agents that need MCP tool integration","Teams requiring both synchronous and asynchronous tool execution","Applications using LangChain's tool calling with language models"],"limitations":["Async execution requires MCP server to support async transports — StdIO servers may block event loop","Tool parameter validation is delegated to LangChain's Pydantic models — schema mismatches may cause runtime errors","No built-in tool result caching — LangChain agents must implement caching separately","Large tool outputs may exceed LangChain's context window limits"],"requires":["langchain library installed (0.1.0+)","Python 3.10+","pydantic >= 2.10.6 for parameter model generation"],"input_types":["MCP tool specification","Tool execution function"],"output_types":["LangChain BaseTool object","Tool result (any JSON-serializable type)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_8","uri":"capability://tool.use.integration.crewai.framework.tool.integration.with.role.based.execution","name":"crewai framework tool integration with role-based execution","description":"Adapts MCP tools to CrewAI's tool format by implementing the CrewAIAdapter class that converts MCP tool schemas into CrewAI-compatible tool objects. CrewAI tools are assigned to agents with specific roles, enabling role-based access control and tool usage tracking. The adapter integrates with CrewAI's tool registry and execution model, allowing tools to be invoked within CrewAI's multi-agent orchestration framework.","intents":["Use MCP tools with CrewAI agents assigned to specific roles","Integrate MCP tools into CrewAI's multi-agent workflows","Track tool usage and execution within CrewAI's agent framework","Assign tools to agents based on role requirements"],"best_for":["CrewAI users building multi-agent systems with role-based tool access","Teams using CrewAI's agent orchestration with MCP tool ecosystem","Applications requiring tool usage tracking and audit logs"],"limitations":["CrewAI adapter is synchronous only — no async support","Tools are role-specific — same tool must be re-registered for different agents","No built-in tool result caching — CrewAI agents must implement caching separately","Tool execution errors may interrupt agent workflows — no automatic error recovery"],"requires":["crewai library installed","Python 3.10+","Agent with defined role for tool assignment"],"input_types":["MCP tool specification","Tool execution function","Agent role context"],"output_types":["CrewAI-compatible tool object","Tool result with execution metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-grll-mcpadapt__cap_9","uri":"capability://tool.use.integration.google.genai.framework.tool.integration.with.function.calling","name":"google genai framework tool integration with function calling","description":"Adapts MCP tools to Google GenAI's function calling format by implementing the GoogleGenAIAdapter class that converts MCP tool schemas into Google's function calling schema. Google GenAI models can invoke functions defined in this format, enabling tool use within Google's generative AI models. The adapter supports both synchronous and asynchronous execution, integrating with Google's async client libraries.","intents":["Use MCP tools with Google GenAI models for function calling","Integrate MCP tools into Google's generative AI workflows","Support async tool execution with Google's async clients","Leverage Google's model-based tool selection and invocation"],"best_for":["Google GenAI users who want to leverage the MCP ecosystem","Teams building async applications with Google's generative AI","Applications using Google's function calling for tool use"],"limitations":["Google GenAI function calling has parameter limits — very large schemas may not be supported","Tool selection is model-dependent — some models may not support all tool types","No built-in tool result caching — applications must implement caching separately","Async execution requires Google's async client library"],"requires":["google-generativeai library installed","Python 3.10+","Google API key for authentication"],"input_types":["MCP tool specification","Tool execution function"],"output_types":["Google GenAI function calling schema","Tool result (JSON-serializable)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.10+","mcp >= 1.2.0 library installed","For StdIO: MCP server executable in PATH or absolute path provided","For SSE: HTTP-accessible MCP server endpoint with valid SSL certificates","Target framework installed (smolagents, langchain, crewai, or google-generativeai)","MCP tool schema with valid JSON schema format","pydantic >= 2.10.6 for schema validation","MCP server executable in PATH or absolute path","mcp >= 1.2.0 with StdIO transport support","HTTP-accessible MCP server endpoint with valid SSL certificates"],"failure_modes":["No built-in connection pooling or multiplexing — each MCPAdapt instance maintains separate connections","SSE connections lack automatic reconnection logic — network interruptions require manual re-initialization","Synchronous operation blocks the event loop for async frameworks, requiring separate adapter implementations","No connection timeout configuration exposed in public API — uses framework defaults","Adapter implementations are framework-specific — adding new framework support requires new adapter class","Tool schema validation is delegated to target framework — incompatible schemas may fail at runtime rather than adaptation time","No automatic parameter type coercion — frameworks must handle type mismatches between MCP schema and runtime values","Async/sync mismatch: Smolagents and CrewAI adapters are sync-only, while LangChain and Google GenAI support both","StdIO transport is blocking — server process must be responsive or agent hangs","No automatic server restart — if server crashes, agent must be restarted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3383600669714862,"quality":0.34,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.6,"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":"2025-10-24T15:34:18Z"},"community":{"stars":419,"forks":49,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-grll-mcpadapt","compare_url":"https://unfragile.ai/compare?artifact=mcp-grll-mcpadapt"}},"signature":"Az3VFQlWQLGZ3Yc7Ag6iaYqd4wA45vl129tZi0eRrgUzbobdeurCmeizaot9FWrx38dIt9XDCN9e/OrOkbiFBg==","signedAt":"2026-06-22T01:38:14.231Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-grll-mcpadapt","artifact":"https://unfragile.ai/mcp-grll-mcpadapt","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-grll-mcpadapt","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"}}