{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-langchain-mcp-adapters","slug":"langchain-mcp-adapters","name":"@langchain/mcp-adapters","type":"mcp","url":"https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters/","page_url":"https://unfragile.ai/langchain-mcp-adapters","categories":["mcp-servers"],"tags":["langchain","mcp","model-context-protocol","ai","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-langchain-mcp-adapters__cap_0","uri":"capability://tool.use.integration.mcp.server.to.langchain.tool.adapter","name":"mcp server-to-langchain tool adapter","description":"Converts Model Context Protocol (MCP) servers into LangChain-compatible Tool objects through a standardized adapter pattern. The adapter introspects MCP server capabilities (resources, prompts, tools) and wraps them as LangChain ToolInterface implementations, enabling seamless integration of MCP-exposed functionality into LangChain agent chains without manual schema translation or binding code.","intents":["I want to use an existing MCP server (like Brave Search or GitHub) as a tool in my LangChain agent without rewriting its interface","I need to expose MCP server capabilities to LangChain's tool-calling infrastructure with automatic schema mapping","I'm building a multi-tool agent and want to mix MCP servers with native LangChain tools in a single orchestration layer"],"best_for":["LangChain.js developers integrating with MCP ecosystem","Teams building agent systems that need to leverage existing MCP server implementations","Developers migrating from custom tool bindings to standardized MCP protocol"],"limitations":["Requires MCP server to be running and accessible (local or remote) — no built-in server lifecycle management","Schema translation overhead adds ~50-100ms per tool invocation for introspection and marshaling","Limited to MCP protocol capabilities — cannot adapt non-MCP tools or legacy protocols without additional wrappers","No built-in caching of MCP server schemas — re-introspects on each adapter instantiation unless manually cached"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server implementation (local or remote)","TypeScript 4.7+ (for type safety, though JavaScript works)"],"input_types":["MCP server configuration (host, port, or stdio transport)","MCP protocol messages (JSON-RPC 2.0 format)","Tool invocation parameters (structured JSON)"],"output_types":["LangChain ToolInterface objects","Tool execution results (JSON or text)","Error messages and status codes"],"categories":["tool-use-integration","adapter-pattern"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_1","uri":"capability://memory.knowledge.mcp.resource.to.context.injection","name":"mcp resource-to-context injection","description":"Extracts and injects MCP server resources (documents, files, structured data) into LangChain's context/memory systems through a resource adapter. The adapter reads MCP resource URIs, fetches content via the MCP protocol, and converts them into LangChain-compatible context formats (Document objects, memory stores, or RAG-ready embeddings), enabling agents to access external knowledge without explicit tool calls.","intents":["I want my LangChain agent to automatically load and reference documents from an MCP resource server (e.g., file system, database) as context","I need to populate a LangChain retriever with content from MCP resources without manual ETL","I'm building a knowledge-augmented agent that should have access to MCP-served documents in its context window"],"best_for":["Developers building RAG-augmented agents that source documents from MCP servers","Teams with centralized knowledge bases exposed via MCP wanting to integrate with LangChain","Multi-agent systems needing shared resource access through MCP protocol"],"limitations":["Resource fetching is synchronous per request — no built-in batching or parallel resource loading","Large resources (>10MB) may exceed context window limits — requires manual chunking or filtering","No automatic resource change detection — agents see stale data until adapter is re-instantiated","MCP resource URIs must be known in advance — no built-in resource discovery or listing"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server with resource capability enabled","LangChain document loaders or custom Document parsers"],"input_types":["MCP resource URIs (string identifiers)","Resource filtering criteria (optional)","Content type hints (text, JSON, binary)"],"output_types":["LangChain Document objects","Structured context objects","Embeddings (if RAG integration enabled)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_10","uri":"capability://safety.moderation.mcp.tool.result.validation.and.schema.enforcement","name":"mcp tool result validation and schema enforcement","description":"Validates MCP tool results against declared schemas and enforces type safety through a validation layer that parses tool responses, checks against JSON Schema definitions, and raises errors for schema violations. The validator supports custom validation rules, type coercion, and detailed error reporting, preventing downstream errors from malformed MCP responses and enabling type-safe tool result handling in LangChain chains.","intents":["I want my LangChain agent to validate MCP tool results against their declared schemas to catch errors early","I need to enforce type safety for MCP tool results before passing them to downstream LLM calls","I'm building a robust agent system that should fail fast on invalid MCP responses instead of propagating errors"],"best_for":["Developers building production agents requiring high reliability","Teams integrating with MCP servers that may return malformed responses","Systems needing strict type safety for tool results"],"limitations":["Validation adds latency (~10-50ms per tool result depending on schema complexity)","Schema enforcement is strict — may reject valid but non-conformant responses","No built-in schema versioning — schema changes require manual updates","Custom validation rules require manual implementation — no built-in domain-specific validators"],"requires":["Node.js 18+","@langchain/core >=0.1.0","JSON Schema validator (ajv or similar)","MCP tool schema definitions"],"input_types":["MCP tool results (JSON or text)","Tool schema definitions (JSON Schema format)","Validation rules and coercion policies"],"output_types":["Validated tool results","Validation error reports","Type-coerced results (if coercion enabled)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_11","uri":"capability://tool.use.integration.mcp.multi.server.orchestration.and.routing","name":"mcp multi-server orchestration and routing","description":"Orchestrates multiple MCP servers and routes tool calls to appropriate servers based on capability matching, load balancing, or explicit routing rules through a routing layer. The layer maintains a registry of available MCP servers, their capabilities, and health status, matches incoming tool requests to capable servers, and distributes load across servers, enabling agents to leverage multiple MCP servers transparently without explicit server selection.","intents":["I want my LangChain agent to automatically route tool calls to the best MCP server based on capability matching","I need to distribute load across multiple MCP servers to improve throughput and reliability","I'm building a multi-server agent system where different tools are exposed by different MCP servers"],"best_for":["Developers building agents with multiple MCP servers","Teams deploying MCP servers across multiple instances or regions","Systems requiring load balancing and failover across MCP servers"],"limitations":["Capability matching adds latency (~50-100ms per tool call for routing decision)","No built-in load balancing algorithm — requires manual configuration of routing rules","Server health checking is passive (based on failures) — no proactive health monitoring","Routing decisions are static per session — cannot adapt to runtime changes in server availability"],"requires":["Node.js 18+","@langchain/core >=0.1.0","Multiple MCP server instances","Optional: load balancer or service discovery system"],"input_types":["MCP server configurations (multiple servers)","Tool request with capability requirements","Routing rules and load balancing policy"],"output_types":["Selected MCP server for tool execution","Routing decision metadata","Load distribution statistics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_2","uri":"capability://text.generation.language.mcp.prompt.template.adapter","name":"mcp prompt template adapter","description":"Converts MCP prompt definitions (reusable prompt templates with arguments) into LangChain PromptTemplate objects through schema introspection and binding. The adapter reads MCP prompt metadata (name, description, arguments), maps argument types to LangChain variable placeholders, and creates executable prompt templates that can be chained with LLMs, enabling prompt reuse across MCP and LangChain ecosystems.","intents":["I want to use MCP-defined prompt templates in my LangChain chains without rewriting them as LangChain PromptTemplates","I need to share prompt definitions between MCP clients and LangChain agents with a single source of truth","I'm building a system where prompts are centrally managed via MCP and consumed by multiple LangChain applications"],"best_for":["Teams managing centralized prompt libraries exposed via MCP","LangChain developers wanting to leverage existing MCP prompt definitions","Multi-agent systems with shared prompt templates across MCP and LangChain"],"limitations":["MCP prompt argument types may not map 1:1 to LangChain template variable types — requires manual type coercion","No built-in prompt versioning — adapter uses latest MCP prompt definition, no historical access","Limited to MCP prompt capabilities — cannot adapt prompts from non-MCP sources","Prompt execution is synchronous — no streaming or async prompt generation support"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server with prompt capability","LangChain LLM instance for prompt execution"],"input_types":["MCP prompt identifiers (string names)","Prompt argument values (JSON object)","Optional argument overrides"],"output_types":["LangChain PromptTemplate objects","Formatted prompt strings","LLM-ready prompt inputs"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_3","uri":"capability://tool.use.integration.mcp.transport.abstraction.layer","name":"mcp transport abstraction layer","description":"Provides a unified transport abstraction for MCP communication (stdio, HTTP, WebSocket) that abstracts protocol-level details from LangChain adapters. The layer handles connection lifecycle (setup, teardown, reconnection), message serialization (JSON-RPC 2.0), and error handling, allowing adapters to work with any MCP transport without transport-specific code, enabling flexible deployment (local servers, remote APIs, containerized services).","intents":["I want to connect my LangChain agent to MCP servers via different transports (stdio, HTTP, WebSocket) without changing adapter code","I need reliable connection management and automatic reconnection for MCP servers in production","I'm deploying MCP servers in different environments (local, cloud, containerized) and need a unified interface"],"best_for":["Developers building production LangChain agents with MCP integration","Teams deploying MCP servers across multiple environments (local, cloud, edge)","Systems requiring flexible MCP transport switching without code changes"],"limitations":["Transport abstraction adds ~20-50ms latency per message due to serialization and marshaling overhead","No built-in load balancing or failover across multiple MCP servers — single-server per adapter instance","HTTP transport requires MCP server to expose HTTP endpoint — not all MCP servers support this","WebSocket transport requires persistent connection — may not work in all network environments (proxies, firewalls)"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server implementation","Transport-specific dependencies (e.g., ws for WebSocket)"],"input_types":["Transport configuration (type, host, port, credentials)","MCP protocol messages (JSON-RPC 2.0)","Connection parameters (timeouts, retries)"],"output_types":["MCP protocol responses (JSON-RPC 2.0)","Connection status events","Error messages and diagnostics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_4","uri":"capability://automation.workflow.mcp.error.handling.and.retry.logic","name":"mcp error handling and retry logic","description":"Implements standardized error handling and exponential backoff retry logic for MCP communication failures through a resilience layer. The layer catches MCP protocol errors (timeouts, connection failures, invalid responses), applies configurable retry strategies (exponential backoff, jitter), and provides detailed error context to LangChain agents, enabling graceful degradation and automatic recovery without explicit error handling in adapter code.","intents":["I want my LangChain agent to automatically retry failed MCP tool calls with exponential backoff instead of failing immediately","I need detailed error information from MCP failures to debug integration issues and monitor system health","I'm building a resilient agent system that should handle transient MCP server failures gracefully"],"best_for":["Production LangChain agents requiring high availability and resilience","Teams integrating with unreliable or remote MCP servers","Systems needing detailed error diagnostics and monitoring for MCP failures"],"limitations":["Retry logic adds latency (exponential backoff can delay responses by seconds for repeated failures)","No built-in circuit breaker — will continue retrying even if MCP server is permanently down","Retry configuration is global per adapter — cannot set per-tool retry policies","Error context may be lossy for complex MCP failures — some protocol-level details may not propagate to LangChain"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server connection","Optional: monitoring/logging infrastructure"],"input_types":["MCP protocol messages","Retry configuration (max attempts, backoff multiplier, jitter)","Error context and diagnostics"],"output_types":["Successful MCP responses (after retries)","Detailed error objects with retry metadata","Diagnostic logs and traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_5","uri":"capability://tool.use.integration.mcp.server.capability.discovery.and.introspection","name":"mcp server capability discovery and introspection","description":"Automatically discovers and introspects MCP server capabilities (available tools, resources, prompts, sampling methods) through protocol-level introspection without requiring manual capability declarations. The discovery mechanism queries the MCP server's capability manifest, parses tool schemas, resource types, and prompt definitions, and exposes them as queryable metadata, enabling dynamic tool registration and capability-aware agent routing.","intents":["I want my LangChain agent to automatically discover what tools and resources an MCP server provides without manual configuration","I need to dynamically register MCP server tools in my agent based on what the server actually supports","I'm building a capability-aware agent that routes requests to MCP servers based on their declared capabilities"],"best_for":["Developers building dynamic agent systems with multiple MCP servers","Teams deploying MCP servers with evolving capabilities without code changes","Systems requiring capability-aware routing and tool selection"],"limitations":["Introspection adds startup latency (~500ms-2s per MCP server depending on capability count)","No built-in caching of capability metadata — re-introspects on each adapter instantiation","Capability discovery is one-time at initialization — cannot detect runtime capability changes","Complex tool schemas may not fully translate to LangChain's tool interface — requires manual schema mapping for edge cases"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server with capability manifest support","Network connectivity to MCP server"],"input_types":["MCP server connection","Optional: capability filters (e.g., only tools, exclude resources)"],"output_types":["Capability metadata (tools, resources, prompts, sampling methods)","Tool schemas (JSON Schema format)","Resource type definitions","Prompt argument specifications"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_6","uri":"capability://text.generation.language.mcp.sampling.method.integration","name":"mcp sampling method integration","description":"Integrates MCP sampling methods (server-side LLM sampling capabilities) with LangChain's LLM interface through a sampling adapter. The adapter wraps MCP sampling method definitions, handles sampling parameter marshaling, and executes sampling requests through the MCP protocol, enabling LangChain agents to leverage server-side LLM capabilities (e.g., specialized models, fine-tuned variants) without local LLM instantiation.","intents":["I want my LangChain agent to use specialized LLM models exposed via MCP sampling methods instead of my default LLM","I need to leverage server-side LLM capabilities (fine-tuned models, specialized domains) without running them locally","I'm building a multi-model agent that routes requests to different LLMs based on MCP sampling method availability"],"best_for":["Developers building multi-model agents with MCP-exposed LLM capabilities","Teams with specialized LLMs exposed via MCP wanting to integrate with LangChain","Systems requiring dynamic LLM selection based on MCP server capabilities"],"limitations":["Sampling method execution adds network latency — not suitable for low-latency local inference","Limited to MCP sampling method capabilities — cannot customize sampling parameters beyond what MCP server exposes","No built-in streaming support for sampling methods — responses are buffered and returned as complete","Sampling method availability is static at initialization — cannot detect new methods at runtime"],"requires":["Node.js 18+","@langchain/core >=0.1.0","MCP server with sampling method capability","Network connectivity to MCP server"],"input_types":["Sampling method identifiers","LLM prompt and parameters","Sampling configuration (temperature, max_tokens, etc.)"],"output_types":["LLM completion responses","Sampling metadata (model used, tokens consumed, etc.)","Error messages for sampling failures"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_7","uri":"capability://memory.knowledge.mcp.context.window.management","name":"mcp context window management","description":"Manages context window allocation and token budgeting for MCP-integrated agents through a context manager that tracks token usage across MCP resources, tools, and prompts, and implements strategies for context prioritization (recent messages, high-relevance resources, critical tools). The manager prevents context overflow by truncating or filtering low-priority context, enabling agents to operate within LLM token limits while maintaining access to MCP capabilities.","intents":["I want my LangChain agent to automatically manage context window when using MCP resources and tools to avoid token limit errors","I need to prioritize which MCP resources and tool results to include in the context when token budget is limited","I'm building a long-running agent that needs to track and optimize token usage across multiple MCP interactions"],"best_for":["Developers building token-constrained agents with MCP integration","Teams managing long-running agents with multiple MCP interactions","Systems requiring fine-grained control over context allocation"],"limitations":["Context prioritization heuristics may be suboptimal for domain-specific use cases — requires tuning","Token counting is approximate (uses tiktoken or similar) — actual token usage may vary by LLM","No built-in learning from past context decisions — prioritization is static per session","Truncating context may lose important information — no automatic recovery or re-fetching of truncated data"],"requires":["Node.js 18+","@langchain/core >=0.1.0","Token counter (tiktoken or similar)","LLM with known context window size"],"input_types":["MCP resources and tool results","Context prioritization rules","Token budget and LLM context window size"],"output_types":["Prioritized context items","Token usage reports","Context truncation decisions and metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_8","uri":"capability://memory.knowledge.mcp.tool.result.caching.and.memoization","name":"mcp tool result caching and memoization","description":"Caches MCP tool execution results and resource fetches through a memoization layer that stores results keyed by tool name, arguments, and timestamp, enabling agents to reuse results for identical or similar requests without re-executing MCP tools. The cache supports TTL-based expiration, LRU eviction, and optional persistent storage, reducing latency and MCP server load for repeated queries.","intents":["I want my LangChain agent to cache MCP tool results to avoid redundant calls for the same query","I need to reduce latency for repeated MCP resource fetches by caching results locally","I'm building a multi-turn agent where previous tool results should be reused in subsequent turns"],"best_for":["Developers building agents with repeated MCP tool calls","Teams optimizing MCP server load and agent latency","Systems with multi-turn conversations requiring result reuse"],"limitations":["Cache invalidation is time-based (TTL) — no automatic detection of stale data","Cache key generation may miss semantically equivalent queries (e.g., 'weather in NYC' vs 'weather in New York City')","In-memory cache has limited size — large result sets may cause memory pressure","No built-in cache warming or preloading — cache is populated on-demand"],"requires":["Node.js 18+","@langchain/core >=0.1.0","Optional: persistent cache backend (Redis, SQLite, etc.)"],"input_types":["MCP tool name and arguments","Cache configuration (TTL, max size, eviction policy)","Optional: cache key customization"],"output_types":["Cached tool results","Cache hit/miss metadata","Cache statistics (size, eviction count, etc.)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-langchain-mcp-adapters__cap_9","uri":"capability://automation.workflow.mcp.agent.state.persistence.and.recovery","name":"mcp agent state persistence and recovery","description":"Persists MCP-integrated agent state (tool execution history, context, decision traces) to external storage and enables recovery from failures through a state management layer. The layer serializes agent state including MCP tool calls, results, and context, stores it in configurable backends (file system, database, cloud storage), and restores state on recovery, enabling long-running agents to resume from checkpoints without losing progress.","intents":["I want my LangChain agent to save its state periodically so it can resume from a checkpoint if it crashes","I need to audit and replay MCP tool calls made by my agent for debugging and compliance","I'm building a long-running agent that should persist its decision history and context across sessions"],"best_for":["Developers building production agents requiring high availability","Teams needing audit trails and replay capabilities for agent decisions","Systems with long-running agents that must survive failures"],"limitations":["State serialization adds latency (~100-500ms per checkpoint depending on state size)","No built-in state versioning — schema changes require manual migration","State recovery is point-in-time — cannot replay partial state or selective recovery","Persistent storage backend must be available and reliable — failures in storage layer block agent execution"],"requires":["Node.js 18+","@langchain/core >=0.1.0","Persistent storage backend (file system, database, cloud storage)","Optional: serialization library (JSON, Protocol Buffers, etc.)"],"input_types":["Agent state (tool calls, results, context, decision traces)","Checkpoint configuration (frequency, storage backend)","Recovery parameters (checkpoint ID, timestamp)"],"output_types":["Serialized agent state","Checkpoint metadata (timestamp, state size, etc.)","Recovered agent state on resume"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","@langchain/core >=0.1.0","MCP server implementation (local or remote)","TypeScript 4.7+ (for type safety, though JavaScript works)","MCP server with resource capability enabled","LangChain document loaders or custom Document parsers","JSON Schema validator (ajv or similar)","MCP tool schema definitions","Multiple MCP server instances","Optional: load balancer or service discovery system"],"failure_modes":["Requires MCP server to be running and accessible (local or remote) — no built-in server lifecycle management","Schema translation overhead adds ~50-100ms per tool invocation for introspection and marshaling","Limited to MCP protocol capabilities — cannot adapt non-MCP tools or legacy protocols without additional wrappers","No built-in caching of MCP server schemas — re-introspects on each adapter instantiation unless manually cached","Resource fetching is synchronous per request — no built-in batching or parallel resource loading","Large resources (>10MB) may exceed context window limits — requires manual chunking or filtering","No automatic resource change detection — agents see stale data until adapter is re-instantiated","MCP resource URIs must be known in advance — no built-in resource discovery or listing","Validation adds latency (~10-50ms per tool result depending on schema complexity)","Schema enforcement is strict — may reject valid but non-conformant responses","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7277281967843395,"quality":0.34,"ecosystem":0.55,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":217576,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain-mcp-adapters","compare_url":"https://unfragile.ai/compare?artifact=langchain-mcp-adapters"}},"signature":"A/9fVsu7u/LcXRDpYyF/ew59Xz8PvDWFOpun0JpUmsoPAvsY1z5qOv+Qz2xgnqEdDYx6c6v61J9tzteaVzUNDA==","signedAt":"2026-06-22T09:42:26.277Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-mcp-adapters","artifact":"https://unfragile.ai/langchain-mcp-adapters","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-mcp-adapters","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"}}