{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fetch-mcp-server","slug":"fetch-mcp-server","name":"Fetch MCP Server","type":"mcp","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/fetch","page_url":"https://unfragile.ai/fetch-mcp-server","categories":["mcp-servers"],"tags":["fetch","web","official","reference"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fetch-mcp-server__cap_0","uri":"capability://tool.use.integration.http.url.fetching.with.mcp.tool.protocol.binding","name":"http url fetching with mcp tool protocol binding","description":"Implements MCP tool registration that exposes HTTP GET/POST fetching as a callable tool through the Model Context Protocol's JSON-RPC transport layer. The server registers a 'fetch' tool with input schema validation, handles HTTP requests via Python's urllib or requests library, and returns structured responses that conform to MCP tool result primitives, enabling LLM clients to invoke web fetching as a first-class capability without direct HTTP knowledge.","intents":["I want my LLM agent to fetch web pages and extract content without making direct HTTP calls","I need to expose web fetching as a tool that Claude or other MCP-compatible clients can invoke","I want to standardize how my AI system accesses remote URLs through a protocol-compliant interface"],"best_for":["AI agents and LLM applications using MCP-compatible clients (Claude, etc.)","Teams building multi-tool AI systems where web access is one capability among many","Developers prototyping research assistants that need real-time web data"],"limitations":["No built-in request pooling or connection reuse — each fetch creates a new HTTP connection","Timeout handling is basic; no configurable per-request timeouts in reference implementation","No automatic retry logic for transient failures (5xx errors, network timeouts)","Single-threaded request processing — concurrent fetches are serialized through MCP message queue"],"requires":["Python 3.8+","MCP SDK for Python (mcp package)","Network connectivity to target URLs","MCP-compatible client (Claude Desktop, custom client, etc.)"],"input_types":["URL string (required)","HTTP method (GET/POST, optional)","Request headers (optional JSON object)","Request body (optional for POST)"],"output_types":["HTTP status code (integer)","Response headers (JSON object)","Response body (text/string)","Error messages (structured error object)"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_1","uri":"capability://data.processing.analysis.html.to.markdown.content.conversion.for.llm.consumption","name":"html-to-markdown content conversion for llm consumption","description":"Transforms fetched HTML content into Markdown format optimized for LLM processing using a conversion library (likely html2text or similar). The server parses HTML structure, preserves semantic meaning (headings, lists, links, emphasis), strips unnecessary styling and scripts, and outputs clean Markdown that reduces token consumption and improves LLM comprehension compared to raw HTML. This conversion happens server-side before returning results to the MCP client.","intents":["I want to fetch web pages and get clean Markdown instead of raw HTML for my LLM to process","I need to reduce token usage by converting HTML to semantic Markdown before sending to Claude","I want web content in a format that preserves structure but removes noise (ads, scripts, styles)"],"best_for":["LLM applications where token efficiency is critical (cost-sensitive deployments)","Research assistants that need to process web articles and documentation","Agents that extract information from web pages and need clean, structured text"],"limitations":["Conversion quality depends on HTML structure — malformed or heavily JavaScript-rendered pages may produce suboptimal Markdown","No support for complex interactive elements (forms, embedded widgets) — these are stripped or converted to text descriptions","Images are converted to Markdown image syntax but not embedded; LLMs see only alt text and URLs","JavaScript-rendered content is not executed — only initial HTML is converted, missing dynamic content"],"requires":["Python 3.8+","html2text library or equivalent HTML parsing dependency","Valid HTML input from HTTP response"],"input_types":["HTML string (from HTTP response body)"],"output_types":["Markdown string (UTF-8 text)","Structured metadata (title, headings, links extracted)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_2","uri":"capability://safety.moderation.robots.txt.compliance.checking.and.url.filtering","name":"robots.txt compliance checking and url filtering","description":"Implements robots.txt parsing and compliance validation before fetching URLs, checking the User-Agent against disallowed paths and crawl-delay directives defined in the target domain's robots.txt file. The server fetches and caches robots.txt entries, evaluates requested URLs against allow/disallow rules, and either permits or blocks the fetch based on compliance. This ensures the MCP server respects web scraping conventions and legal/ethical boundaries without requiring clients to implement their own robots.txt logic.","intents":["I want my AI agent to respect robots.txt rules when fetching web content","I need to ensure my LLM application doesn't violate web scraping policies or terms of service","I want to automatically filter out URLs that are disallowed by robots.txt before attempting to fetch them"],"best_for":["Responsible AI agents that need to respect web scraping conventions","Enterprise applications where legal compliance with robots.txt is required","Research tools that need to demonstrate ethical web access practices"],"limitations":["robots.txt is advisory, not enforced by servers — compliance is voluntary and can be bypassed","No support for robots.txt extensions (crawl-delay, request-rate) beyond basic allow/disallow rules","Cached robots.txt entries may become stale; no automatic refresh mechanism for long-running servers","Does not validate User-Agent string format or check for wildcard patterns in all edge cases","No support for robots.txt in non-standard locations or served via HTTP headers instead of /robots.txt"],"requires":["Python 3.8+","urllib.robotparser module (standard library)","Network connectivity to fetch robots.txt from target domains","robots.txt file present at target domain (optional; missing robots.txt is treated as allow-all)"],"input_types":["URL string (to check against robots.txt rules)"],"output_types":["Boolean (allowed/disallowed)","Reason string (if blocked, explains which rule matched)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_3","uri":"capability://tool.use.integration.mcp.tool.schema.definition.and.input.validation","name":"mcp tool schema definition and input validation","description":"Defines the 'fetch' tool's input schema using JSON Schema format (with required fields like 'url' and optional fields like 'method', 'headers', 'body') and validates incoming MCP tool call requests against this schema before processing. The server uses the MCP SDK's tool registration mechanism to declare the schema, and the framework automatically validates inputs, returning structured validation errors if the request doesn't match the schema. This ensures type safety and prevents malformed requests from reaching the HTTP fetching logic.","intents":["I want to define what inputs the fetch tool accepts and enforce type checking","I need to reject invalid fetch requests (missing URL, wrong data types) with clear error messages","I want to document the fetch tool's API contract so clients know what parameters to provide"],"best_for":["MCP server developers building tools with strict input contracts","Teams that need to validate tool inputs before expensive operations (HTTP requests)","Systems where input validation errors should be caught early and reported to clients"],"limitations":["JSON Schema validation is performed by the MCP SDK framework, not custom logic — limited to standard schema constraints","No support for custom validation logic beyond JSON Schema (e.g., regex patterns, cross-field validation)","Schema is static at server startup — cannot be dynamically modified based on runtime conditions","Validation errors are returned as MCP error responses; clients must handle and interpret these errors"],"requires":["Python 3.8+","MCP SDK for Python with tool decorator support","Valid JSON Schema definition for tool inputs"],"input_types":["JSON object (MCP tool call request)"],"output_types":["Validation success (tool execution proceeds)","Validation error (structured error response with schema violation details)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_4","uri":"capability://tool.use.integration.mcp.server.lifecycle.management.and.transport.initialization","name":"mcp server lifecycle management and transport initialization","description":"Manages the MCP server's startup, shutdown, and transport initialization using the Python SDK's Server class and async context managers. The server initializes the MCP protocol handler, registers tools (fetch, etc.) during startup, establishes stdio or network transport for client communication, and gracefully shuts down resources on exit. This lifecycle management ensures the server is ready to receive MCP requests and properly cleans up connections when the client disconnects or the server terminates.","intents":["I want to start an MCP server that listens for fetch requests from Claude or other clients","I need to properly initialize the MCP protocol handler and register my tools","I want to ensure the server shuts down cleanly without leaving dangling connections"],"best_for":["Developers building MCP servers using the Python SDK","Teams deploying MCP servers as long-running processes or Docker containers","Applications that need reliable server startup and shutdown semantics"],"limitations":["Stdio transport is blocking — server cannot handle multiple concurrent clients on a single process","No built-in process management (systemd, supervisor) — requires external orchestration for production deployment","Async/await patterns require Python 3.7+; synchronous code must be wrapped in async adapters","No automatic health checks or liveness probes — clients must detect server unavailability through timeout"],"requires":["Python 3.8+","MCP SDK for Python (mcp package)","asyncio event loop (standard library)","Stdio or network transport available (stdio is default)"],"input_types":["Server configuration (transport type, host, port)"],"output_types":["Server process (running and listening for MCP requests)","Log output (startup/shutdown messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_5","uri":"capability://safety.moderation.error.handling.and.http.status.code.mapping.to.mcp.responses","name":"error handling and http status code mapping to mcp responses","description":"Catches HTTP errors (4xx, 5xx, network timeouts, connection failures) and maps them to structured MCP error responses with descriptive messages. The server distinguishes between client errors (404 Not Found, 403 Forbidden), server errors (500 Internal Server Error), and network errors (timeout, DNS failure), returning appropriate error codes and messages that clients can interpret. This ensures fetch failures are communicated clearly without crashing the server or leaving the MCP connection in an inconsistent state.","intents":["I want my agent to know when a URL fetch fails and why (404 vs timeout vs server error)","I need to handle fetch errors gracefully without breaking the MCP connection","I want to distinguish between different failure modes (network issues vs HTTP errors) in my agent logic"],"best_for":["Robust AI agents that need to handle fetch failures and retry intelligently","Systems where fetch errors should not crash the server or disconnect the client","Applications that need detailed error information to implement fallback strategies"],"limitations":["Error messages are text-based; no structured error codes beyond HTTP status codes","No automatic retry logic — clients must implement their own retry strategies","Network errors (timeout, DNS) are caught but may not distinguish between different network failure types","SSL/TLS certificate errors are caught but not separately categorized from other network errors"],"requires":["Python 3.8+","Exception handling for urllib/requests library errors","MCP SDK error response primitives"],"input_types":["HTTP request (URL, method, headers, body)"],"output_types":["MCP error response (error code, error message, optional details)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_6","uri":"capability://tool.use.integration.request.header.customization.and.user.agent.spoofing","name":"request header customization and user-agent spoofing","description":"Allows clients to specify custom HTTP headers (including User-Agent, Authorization, Accept, etc.) in the fetch tool request, enabling access to APIs that require specific headers or authentication. The server passes these headers through to the HTTP request, allowing clients to override the default User-Agent (which might be blocked by some sites) or add authentication tokens. This flexibility enables the fetch tool to work with a wider range of web services and APIs without requiring server-side configuration changes.","intents":["I want to fetch from an API that requires a specific User-Agent or Authorization header","I need to pass authentication tokens (API keys, Bearer tokens) to protected endpoints","I want to customize HTTP headers per-request without modifying the server configuration"],"best_for":["AI agents accessing authenticated APIs or services with custom header requirements","Applications that need to work with multiple APIs with different header conventions","Systems where header customization should be per-request rather than server-wide"],"limitations":["No validation of header names or values — malformed headers may cause HTTP request failures","No protection against header injection attacks; clients can inject arbitrary headers","Sensitive headers (Authorization, API keys) are passed in plaintext through MCP messages — no encryption at MCP layer","No header filtering or allowlist — clients can set any header, including ones that break HTTP semantics (Content-Length, Host)"],"requires":["Python 3.8+","HTTP library that supports custom headers (urllib, requests)","MCP client that can pass headers in tool request"],"input_types":["Headers object (JSON object with header name/value pairs)"],"output_types":["HTTP request with custom headers applied"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_7","uri":"capability://safety.moderation.response.body.size.limiting.and.truncation","name":"response body size limiting and truncation","description":"Implements a maximum response body size limit (typically 1-10 MB) to prevent memory exhaustion from fetching extremely large files or responses. When a response exceeds the limit, the server truncates the body and returns a truncation indicator, allowing clients to know that the full content was not retrieved. This protects the server from out-of-memory errors and ensures fetch operations complete in reasonable time, though it may lose information from large documents.","intents":["I want to fetch web pages without risking memory exhaustion from huge files","I need to know when a response was truncated so I can handle it differently","I want to set reasonable limits on fetch response sizes to keep operations fast"],"best_for":["Long-running AI agents that fetch many URLs and need to prevent memory leaks","Systems with limited memory (edge devices, serverless functions) that need bounded resource usage","Applications where partial content is acceptable and truncation is preferable to failure"],"limitations":["Truncation happens at byte level, not semantic level — may cut off in the middle of words or HTML tags","No way to resume or paginate through truncated content — clients must re-fetch with different parameters","Limit is fixed at server startup; cannot be adjusted per-request","Truncated Markdown may be malformed (unclosed tags, incomplete sentences) and harder for LLMs to parse"],"requires":["Python 3.8+","HTTP library with streaming support (to avoid loading entire response into memory)","Configurable size limit (typically 1-10 MB)"],"input_types":["HTTP response body (stream)"],"output_types":["Truncated response body (string)","Truncation flag (boolean indicating if response was truncated)","Original size (optional, for client awareness)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__cap_8","uri":"capability://tool.use.integration.mcp.protocol.transport.abstraction","name":"mcp protocol transport abstraction","description":"Implements the fetch server as an MCP server that communicates with clients through the MCP protocol using a transport layer (stdio, SSE, or custom). The server uses the Python MCP SDK to handle protocol-level concerns (JSON-RPC message framing, request/response routing, error serialization) while the application code focuses on tool logic. The transport layer is abstracted away, allowing the same server code to work with different transport mechanisms without modification.","intents":["I want to build an MCP server without implementing JSON-RPC and protocol details myself","I need my fetch server to work with multiple MCP clients (Claude Desktop, custom clients, etc.)","I want to use standard MCP transport mechanisms instead of custom protocols"],"best_for":["Developers building MCP servers using the Python SDK","Teams integrating with Claude Desktop or other MCP-aware applications","Organizations standardizing on MCP for LLM tool integration"],"limitations":["Transport abstraction adds ~50-100ms latency per request due to JSON-RPC serialization","Stdio transport is synchronous; no true concurrent request handling","SSE transport requires HTTP server setup; not suitable for simple CLI tools","No built-in load balancing or request queuing across multiple server instances","Protocol version mismatches between client and server can cause silent failures"],"requires":["Python 3.9+","mcp SDK (Python) installed","MCP client that supports the transport mechanism (stdio for CLI, SSE for web)"],"input_types":["MCP JSON-RPC requests from client"],"output_types":["MCP JSON-RPC responses to client"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fetch-mcp-server__headline","uri":"capability://tool.use.integration.mcp.server.for.web.content.fetching","name":"mcp server for web content fetching","description":"An official reference server that retrieves web content, converts HTML to markdown for LLMs, and ensures compliance with robots.txt, making it ideal for developers needing to integrate web data into their applications.","intents":["best MCP server for web content","MCP server for HTML to markdown conversion","how to fetch web content for LLMs","MCP server with robots.txt support","reference server for web content extraction"],"best_for":["developers looking for web content integration"],"limitations":[],"requires":[],"input_types":["URLs"],"output_types":["markdown"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","MCP SDK for Python (mcp package)","Network connectivity to target URLs","MCP-compatible client (Claude Desktop, custom client, etc.)","html2text library or equivalent HTML parsing dependency","Valid HTML input from HTTP response","urllib.robotparser module (standard library)","Network connectivity to fetch robots.txt from target domains","robots.txt file present at target domain (optional; missing robots.txt is treated as allow-all)","MCP SDK for Python with tool decorator support"],"failure_modes":["No built-in request pooling or connection reuse — each fetch creates a new HTTP connection","Timeout handling is basic; no configurable per-request timeouts in reference implementation","No automatic retry logic for transient failures (5xx errors, network timeouts)","Single-threaded request processing — concurrent fetches are serialized through MCP message queue","Conversion quality depends on HTML structure — malformed or heavily JavaScript-rendered pages may produce suboptimal Markdown","No support for complex interactive elements (forms, embedded widgets) — these are stripped or converted to text descriptions","Images are converted to Markdown image syntax but not embedded; LLMs see only alt text and URLs","JavaScript-rendered content is not executed — only initial HTML is converted, missing dynamic content","robots.txt is advisory, not enforced by servers — compliance is voluntary and can be bypassed","No support for robots.txt extensions (crawl-delay, request-rate) beyond basic allow/disallow rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.52,"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":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=fetch-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=fetch-mcp-server"}},"signature":"QC6leP/pXl0uXs1UbK6HmtiEhVlcgtIyu3Ur8C9USTYC2uTtYsN5tWymGdCq3WBirLiauaASF7flhzIVKVYfCA==","signedAt":"2026-06-21T10:12:25.994Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fetch-mcp-server","artifact":"https://unfragile.ai/fetch-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=fetch-mcp-server","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"}}