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Implements MCP resource handlers that translate client requests into safe filesystem operations with path validation and permission checks, enabling AI agents to inspect codebases, configuration files, and documentation on the host machine.","intents":["I want my LLM agent to read specific files from my project directory to understand the codebase structure","I need Claude or another AI to examine configuration files, logs, or documentation without giving it direct shell access","I'm building an agent that needs to analyze multiple files across a directory tree to make decisions"],"best_for":["AI agent developers building code analysis or documentation tools","Teams deploying LLM-powered development assistants with sandboxed filesystem access","Solo developers prototyping AI-assisted refactoring or code review agents"],"limitations":["Read-only by default — no write operations without explicit capability configuration","Performance degrades with very large files (>100MB) as entire content is loaded into context","No built-in caching of frequently-accessed files — each request re-reads from disk","Symlink handling depends on OS permissions; may fail on restricted filesystems"],"requires":["Node.js 18+","MCP client implementation (Claude Desktop, custom LLM application, or MCP SDK)","Read permissions on target filesystem paths","@modelcontextprotocol/sdk package for server implementation"],"input_types":["file paths (string)","directory paths (string)","glob patterns (optional, if supported)"],"output_types":["file contents (string/binary)","directory listings (JSON with metadata)","file metadata (size, modified time, permissions)"],"categories":["tool-use-integration","filesystem-access"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_1","uri":"capability://tool.use.integration.mcp.resource.discovery.and.listing","name":"mcp-resource-discovery-and-listing","description":"Implements MCP resource discovery endpoints that allow clients to enumerate available files and directories, including metadata like file size, modification time, and MIME type. Uses the MCP resource listing protocol to expose filesystem structure as queryable resources with optional filtering and pagination, enabling clients to understand what files are accessible before requesting specific content.","intents":["I want my AI agent to discover what files exist in a project before deciding which ones to read","I need to list all files matching a pattern (e.g., all .py files) so the agent can prioritize analysis","I'm building a file browser UI that shows available files the LLM can access"],"best_for":["AI agents that need to explore unknown codebases dynamically","Interactive tools building file selection interfaces for LLM context","Multi-file analysis workflows that benefit from upfront directory scanning"],"limitations":["Large directory trees (>10k files) may cause slow enumeration and high memory usage","No built-in recursive depth limiting — deep nested structures require explicit path filtering","Metadata freshness not guaranteed — relies on OS filesystem cache, may be stale if files change during enumeration"],"requires":["MCP client with resource listing support","Filesystem read permissions on target directories","Node.js 18+"],"input_types":["directory paths (string)","optional filter patterns (glob or regex)"],"output_types":["resource list (JSON array with file metadata)","MIME types (string)","file sizes (bytes)","modification timestamps (ISO 8601)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_2","uri":"capability://safety.moderation.path.validation.and.sandboxing","name":"path-validation-and-sandboxing","description":"Enforces path validation rules to prevent directory traversal attacks and unauthorized access to files outside configured root directories. Implements path normalization (resolving `..` and symlinks), allowlist/denylist filtering, and permission checks before serving any filesystem operation, ensuring that LLM clients cannot escape the intended sandbox or access sensitive system files.","intents":["I want to safely expose only my project directory to an AI agent without risk of it accessing /etc or home directory files","I need to prevent path traversal attacks if the LLM tries to request ../../../etc/passwd","I'm deploying a shared MCP server and need to isolate different users' filesystem access"],"best_for":["Production deployments of MCP servers handling untrusted or multi-tenant LLM clients","Security-conscious teams integrating AI agents into development workflows","Organizations with compliance requirements (SOC 2, HIPAA) needing filesystem access controls"],"limitations":["Symlink resolution may leak information about filesystem structure outside the sandbox","Configuration errors (overly permissive allowlists) can accidentally expose sensitive paths","No runtime monitoring or audit logging of access attempts — requires external logging integration","Windows and Unix path handling differs; cross-platform deployments need careful configuration"],"requires":["MCP server configuration with root directory and access rules defined","Node.js 18+","Proper filesystem permissions on the host OS"],"input_types":["file paths (string, potentially malicious)","access control configuration (JSON)"],"output_types":["validation result (boolean: allowed/denied)","normalized path (string, if allowed)","error message (string, if denied)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_3","uri":"capability://tool.use.integration.mcp.tool.function.calling.for.filesystem.operations","name":"mcp-tool-function-calling-for-filesystem-operations","description":"Exposes filesystem operations as MCP tools with structured schemas, allowing LLM clients to invoke read, list, and metadata operations through the MCP tool-calling protocol. Implements request/response marshaling that converts LLM tool calls into filesystem operations and returns results in a format the LLM can parse and reason about, enabling natural language requests like 'read the main.py file' to be translated into filesystem calls.","intents":["I want my LLM to be able to call 'read_file' as a tool when it decides it needs to examine code","I need the LLM to list directory contents and then decide which files to read based on the results","I'm building an agentic workflow where the LLM iteratively explores the filesystem to understand a project"],"best_for":["Agentic AI systems that need to autonomously explore and analyze codebases","Interactive development tools where LLMs make decisions about which files to examine","Multi-step workflows combining filesystem exploration with code analysis or generation"],"limitations":["Tool schemas must be predefined — LLMs cannot dynamically discover new filesystem operations at runtime","Large file reads may exceed LLM context windows, requiring client-side chunking or summarization","No built-in retry logic for transient filesystem errors (permission denied, file locked)","Tool call latency includes filesystem I/O — slow for high-frequency small reads"],"requires":["MCP client with tool-calling support (Claude, custom LLM application)","Tool schemas defined in MCP server configuration","Node.js 18+","@modelcontextprotocol/sdk"],"input_types":["tool call requests (JSON with file paths and parameters)","LLM-generated tool invocations"],"output_types":["tool results (JSON with file contents, directory listings, or metadata)","error responses (JSON with error messages)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_4","uri":"capability://tool.use.integration.streaming.large.file.content.delivery","name":"streaming-large-file-content-delivery","description":"Supports streaming large file contents through the MCP protocol to avoid loading entire files into memory or LLM context at once. Implements chunked reading and optional compression to efficiently deliver large files (>10MB) without overwhelming the client or exceeding context limits, enabling analysis of large codebases or log files that would otherwise be impractical.","intents":["I need to analyze a large log file (500MB+) without loading it all into the LLM's context","I want to read a large codebase file-by-file, streaming each file to the LLM as it's processed","I'm building a tool that needs to handle large files efficiently without memory spikes"],"best_for":["Large-scale codebase analysis where files exceed typical LLM context windows","Log analysis and debugging workflows with multi-gigabyte log files","Memory-constrained environments (edge devices, serverless functions) processing large files"],"limitations":["Streaming adds complexity to client implementation — requires handling partial results and reassembly","LLMs cannot reason about file content they haven't fully received — may require summarization or chunking strategies","Compression overhead may not justify benefits for already-compressed formats (images, videos, archives)","Network latency becomes significant factor for very large files over slow connections"],"requires":["MCP client with streaming support","Node.js 18+","Sufficient network bandwidth for large file transfers"],"input_types":["file paths (string)","optional chunk size parameters (bytes)"],"output_types":["file content chunks (string or binary)","optional compression format (gzip, brotli)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_5","uri":"capability://tool.use.integration.file.metadata.and.attribute.inspection","name":"file-metadata-and-attribute-inspection","description":"Provides detailed file metadata (size, modification time, permissions, ownership, MIME type) through MCP resources, allowing clients to make informed decisions about which files to read or how to process them. Implements metadata caching and lazy evaluation to avoid expensive stat() calls for every file, enabling efficient filtering and prioritization of large directory trees.","intents":["I want the LLM to check file sizes before reading to avoid loading huge binaries into context","I need to find recently modified files in a project to focus analysis on recent changes","I'm building a tool that prioritizes reading small text files over large binary files"],"best_for":["Intelligent file selection workflows that filter based on size, type, or modification time","Codebase analysis tools that need to skip binary files and focus on source code","Change detection workflows that identify recently modified files for incremental analysis"],"limitations":["Metadata can become stale if files are modified during analysis — no real-time invalidation","MIME type detection relies on file extensions or magic bytes — may be inaccurate for unusual formats","Permission metadata is OS-specific (Unix vs Windows) — cross-platform tools need careful handling","Metadata caching adds complexity and potential consistency issues in multi-process environments"],"requires":["Node.js 18+","Read permissions on target files","MCP client support for metadata in resource listings"],"input_types":["file paths (string)"],"output_types":["file size (bytes)","modification time (ISO 8601 timestamp)","MIME type (string)","permissions (octal or symbolic)","ownership (uid/gid or username)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_6","uri":"capability://tool.use.integration.mcp.protocol.error.handling.and.reporting","name":"mcp-protocol-error-handling-and-reporting","description":"Implements comprehensive error handling for filesystem operations with MCP-compliant error responses, translating OS-level errors (permission denied, file not found, I/O errors) into structured error messages that LLM clients can understand and act upon. Provides detailed error context (error codes, descriptions, suggested remedies) to enable intelligent error recovery and user feedback.","intents":["I want the LLM to understand when a file doesn't exist and try an alternative path","I need clear error messages when permission is denied so the agent can report the issue","I'm building a tool that gracefully handles filesystem errors without crashing"],"best_for":["Robust agentic systems that need to handle filesystem errors gracefully","Interactive tools providing user-friendly error messages for filesystem issues","Production deployments requiring detailed error logging and monitoring"],"limitations":["Error messages may leak filesystem structure information (e.g., 'file not found' reveals path existence)","Some OS-level errors don't map cleanly to MCP error codes — may require custom error types","Error recovery strategies depend on LLM reasoning — not all errors can be automatically remedied","No built-in retry logic — clients must implement their own retry strategies"],"requires":["MCP client with error handling support","Node.js 18+","@modelcontextprotocol/sdk"],"input_types":["filesystem operation requests (file paths, operations)"],"output_types":["error responses (JSON with error code, message, and context)","structured error objects (MCP error format)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-modelcontextprotocol-server-filesystem__cap_7","uri":"capability://automation.workflow.mcp.server.lifecycle.and.configuration.management","name":"mcp-server-lifecycle-and-configuration-management","description":"Manages MCP server initialization, configuration loading, and graceful shutdown, implementing standard MCP server patterns for capability negotiation and protocol versioning. Handles configuration of root directories, access rules, and resource schemas at startup, with support for environment variables and configuration files to enable flexible deployment across different environments.","intents":["I want to configure which directories the MCP server can access via environment variables","I need to start and stop the filesystem MCP server as part of my application lifecycle","I'm deploying the MCP server in different environments (dev, staging, prod) with different access rules"],"best_for":["Developers integrating the filesystem MCP server into larger applications","DevOps teams deploying MCP servers in containerized or serverless environments","Multi-environment deployments requiring configuration management"],"limitations":["Configuration changes require server restart — no hot-reload support","No built-in configuration validation — invalid configs may fail silently or at runtime","Environment variable handling is OS-specific — Windows and Unix differ in path separators and escaping","No built-in health checks or readiness probes for Kubernetes deployments"],"requires":["Node.js 18+","@modelcontextprotocol/sdk","Ability to set environment variables or provide configuration files"],"input_types":["configuration objects (JSON)","environment variables (string)","configuration files (JSON or YAML)"],"output_types":["server instance (running MCP server)","initialization status (success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","MCP client implementation (Claude Desktop, custom LLM application, or MCP SDK)","Read permissions on target filesystem paths","@modelcontextprotocol/sdk package for server implementation","MCP client with resource listing support","Filesystem read permissions on target directories","MCP server configuration with root directory and access rules defined","Proper filesystem permissions on the host OS","MCP client with tool-calling support (Claude, custom LLM application)","Tool schemas defined in MCP server configuration"],"failure_modes":["Read-only by default — no write operations without explicit capability configuration","Performance degrades with very large files (>100MB) as entire content is loaded into context","No built-in caching of frequently-accessed files — each request re-reads from disk","Symlink handling depends on OS permissions; may fail on restricted filesystems","Large directory trees (>10k files) may cause slow enumeration and high memory usage","No built-in recursive depth limiting — deep nested structures require explicit path filtering","Metadata freshness not guaranteed — relies on OS filesystem cache, may be stale if files change during enumeration","Symlink resolution may leak information about filesystem structure outside the sandbox","Configuration errors (overly permissive allowlists) can accidentally expose sensitive paths","No runtime monitoring or audit logging of access attempts — requires external logging integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7661123449825045,"quality":0.26,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.471Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":338479,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=modelcontextprotocol-server-filesystem","compare_url":"https://unfragile.ai/compare?artifact=modelcontextprotocol-server-filesystem"}},"signature":"Z/QWtIr7k54F9s0hyanQc+eRH5yhF9OX0Y+a2lL6pM+SvtjuPKqVB56mb6gMqvW1OxMg90BuPsL4f6RX6gpjBA==","signedAt":"2026-06-20T14:27:08.839Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/modelcontextprotocol-server-filesystem","artifact":"https://unfragile.ai/modelcontextprotocol-server-filesystem","verify":"https://unfragile.ai/api/v1/verify?slug=modelcontextprotocol-server-filesystem","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"}}