| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes ESLint's core linting engine as an MCP resource/tool, allowing Claude and other MCP clients to invoke ESLint rules against code without spawning separate processes. Implements ESLint's plugin architecture through MCP's tool-calling interface, enabling dynamic rule configuration and multi-file linting workflows within a single MCP session.
Unique: First MCP server to expose ESLint as a native tool-calling interface, eliminating subprocess spawning and enabling stateful linting sessions within MCP's request-response model. Leverages ESLint's plugin architecture directly rather than wrapping CLI output.
vs alternatives: Faster and more composable than invoking ESLint CLI via subprocess calls because it keeps the linting engine resident in the MCP process and integrates with Claude's native tool-calling, avoiding serialization overhead and enabling multi-step linting workflows.
Automatically discovers and loads ESLint configuration files (.eslintrc.js, .eslintrc.json, .eslintrc.yml, eslintrc.config.js) from the project hierarchy, parses them, and applies rule sets and parser options to linting operations. Supports ESLint's cascading configuration model where nested .eslintrc files override parent configs, and handles environment-specific overrides (browser, node, es6, etc.).
Unique: Implements ESLint's full cascading configuration resolution algorithm within the MCP server, allowing it to respect project-specific rule sets without requiring users to re-specify configs. Handles extends chains and environment overrides automatically.
vs alternatives: More intelligent than naive CLI wrapping because it understands ESLint's configuration inheritance model and can apply the correct rule set to any file in the project without user intervention, matching the behavior of ESLint's native config resolution.
Accepts a list of file paths or glob patterns and lints them in parallel using Node.js worker threads or async I/O, returning aggregated results with per-file diagnostics. Implements ESLint's caching layer to avoid re-linting unchanged files, and supports filtering results by severity (error, warning) or rule name.
Unique: Implements parallel linting using Node.js async I/O within the MCP server's event loop, avoiding the overhead of spawning separate ESLint CLI processes. Integrates ESLint's built-in caching to skip re-analysis of unchanged files.
vs alternatives: Faster than running ESLint CLI multiple times because it keeps the linting engine warm in memory and parallelizes file processing, while still respecting ESLint's cache invalidation logic.
Invokes ESLint's built-in --fix mechanism to automatically correct violations that have fix implementations (e.g., semicolon insertion, whitespace normalization). For violations without automatic fixes, generates structured suggestions with before/after code snippets and rule documentation links, enabling Claude to propose manual fixes or ask for user confirmation.
Unique: Exposes ESLint's fix engine through MCP's tool interface, allowing Claude to apply fixes as part of a multi-turn conversation. Generates structured fix suggestions for non-auto-fixable rules by parsing rule metadata and documentation.
vs alternatives: More interactive than running ESLint --fix from the CLI because it allows Claude to preview fixes, ask for confirmation, and apply them selectively, enabling a collaborative code improvement workflow.
Exposes metadata for all loaded ESLint rules (name, description, category, fixable, deprecated status) and provides links to official documentation. Allows filtering rules by category (best-practices, errors, style, etc.) and searching by name or keyword, enabling Claude to explain rules to users and recommend relevant rules for specific code patterns.
Unique: Indexes ESLint's rule definitions at server startup and exposes them as searchable MCP resources, allowing Claude to provide in-context rule explanations without external API calls. Includes deprecated rule detection and migration guidance.
vs alternatives: More efficient than having Claude search ESLint's documentation website because rule metadata is pre-indexed and available instantly, and Claude can provide contextual explanations tailored to the user's code.
Dynamically loads ESLint plugins (e.g., eslint-plugin-react, eslint-plugin-vue) from node_modules and registers their rules with the linting engine. Supports plugin configuration options and namespace-prefixed rules (e.g., react/jsx-uses-react). Validates plugin compatibility with the current ESLint version before loading.
Unique: Implements ESLint's plugin loading mechanism within the MCP server, allowing plugins to be discovered and loaded from the project's node_modules without CLI invocation. Includes version compatibility checking.
vs alternatives: More flexible than static ESLint CLI because it allows plugins to be loaded dynamically based on project configuration, and Claude can work with framework-specific rules (React, Vue, etc.) without separate tool invocations.
Supports multiple ESLint parsers (default Espree, @babel/eslint-parser, @typescript-eslint/parser, vue-eslint-parser, etc.) and automatically selects the appropriate parser based on file extension and ESLint configuration. Handles parser options (ecmaVersion, sourceType, parserOptions) and validates parser compatibility with the target file type.
Unique: Implements ESLint's parser resolution algorithm within the MCP server, automatically selecting the correct parser for each file type based on configuration. Supports all major ESLint parsers (Espree, Babel, TypeScript, Vue, etc.).
vs alternatives: More intelligent than a generic linter because it understands ESLint's parser ecosystem and can lint TypeScript, JSX, Vue, and other non-standard JavaScript variants without user configuration, matching the behavior of ESLint CLI.
Filters linting results by severity level (error, warning, off) and aggregates statistics across multiple files. Supports custom severity mappings (e.g., treat warnings as errors) and generates summary reports with violation counts, most-violated rules, and files with the most issues. Enables Claude to provide targeted feedback based on severity thresholds.
Unique: Implements multi-dimensional filtering and aggregation on linting results, allowing Claude to provide context-aware feedback based on severity, rule category, and file impact. Supports custom severity mappings for organizational standards.
vs alternatives: More flexible than ESLint's built-in --max-warnings flag because it allows real-time filtering and aggregation, enabling Claude to tailor feedback to the user's priorities (e.g., show only errors for a quick review, or show all violations for a detailed audit).
Establishes bidirectional communication between LLM clients (Claude Desktop, VS Code Copilot, Cursor IDE) and MongoDB instances through the Model Context Protocol using either stdio or HTTP transports. The server implements a four-layer architecture separating transport handling, server orchestration, tool execution, and external service integration, enabling seamless tool invocation without custom client-side integration code.
Unique: Official MongoDB implementation of MCP with dual transport support (stdio and HTTP) and four-layer architecture that cleanly separates transport concerns from tool execution, enabling deployment flexibility without client-side code changes
vs alternatives: As the official MongoDB MCP server, it provides tighter integration with MongoDB's native APIs and Atlas infrastructure than third-party MCP implementations, with built-in support for vector search and Atlas-specific operations
Executes parameterized MongoDB find() queries against collections with support for filtering, projection, sorting, and pagination. The implementation uses the MongoDB Node.js driver's native find() API with automatic cursor management, enabling efficient streaming of large result sets through the MCP resource export mechanism to avoid protocol message size limits.
Unique: Integrates MongoDB's native cursor streaming with MCP resource export mechanism, automatically offloading large result sets to prevent protocol message size violations while maintaining transparent access patterns
vs alternatives: Handles result set size constraints more elegantly than REST API wrappers by leveraging MCP's resource URI scheme, enabling seamless access to large collections without client-side pagination logic
MongoDB MCP Server scores higher at 59/100 vs @eslint/mcp at 38/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Manages MongoDB Atlas Vector Search indexes for semantic search operations, including index creation with embedding field specifications and vector search query execution. The implementation integrates with the aggregation pipeline's $vectorSearch stage, enabling LLMs to build RAG systems that combine vector similarity search with traditional MongoDB queries.
Unique: Integrates MongoDB Atlas Vector Search index management and querying into MCP tools, enabling LLMs to autonomously build and query semantic search indexes without manual Atlas UI interactions, with full aggregation pipeline integration
vs alternatives: Provides end-to-end vector search capabilities through MCP tools, eliminating the need for separate vector database clients or custom embedding management code, enabling RAG systems built entirely through natural language prompts
Exports large query results to MCP resources (accessible via exported-data:// URIs) to circumvent protocol message size limits. The implementation stores result sets in memory or temporary storage and exposes them through MCP's resource mechanism, enabling LLMs to retrieve large datasets through separate resource access calls without overwhelming the tool response channel.
Unique: Leverages MCP's resource URI scheme to transparently handle result sets exceeding protocol message limits, enabling seamless access to large MongoDB collections without client-side pagination logic or message fragmentation
vs alternatives: Provides a cleaner abstraction for large result handling than REST API pagination by using MCP's native resource mechanism, eliminating the need for custom pagination logic in LLM prompts
Exposes server configuration and connection diagnostics through MCP resources (config:// and debug://mongodb URIs). The implementation provides current configuration with secrets redacted and last connectivity attempt information, enabling LLMs to diagnose connection issues and verify server setup without direct log access.
Unique: Provides secure configuration inspection through MCP resources with automatic secret redaction, enabling LLMs to diagnose issues without exposing sensitive credentials in tool responses
vs alternatives: Offers safer configuration debugging than direct log access by automatically redacting secrets and providing structured diagnostic information through MCP resources
Manages database and collection context across multiple tool invocations through session-based state management. The implementation maintains per-session configuration including current database and collection selections, enabling LLMs to work with multiple databases and collections without repeating context in every tool call.
Unique: Implements session-based context management that isolates database and collection selections per LLM session, enabling multi-database workflows without explicit context parameters in every tool call
vs alternatives: Reduces prompt engineering overhead by maintaining implicit context across tool calls, enabling more natural LLM interactions with MongoDB without verbose parameter passing
Implements a type-safe tool framework in TypeScript with automatic parameter validation and schema generation. The framework uses TypeScript interfaces to define tool parameters, automatically generates JSON schemas for MCP protocol compliance, and validates inputs at runtime, enabling type-safe tool development without manual schema management.
Unique: Provides a TypeScript-first tool framework that automatically generates MCP schemas from type definitions, eliminating manual schema management and enabling type-safe tool development with minimal boilerplate
vs alternatives: Reduces schema maintenance burden compared to manual JSON schema definitions by deriving schemas from TypeScript types, enabling developers to focus on tool logic rather than schema synchronization
Executes MongoDB aggregation pipelines with support for all standard stages ($match, $group, $project, $sort, etc.) and specialized stages like $vectorSearch for semantic search operations. The implementation passes pipeline definitions directly to MongoDB's aggregate() method, enabling complex multi-stage transformations and vector similarity searches on Atlas Vector Search indexes without intermediate result materialization.
Unique: Native support for $vectorSearch stage enables semantic search directly within aggregation pipelines, allowing LLMs to compose complex retrieval workflows combining vector similarity with traditional filtering and transformations in a single operation
vs alternatives: Eliminates the need for separate vector search clients or post-processing logic by embedding vector operations into MongoDB's aggregation framework, reducing latency and simplifying LLM prompt engineering for RAG systems
+7 more capabilities