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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes clj-kondo linting capabilities through the Model Context Protocol (MCP), allowing AI models and tools to invoke static analysis on Clojure code without direct subprocess management. Implements MCP server transport layer that wraps clj-kondo's analysis engine, translating linting results into structured JSON responses that conform to MCP resource and tool schemas for seamless integration with Claude, other LLMs, and MCP-compatible clients.
Unique: Bridges clj-kondo (a mature Clojure linter) into the MCP ecosystem, enabling AI models to invoke linting as a first-class tool without subprocess management boilerplate. Uses MCP's resource and tool schemas to expose linting as callable functions rather than requiring models to parse raw CLI output.
vs alternatives: Provides standardized MCP integration for Clojure linting, whereas direct clj-kondo CLI usage requires models to handle subprocess spawning and output parsing, and existing Clojure IDE plugins are editor-specific rather than AI-model-agnostic.
Performs on-demand static analysis of Clojure code to detect syntax errors, style violations, and common mistakes using clj-kondo's rule engine. Parses Clojure source text, applies configurable linting rules (unused variables, incorrect function arity, deprecated APIs, etc.), and returns diagnostics with precise line/column positions and severity levels (error, warning, info). Configuration is read from .clj-kondo/config.edn if present, allowing per-project customization.
Unique: Exposes clj-kondo's mature rule engine (covering 100+ linting rules) through MCP, enabling AI models to validate Clojure code with the same rigor as IDE plugins, but in a model-agnostic, protocol-standardized way. Respects project-level .clj-kondo/config.edn for rule customization.
vs alternatives: More comprehensive than regex-based linting and more accessible than requiring IDE integration; clj-kondo itself is the de-facto Clojure linter, so this MCP wrapper provides the industry standard in an AI-friendly format.
Registers clj-kondo linting as a callable MCP tool with a defined JSON schema, allowing MCP clients (like Claude) to discover, invoke, and handle linting requests as first-class tool calls. Implements MCP's tools/list and tools/call handlers, translating tool invocation parameters (code text, file paths) into clj-kondo subprocess calls and marshaling results back as structured JSON responses. Enables natural language requests like 'lint this code' to be routed to the linting engine without explicit model prompting.
Unique: Implements MCP's tools/list and tools/call protocol handlers to expose clj-kondo as a discoverable, invokable tool. Uses JSON schema to describe tool parameters, enabling clients to understand and invoke linting without hardcoded knowledge of clj-kondo's CLI interface.
vs alternatives: Standardizes linting as an MCP tool, making it discoverable and callable by any MCP client; direct clj-kondo CLI usage requires models to know the exact invocation syntax, whereas MCP schema-based discovery is self-documenting and client-agnostic.
Respects project-level .clj-kondo/config.edn configuration files to customize which linting rules are enabled, disabled, or configured with specific parameters. Reads configuration from the project directory, merges it with clj-kondo's defaults, and applies the resulting rule set during analysis. Supports rule-level configuration such as severity overrides, exclusion patterns, and rule-specific options (e.g., max function arity warnings).
Unique: Leverages clj-kondo's native configuration system (.clj-kondo/config.edn) to allow per-project rule customization without modifying the MCP server. Configuration is read at linting time, enabling teams to enforce project-specific standards.
vs alternatives: Provides configuration flexibility comparable to IDE-based linting, whereas hardcoded linting rules would require server code changes to customize; respects the Clojure ecosystem's standard configuration format.
Accepts file paths or directory paths as input and performs linting on multiple Clojure files in a single MCP call. Recursively traverses directories, identifies .clj, .cljs, and .cljc files, and returns aggregated diagnostics for all files with file-level grouping. Enables efficient bulk analysis of codebases without requiring separate tool calls per file.
Unique: Wraps clj-kondo's batch analysis capability in MCP, allowing single tool calls to lint entire directories. Aggregates results with file-level grouping, enabling efficient codebase-wide analysis without per-file MCP overhead.
vs alternatives: More efficient than invoking linting separately for each file; provides codebase-wide analysis in a single MCP call, reducing latency and simplifying client logic compared to manual file enumeration and sequential linting.
Returns linting results as structured JSON with detailed diagnostic objects including file path, line number, column number, rule name, message, and severity level (error, warning, info). Each diagnostic is a discrete object with all metadata needed for programmatic handling, enabling clients to filter, sort, or aggregate violations by severity, rule type, or file. Severity levels align with LSP (Language Server Protocol) conventions for compatibility with IDE tooling.
Unique: Exposes clj-kondo's diagnostic output as structured JSON with LSP-compatible severity levels, enabling programmatic filtering and aggregation. Each diagnostic includes full metadata (file, line, column, rule name, message) for rich client-side handling.
vs alternatives: More structured than raw CLI output; JSON format enables easy parsing and filtering, whereas plain-text linting output requires regex parsing and is fragile to format changes.
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 clj-kondo-MCP at 24/100.
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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