| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 11 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.
Executes semantic web searches using Exa's neural search index, which ranks results by semantic relevance rather than keyword matching. The server translates natural language queries into vector embeddings via the Exa API, retrieves ranked results with metadata (URL, title, snippet, publication date), and returns structured JSON with configurable result limits. This differs from traditional keyword-based search by understanding query intent and returning contextually relevant pages even without exact keyword matches.
Unique: Uses Exa's proprietary neural search index with semantic embeddings for ranking instead of BM25 keyword matching; integrates via MCP protocol allowing direct tool invocation from Claude, VS Code, and other MCP-compatible clients without custom API wrappers
vs alternatives: Provides semantic relevance ranking superior to Google Search API's keyword-based results, and integrates natively into AI workflows via MCP without requiring custom HTTP client code
Fetches complete page content from a given URL and automatically converts HTML to clean, readable text suitable for LLM consumption. The server handles DOM parsing, removes boilerplate (navigation, ads, scripts), extracts main content, and returns structured text with preserved formatting. This replaces traditional web crawling by providing AI-ready content without manual parsing or regex-based extraction.
Unique: Implements intelligent boilerplate removal and DOM-aware content extraction (not regex-based) to produce LLM-optimized text; handles encoding detection and preserves semantic structure while removing noise, integrated as a single MCP tool callable from AI assistants
vs alternatives: More reliable than Puppeteer-based crawling for static content (no browser overhead), and produces cleaner output than raw HTML parsing; faster than Readability.js implementations due to server-side optimization
Exa MCP Server scores higher at 59/100 vs clj-kondo-MCP at 24/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Manages the complete lifecycle of Exa API requests, including timeout handling, rate limit detection, and quota enforcement. The server monitors request duration, detects Exa API rate limit responses (429 status), and returns meaningful error messages to clients. This enables graceful degradation under load and prevents clients from overwhelming the Exa API with requests.
Unique: Implements request lifecycle management at the MCP server level, detecting and handling Exa API rate limits and timeouts before returning responses to clients. This enables the server to provide meaningful error messages and prevent cascading failures when the API quota is exhausted.
vs alternatives: More resilient than client-side timeout handling because the server can enforce timeouts uniformly across all clients; better error messages than raw API errors because the server translates Exa API responses into MCP-compatible error formats; enables quota management at the server level rather than requiring each client to implement its own rate limiting.
Provides fine-grained control over search parameters including domain whitelisting/blacklisting, date range filtering, content category filtering, and result type specification (news, research papers, etc.). The server accepts structured filter objects and translates them into Exa API query parameters, enabling researchers to narrow results to specific sources, time periods, or content types. This is an opt-in tool for use cases requiring precision over breadth.
Unique: Exposes Exa's full filter API through MCP tool parameters, allowing declarative specification of domain whitelists/blacklists, date ranges, and content categories without requiring direct API calls; filters are applied server-side before ranking
vs alternatives: More flexible than Google Search API's site: operator; supports simultaneous multi-domain filtering, date ranges, and category constraints in a single query rather than requiring multiple searches
Implements the Model Context Protocol (MCP) specification to expose Exa tools as callable functions within MCP-compatible clients (Claude, VS Code, Cursor, etc.). The server supports multiple transport mechanisms: stdio for local deployment, HTTP/SSE for hosted endpoints, and serverless functions (Vercel). A single codebase (src/mcp-handler.ts) defines tool schemas and logic; deployment-specific entry points (src/index.ts for Smithery, api/mcp.ts for Vercel) adapt the core to different transports without code duplication.
Unique: Abstracts transport layer from tool logic via separate entry points (stdio vs HTTP/SSE vs serverless); uses Smithery framework for configuration schema and dynamic tool registration, enabling single-codebase deployment across stdio, hosted HTTP, and Vercel serverless without conditional logic
vs alternatives: Eliminates need for custom HTTP wrappers or plugin development; MCP standardization allows same tool to work across Claude, VS Code, Cursor, and future MCP clients without modification
Allows runtime configuration of which tools are exposed and how they behave through a configSchema defined in src/index.ts. The initializeMcpServer function in src/mcp-handler.ts reads configuration and conditionally registers tools (web_search_exa, web_fetch_exa, web_search_advanced_exa) with their respective input schemas. This enables deployment-time tool selection without code changes, supporting different use cases (e.g., disabling advanced search for free tier users).
Unique: Uses Smithery's configSchema pattern to define tool availability at deployment time; initializeMcpServer conditionally registers tools based on config, avoiding hardcoded tool lists and enabling tiered feature access without code branching
vs alternatives: More flexible than static tool registration; supports multi-tenant scenarios where different customers see different tool sets, and enables A/B testing of tool availability without code changes
Defines strict TypeScript interfaces in src/types.ts for all Exa API requests and responses, ensuring compile-time type safety and runtime validation. The server validates incoming MCP tool parameters against these types before forwarding to Exa API, and validates Exa responses before returning to clients. This prevents type mismatches, malformed requests, and unexpected response structures from propagating to downstream consumers.
Unique: Implements bidirectional validation: validates MCP input parameters against Exa request types before API call, and validates Exa responses against response types before returning to client; uses TypeScript interfaces as single source of truth for API contracts
vs alternatives: Stricter than runtime-only validation (e.g., Zod); catches type errors at compile time and provides IDE autocomplete, reducing debugging time and preventing invalid requests from reaching Exa API
Implements comprehensive error handling across the request lifecycle: validates input parameters, catches Exa API errors, handles network timeouts, and returns structured error responses to MCP clients. The server logs errors with context (request ID, tool name, parameters) to aid debugging. Error responses follow MCP error format, allowing clients to distinguish between invalid input, API failures, and transient errors.
Unique: Implements error handling at multiple layers: input validation, API call execution, response parsing; returns structured MCP error responses that distinguish between client errors (invalid input), server errors (API failures), and transient errors (timeouts)
vs alternatives: More granular than generic HTTP error handling; MCP error format allows clients to implement intelligent retry logic based on error type, and structured logging enables faster root cause analysis
+3 more capabilities