"How to send queries to an Elasticsearch cluster" matching MCP servers:

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.

Enables AI assistants to read and modify Figma designs programmatically through the Model Context Protocol.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

An MCP server that enables AI assistants to perform web searches on Perplexity.ai using browser automation instead of an official API. It supports persistent authenticated sessions and returns search results along with cited sources directly to the client.

AI job search and job application tracker. Search live jobs by title, location, salary, date posted, and remote or on-site, and see each one checked on real data: pay against market rates, ghost-job signals, visa sponsorship history, and how well it matches your resume. Check how hiring software reads your resume, track applications, set follow-up reminders, and get email alerts.

Enables AI agents to send HTTP requests to any endpoint with full control over methods, headers, query parameters, and request bodies.

A universal MCP server enabling AI assistants to query and manage six database engines (Postgres, Redis, Elasticsearch, MySQL, MongoDB, LDAP) through 113+ tools with read-only safety and fault tolerance.

Provides complete access to Model Context Protocol documentation through an MCP server, allowing LLMs to query specific sections like 'Tools', 'Resources', and 'Authorization' to retrieve comprehensive protocol specifications.

Enables users to send text to an echo tool and receive the same text back, running locally with no telemetry.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

A local-first, no-API-key MCP server that enables LLMs to search the web, fetch pages, and read documents using multiple engines and smart fallbacks.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Local MCP server for generating image assets with Google Gemini (Nano Banana 2 / Pro). Supports transparent PNG/WebP output, exact resizing/cropping, up to 14 reference images, and Google Search grounding.

An offline MCP server for SAP ABAP that provides static analysis, ABAP Cloud readiness checks, and RAP scaffolding using abaplint, enabling AI agents to analyze and generate ABAP code without a live SAP system.

Enables LLMs to interact with the AT Protocol ecosystem, including Bluesky, through natural language. Supports public data access without authentication and full write operations with authentication.

Enables AI assistants to read, search, and traverse your entire reMarkable library, including handwritten notes via OCR.