"Adding functionality to large language models" matching MCP servers:

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

A self-hosted MCP server that provides capability packs for AI agents to perform browser, desktop, git, and code actions via schema-validated JSON calls, enabling reliable agentic workflows even with small open-weight models.

Description: Introduce Beelzebub, an MCP‑based honeypot framework that enables creating decoy tools to detect prompt injection and malicious agent behavior. Motivation: Strengthen the security of LLM workflows by adding a non‑intrusive detection mechanism.

Simple MCP Runner makes it effortless to safely expose system commands to language models via a lightweight MCP server—all configurable with a clean, minimal YAML file and zero boilerplate.

KSM MCP is a Model Context Protocol (MCP) server that acts as a secure intermediary between AI language models (like Claude) and Keeper Secrets Manager (KSM). It allows AI agents to manage your KSM secrets-such as listing, creating, retrieving, and deleting records and folders-while protecting your

Enables coding agents to natively invoke Java project scaffolding operations such as initializing projects, adding modules, designing systems, and suggesting architectures.

LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.

Enables AI assistants to manage Sable AI voice agents, journeys, knowledge bases, deployments, and analytics through natural language, plus observability via HyperDX.

An MCP server that enables language models to design real KiCad electronic schematics from plain-language descriptions, placing parts, wiring nets, and running KiCad's electrical rules check to produce editable .kicad_sch files.

Model Context Protocol (MCP) server for the Royal Spanish Academy (RAE) API. It allows language models to interact with RAE's dictionary and linguistic resources.

Enables AI assistants to contribute to Google Cloud Platform's k8s-config-connector by adding fields to resources, migrating from Terraform to direct controller architecture, and creating properly attributed commits.

Records your terminal sessions per command (PTY + OSC 133) into local SQLite, so AI agents can search, retrieve, and diff what commands actually printed. Secret redaction is applied by default to everything served over MCP.

Model Context Protocol server for the NetLoc8 IP geolocation API. Gives AI assistants access to country/city lookup, timezone reconciliation, and IP utilities.

PCI DSS v4.0.1 compliance scanner for Go payment services, delivered as an MCP server. Every finding maps to a specific PCI DSS requirement ID. Taint-aware cardholder data flow analysis with PCI SSC FAQ semantics, designed to complement broad SAST tools like Semgrep and CodeQL - not replace them.

Turn any OpenAPI/Swagger/Postman spec into an MCP server — give your LLM live access to any API.

MCP server for a self-hostable personal planner — exposes memo, todo, calendar event, and cross-resource full-text search tools so Claude, Cursor, or any MCP client can manage your schedule and notes in natural language.

Your agents, briefed. Not flooded. Self-hosted context compiler: git-backed team knowledge served to coding agents as token-budgeted bundles over MCP.

An MCP server providing web search, image search, and page scraping tools to LLMs without requiring API keys.

Enables AI assistants and local LLMs to interact with BirdNET-Go bioacoustic observatories, providing tools to query recent and historical bird detections, monitor station health, and retrieve audio clips.

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.