"Overview of Command Line Interface Usage" matching MCP servers:

Enables fetching Google's AI Overview answers and cited sources as structured JSON, with support for batch queries and country/language targeting.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

Provides search and retrieval over a local database of Pathfinder 2e rules. Enables Claude Code to answer rules questions with cited, accurate answers from fetched entries.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

Enables AI agents to search and retrieve historical Spanish Call of Duty gaming community posts, with free search previews and pay-per-page citation-ready access via x402.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Provides LLM-free search tools across Confluence, JIRA, code, and project knowledge for any MCP client.

A command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.

Enables LLM agents to query and analyze hh.ru job vacancies via full-text and semantic search, with tools for vacancy lookup, skill statistics, market overview, and candidate profile comparison.

Enables efficient AI workflow orchestration by chaining multi-step LLM operations while keeping intermediate results out of the context window, reducing token usage by 90%+ and supporting multiple AI providers.

Unified MCP orchestration layer that consolidates multiple MCPs into a single interface with semantic tool discovery, code-mode execution, scheduling, and intelligent caching to reduce token usage by 97% and eliminate choice paralysis.

Enables AI coding assistants to perform local semantic code search using AST parsing and embeddings, finding code by meaning with exact file and line locations, plus AST-based usage lookup.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

heroicons-mcp is a Model Context Protocol (MCP) server that exposes Heroicons. It allows LLMs and agentic applications to search, list, and generate usage examples of icons in React applications.

Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.

Enables querying Langfuse analytics, cost metrics, and usage data across multiple projects. Provides tools for trace analysis, model/service cost breakdowns, and daily usage trends through natural language queries.

Integrates Dify's external knowledge base API with the Model Context Protocol to enable AI agents to retrieve and query relevant information. It supports relevance scoring, metadata filtering, and flexible configuration through environment variables or command-line arguments.