"Using another LLM for research purposes" matching MCP servers:

Enables AI assistants to query hundreds of models across OpenAI, Google, and OpenRouter, with model discovery, deep-research jobs, and image generation.

Enables fetching weather forecasts for the USA via NWS API and for Israel via browser automation using Playwright.

An MCP server that enables AI assistants to interact with self-hosted Planka boards through the Planka REST API. It allows users to list, search, create, and update projects, boards, and cards using natural language.

Enables AI-powered research by breaking a topic into subtopics, gathering information via agents, and compiling a report. Integrates with LangGraph and RAG for orchestration and contextual retrieval.

An MCP server that lets multiple agent sessions (e.g., Claude Code, Claude Desktop) on the same machine communicate over shared channels like walkie-talkies, enabling them to exchange messages and coordinate tasks without any central broker.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

MCP server for web research using anti-detect Camoufox browser. Enables search, page reading (including JS/SPA), batch fetching, clicking, typing, and live browsing sessions with caching and retries.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

A tool that helps users conduct comprehensive research on complex topics by exploring questions in depth, finding relevant sources, and generating structured, well-cited research reports.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

A batteries-included MCP server providing common coding-agent tools like web search, page fetching, file system access, shell command execution, and Git integration, built in Python with a local-first, Unix-style philosophy.

Enables deep research tasks using a multi-agent architecture that integrates any LLM and MCP tools. Available via MCP stdio, streamable HTTP, and SSE transports.

Enables cost-aware LLM routing over MCP with verification-based cascading, offering tools for routing queries, resuming escalated approvals, estimating costs, comparing policies, and explaining routing decisions, plus read-only resources and a policy-selection prompt.

MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.

A local-first LLM routing MCP server that keeps sensitive data on your own models, with fail-closed privacy and manager-worker delegation, exposing route and complete tools to any MCP client.

MCP server for local, source-agnostic research, turning briefs into platform-specific searches and cited evidence dossiers with PostgreSQL storage and optional browser capture.

Provides a private set of high-level tools for web research, focused URL investigation, read-only GitHub research, and persistent semantic memory via a Streamable HTTP MCP gateway.

Provides real-time LLM pricing and availability data as an MCP server, enabling AI agents to make optimal model routing decisions at inference time with cited pricing sources.