research-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@research-mcpResearch the latest trends in AI agents with sources."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
research-mcp
One MCP server + OpenAI plugin that searches 9 sources, scrapes full content, reranks, and synthesizes a cited answer with a free LLM.
Sources (9)
Source | Default? | Auth | Notes |
Firecrawl (your fork) | ✅ |
| Calls |
GitHub Trending | ✅ | none | HTML scrape, no key |
arXiv | ✅ | none | Official |
Tavily | ✅ |
| Primary fallback scraper, 1k/mo free |
OSS Insight | ✅ | none | Trending repos, free API |
dev.to | ✅ | none | Public API |
libhunt | ❌ opt-in | none | HTML scrape |
X.com (Twitter) | ❌ opt-in |
| No free public X API |
Google SERP | ❌ opt-in |
| No free public SERP API |
Related MCP server: evo-scry
Free LLM & reranker
Summarizer: Groq (
llama-3.3-70b-versatile, free) → Gemini 2.0 Flash → OpenRouter free → extractive fallbackReranker: Cohere
rerank-english-v3.0(1k/mo free) → Jinav2→ local term-overlapScraper: Firecrawl → Tavily extract → Jina Reader (
r.jina.ai, no key)
Install
cd research-mcp
pip install -e .
cp .env.example .env
# fill in whichever keys you have; the rest are optionalRun the MCP (stdio, for Claude Desktop / goose)
python -m mcp_serverAdd to your Claude Desktop config:
{
"mcpServers": {
"research": {
"command": "python",
"args": ["-m", "mcp_server"],
"cwd": "C:\\Users\\kevin\\research-mcp"
}
}
}Run the OpenAI plugin shim (HTTPS)
python -m openai_plugin.app
# then serve over HTTPS via Caddy/Nginx and point ChatGPT at
# https://your.host/.well-known/openai-plugin.jsonTools exposed
Tool | Purpose |
| End-to-end: search → scrape → rerank → summarize |
| Search only, no LLM |
| Single URL scrape |
| Rerank a list of strings |
| Final synthesis |
Smoke test (no keys required)
python tests/smoke.pyRuns arxiv → local rerank → extractive summary. Proves the pipeline is wired correctly without spending any free-tier quota.
Docker
docker compose --profile mcp up --build # MCP over stdio
docker compose --profile plugin up --build # OpenAI plugin on :8000Layout
mcp_server/
server.py # fastmcp entry
config.py # env-driven settings
adapters/ # one file per source, normalized SearchResult
tools/
search.py # fan-out + dedupe
scrape.py # Firecrawl / Tavily / Jina
rerank.py # Cohere / Jina / local
summarize.py # Groq / Gemini / OpenRouter
openai_plugin/
app.py # FastAPI shim re-exporting the same tools
manifest.json
tests/smoke.py
Dockerfile, docker-compose.ymlMaintenance
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