research-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SERPER_API_KEY | No | API key for Google SERP via Serper. | |
| TAVILY_API_KEY | No | API key for Tavily search and scrape. | |
| SERPAPI_API_KEY | No | API key for Google SERP via SerpAPI. | |
| FIRECRAWL_API_KEY | No | Optional API key for Firecrawl. | |
| FIRECRAWL_API_URL | No | The base URL for the Firecrawl API (your fork). | |
| TWITTERAPI_IO_KEY | No | API key for X.com (Twitter) source. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| researchA | End-to-end: search all sources -> scrape -> rerank -> summarize. |
| search_allC | Fan out the query to every source and return deduped SearchResult dicts. |
| scrape_urlC | Scrape a single URL and return markdown content. method: "firecrawl" | "tavily" | "jina" |
| rerank_docsA | Rerank a list of document strings by relevance to the query. model: "cohere" | "jina" | "local" Returns: [{index, relevance_score}, ...] sorted desc. |
| summarize_docsB | Synthesize a final answer from a list of document strings. style: "detailed" | "bullets" | "tldr" model: "groq" | "gemini" | "openrouter" |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a distinct stage in the research pipeline (search, scrape, rerank, summarize), and `research` serves as an orchestrator that combines these steps. No two tools have overlapping core functions, so an agent can easily select the right tool for a specific need.
Most tools follow a consistent verb_noun pattern (scrape_url, rerank_docs, summarize_docs), but `research` and `search_all` deviate slightly. This is a minor inconsistency that does not hinder readability.
With 5 tools, the server is well-scoped for a research workflow. Each tool covers a necessary step in the pipeline, and the count is neither too sparse nor bloated.
The tool set provides end-to-end coverage from searching multiple sources to summarizing final answers. The individual building blocks (search_all, scrape_url, rerank_docs, summarize_docs) and the composite `research` tool leave no obvious gaps for typical research tasks.