Schema | MCP-Deep-Researcher

MCP-Deep-Researcher

Overview Schema Source Related Servers Score Discussions Admin

Server Configuration

Describes the environment variables required to run the server.

Name	Required	Description	Default
`OPENAI_API_KEY`	Yes	OpenAI API key
`TAVILY_API_KEY`	Yes	Tavily API key

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": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Tools

Functions exposed to the LLM to take actions

Name Description

Name	Description
deep_researcher_researchA	Run a multi-agent research pipeline that decomposes a query into sub-questions, searches the web in parallel via Tavily, scores source credibility, and synthesizes a comprehensive markdown report with findings, knowledge gaps, and cited sources. The pipeline has three stages: 1. Planner — breaks the query into 3-5 targeted sub-questions (GPT-4o) 2. Searcher — runs parallel Tavily web searches with credibility scoring 3. Synthesizer — produces a structured markdown report (GPT-4o) Results are cached for 24 hours. Identical (query + context + depth) combinations return instantly on subsequent calls. Args: params (ResearchInput): Validated input containing: - query (str): The research question (3-2000 chars) - search_depth (SearchDepth): 'basic' for speed or 'advanced' for depth - conversation_context (str): Prior research for multi-turn follow-ups Returns: str: Markdown research report with executive summary, key findings per sub-question, knowledge gaps, and numbered source citations with credibility tags (high/medium/unverified).

deep_researcher_researchA

Run a multi-agent research pipeline that decomposes a query into sub-questions, searches the web in parallel via Tavily, scores source credibility, and synthesizes a comprehensive markdown report with findings, knowledge gaps, and cited sources.

The pipeline has three stages:
  1. Planner — breaks the query into 3-5 targeted sub-questions (GPT-4o)
  2. Searcher — runs parallel Tavily web searches with credibility scoring
  3. Synthesizer — produces a structured markdown report (GPT-4o)

Results are cached for 24 hours. Identical (query + context + depth) combinations
return instantly on subsequent calls.

Args:
    params (ResearchInput): Validated input containing:
        - query (str): The research question (3-2000 chars)
        - search_depth (SearchDepth): 'basic' for speed or 'advanced' for depth
        - conversation_context (str): Prior research for multi-turn follow-ups

Returns:
    str: Markdown research report with executive summary, key findings per
         sub-question, knowledge gaps, and numbered source citations with
         credibility tags (high/medium/unverified).

Prompts

Interactive templates invoked by user choice

Name	Description
No prompts

Resources

Contextual data attached and managed by the client

Name	Description
No resources

Server Configuration
Instructions
Capabilities
Tools
Prompts
Resources