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Multi-Agent Deep Researcher MCP

Multi-Agent Deep Researcher MCP

by yashavsarmal30
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

deep_research

Run an autonomous multi-agent investigation that gathers live web sources, synthesizes key insights, and produces a fully cited Markdown research report.

Instructions

Run an autonomous multi-agent deep research investigation on a topic.

Coordinates a Web Researcher, Research Analyst, and Technical Writer
to gather live web data, synthesize insights, and produce a fully cited
Markdown report.

Args:
    query: The research topic, query, or question to investigate.
    depth: Thoroughness level - 'standard' for concise, 'deep' for exhaustive.
    search_engine: Search engine to use - 'auto' (preferred), 'linkup', or 'duckduckgo'.
    model: Optional model identifier (e.g., 'gpt-4o-mini', 'llama-3.3-70b-versatile').
    provider: Optional provider ('openai', 'groq', 'anthropic', 'gemini', 'ollama').

Returns:
    The complete publication-grade Markdown research report with source citations.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "deep_research"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNodeep
modelNo
queryYes
providerNo
search_engineNoauto

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description takes on full behavioral disclosure. It transparently explains the autonomous multi-agent process, including coordination of Web Researcher, Research Analyst, and Technical Writer, and states that it gathers live web data and produces a cited Markdown report. It does not mention runtime expectations, potential costs, or side effects, but the core behavior is well disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and efficient: a clear one-sentence summary, a brief explanation of the agent workflow, a compact Args list with inline value explanations, and a Returns line. Every sentence adds value and the most important information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, no output schema, and no annotations, the description covers the essential information: purpose, workflow, all parameters, and the return value as a Markdown report. It could be more complete with explicit guidance on when to select this tool over quick_search and what resource or time implications exist, but nothing critical is missing for invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must carry the parameter documentation burden. It succeeds by explaining the query as the research topic, defining 'depth' values ('standard' vs 'deep'), listing valid search_engine options with a preferred default, and providing concrete examples for optional model and provider values. Every parameter receives meaningful semantic context beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Run an autonomous multi-agent deep research investigation on a topic.' It also differentiates itself from sibling tools like quick_search by emphasizing 'deep research' and a 'publication-grade Markdown report with source citations,' making the outcome and scope distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for thorough, cited research through phrases like 'deep research investigation' and 'exhaustive' depth, but it never explicitly explains when to use this tool over quick_search or when not to use it. No alternatives or exclusions are mentioned, leaving the agent to infer the appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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