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Galbaz1

video-research-mcp

by Galbaz1

Research Document

research_document
Read-only

Conduct multi-phase deep research on PDFs and local documents using a research question: extract evidence, cross-reference sources, and synthesize cited findings.

Instructions

Run multi-phase deep research grounded in source documents.

Phases: Document Mapping -> Evidence Extraction -> Cross-Reference -> Synthesis. Every claim is labeled with evidence tiers and cited back to source documents.

Input Schema

NameRequiredDescriptionDefault
urlsNoURLs to PDF documents (downloaded and uploaded to Gemini)
scopeNoResearch depth -- quick, moderate, deep, comprehensive.moderate
file_pathsNoLocal PDF/document file paths
instructionYesResearch question or analysis instruction for the documents
thinking_levelNoGemini thinking depth.high

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.6.1
    • addedInput schema / properties / scope / description
      Added value: +"Research depth -- quick, moderate, deep, comprehensive."
    • addedInput schema / properties / thinking_level / description
      Added value: +"Gemini thinking depth."
  2. First observedv0.6.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds real behavioral value beyond that: a concrete four-phase pipeline (Document Mapping -> Evidence Extraction -> Cross-Reference -> Synthesis) and the guarantee that claims carry evidence tiers and citations.

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?

Two compact sentences with the core action front-loaded, followed by the phase pipeline and output guarantee. No filler; every line contributes.

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?

An output schema exists, so return values need not be explained, and annotations plus full schema coverage carry safety and parameters. Behavior is well described; the only real gap is usage routing against sibling research tools.

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

Parameters3/5

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

Schema description coverage is 100%, so the five parameters (urls, file_paths, instruction, scope, thinking_level) are already documented in the schema. The description adds no syntax, format, or interaction detail for them, so baseline 3 applies.

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

Purpose4/5

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

States a specific verb and resource ('Run multi-phase deep research grounded in source documents') and clarifies the domain as document-grounded research. However, it does not distinguish itself from close siblings like research_deep or research_execute, leaving the agent to infer the boundary.

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

Usage Guidelines2/5

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

No explicit when-to-use, when-not-to-use, or alternative routing is given. 'Grounded in source documents' implicitly contrasts with research_web, but the agent must infer whether to pick this over research_deep, research_execute, or research_plan.

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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