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Galbaz1

video-research-mcp

by Galbaz1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Research Web

research_web

Launch an autonomous Gemini Deep Research agent to plan web searches, read sources, and produce a cited markdown report; poll its background status as it runs.

Instructions

Launch a Gemini Deep Research Agent for autonomous web-grounded research.

The agent plans its own research, searches the web (~80-160 queries), reads sources, and produces a cited markdown report. Runs in background; poll with research_web_status. Provider usage is billed; runtime varies.

Input Schema

NameRequiredDescriptionDefault
topicYesPrecise research brief — the more detailed, the better results
job_idNoResume a recorded launch without resubmitting provider work
output_formatNoReport structure/format instructions (e.g. 'executive summary + data tables')

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.7.1
    • addedInput schema / properties / job_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Resume a recorded launch without resubmitting provider work"
+}
  2. First observedv0.6.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare openWorldHint=true and readOnlyHint=false, so the safety profile is partly covered. The description adds genuinely useful behavior beyond that: autonomous planning, ~80-160 queries, background execution, provider billing, and variable runtime. It stops short of stating auth requirements or whether a launch is idempotent/reversible.

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?

Three tight sentences, each earning its place: what it is, how it runs and how to track it, and the cost/latency caveat. The scoping and polling information is front-loaded.

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

Completeness5/5

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

An output schema exists, so return-value detail is not needed. For an async launch tool the description covers the essentials an agent needs: autonomy model, background execution, the polling sibling, and billing. Nothing critical is missing.

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 all three parameters (topic, job_id, output_format) are already documented in the schema with meaningful hints. The description adds no parameter-level detail (e.g. job_id resume semantics), so the baseline 3 applies.

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?

States a specific verb (Launch) and resource (Gemini Deep Research Agent) plus the concrete output (cited markdown report). It distinguishes itself from siblings by naming research_web_status as the polling companion, so an agent can route correctly without opening other schemas.

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

Usage Guidelines4/5

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

Gives a clear operational condition: it runs in background, so poll with research_web_status. It also flags billing and variable runtime, which informs the decision to use it. It does not, however, contrast itself against nearby alternatives like research_deep or web_search, so the 'when not to use' half is absent.

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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