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by net.gentechlabs
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Agent Research — On-Demand LLM Research & Analysis

agent_research
Read-onlyIdempotent

[$0.05 USD per call] On-demand AI agent research and analysis. Task examples: summarize a document, compare two protocols, extract structured data, draft a briefing. Runs against a local LLM; returns text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesWhat to do with the topic — e.g. summarize, compare, extract, draft, critique.
topicYesSubject to research or analyse, e.g. x402 payment rails, LP yield on Base.
paymentNoOptional x402 payment proof (EIP-3009 signed authorization). Call once without it to receive the payment requirements, then retry with the proof.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / task / description
      Previous value: -"Path parameter 'task'"New value: +"What to do with the topic — e.g. summarize, compare, extract, draft, critique."
    • changedInput schema / properties / topic / description
      Previous value: -"Path parameter 'topic'"New value: +"Subject to research or analyse, e.g. x402 payment rails, LP yield on Base."
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "task": "summarize",
+    "topic": "x402"
+  }
+]
  3. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds value by disclosing a $0.05 per-call cost, execution against a local LLM, and text return type—none of which appear in the annotations. It doesn't cover rate limits or auth details, but the payment flow is already in the schema and the annotations mitigate safety concerns.

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 short sentences front-load the cost and then give examples plus output details. There is no filler or repetition of schema content, so every sentence earns its place.

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

Completeness3/5

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

For a broad research tool with 13 siblings and no output schema, the description leaves the agent to infer how to route between this and related tools. It covers cost, execution, and output type, but is thin on distinguishing scope and scenario.

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 coverage is 100%, so baseline is 3. The description's task examples largely repeat the schema's parameter descriptions (summarize, compare, extract, draft/critique) and don't add new meaning to task, topic, or payment.

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?

Description states a clear action ('on-demand AI agent research and analysis') and gives concrete task types, so an agent understands what it does. It doesn't differentiate from sibling tools like agent_discovery or market_intelligence, leaving room for ambiguity when multiple research-ish tools exist.

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?

Task examples imply common use cases, but there is no explicit 'use this for X, use sibling Y for Z' guidance. The description does not mention when not to use it or which alternative to prefer, so the agent must infer applicability from examples.

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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