OpenPapers

Research topic

research_topic

Generate a provenance-preserving literature overview on a given topic, labeling synthesis and omitting unsupported claims to enable reproducible research workflows.

Instructions

Build a provenance-preserving literature overview. Synthesis is labeled; no unsupported claims are emitted.

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": {
      "topic": "<topic>"
    },
    "name": "research_topic"
  }
}

Input Schema

Name	Required	Default
`depth`	No	quick
`limit`	No
`topic`	Yes
`objective`	No	literature_review

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It does disclose meaningful output behavior: 'Synthesis is labeled; no unsupported claims are emitted.' However, it does not mention whether the tool performs searches, whether it has side effects, how sources are selected, or what the returned overview contains.

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 exceptionally concise: two short sentences, with the primary action front-loaded in the first sentence. Every phrase adds meaningful information about the tool's purpose or behavioral constraints, with no filler.

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

Completeness2/5

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

Given four parameters, two enums, no output schema, and no annotations, the description is too sparse to support correct invocation. An agent cannot tell what 'depth' or 'objective' control, what the final overview looks like, or how the provenance guarantee is reflected in the output.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate by explaining at least the key parameters. It does not mention topic, depth, limit, or objective at all, leaving the agent to infer meaning solely from parameter names and enum values.

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?

The description states a clear action and deliverable: 'Build a provenance-preserving literature overview.' It also adds a distinctive qualifier ('provenance-preserving') that helps set it apart from generic research tools, though it does not explicitly name sibling tools or contrast with them.

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?

The description gives no guidance on when to use this tool versus alternatives like research_method or build_research_report. It implies a literature-overview use case, but provides no explicit conditions, exclusions, or references to sibling tools.

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