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by ai.openarx
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compare_papers

Generate side-by-side comparison of 2-5 papers. Returns structured grid: shared entities (intersection), per-paper unique entities, contentType breakdown, top keyConcepts. Built on LLM-extracted entities + chunk classifications. Use for systematic literature review, surveying competing approaches, identifying research gaps between methods.

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": {
      "documentIds": "<documentIds>"
    },
    "name": "compare_papers"
  }
}

Input Schema

NameRequiredDescriptionDefault
detailNo'minimal' = entity intersection sizes only. 'standard' = full per-doc breakdowns. 'full' = + benchmark/code/dataset cross-comparisonstandard
run_idNoOptional. The active methodist run_id (as returned by the methodist diagnose / get_current_dose door). Pass it whenever you call this tool while working inside a run, so the call is attributed to that run for the §8 usage crosscheck — attribution is run-anchored, so it stays correct even if your access token refreshes mid-run. Must be YOUR run: a run_id owned by a different principal, or a non-existent run_id, is rejected.
dimensionsNoWhich comparison dimensions to compute
documentIdsYesDocuments to compare (UUIDs). Minimum 2, maximum 5.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions that the output is 'built on LLM-extracted entities + chunk classifications', giving insight into the method. However, it does not disclose any side effects, limitations, or performance characteristics (e.g., processing time, costs). The description is adequate but not thorough.

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 two sentences: the first states the action and output, the second provides usage context. No superfluous information; every sentence earns its place.

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?

The tool has 4 parameters (1 required) and no output schema. The description explains the output structure reasonably. It could mention prerequisites (e.g., documents must exist) or error scenarios, but overall it covers the key aspects for an AI agent to decide to use it.

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 baseline is 3. The description does not add extra meaning to the parameters beyond what the schema provides. The output description is in the tool description, not parameter-specific. Hence, minimal additional value.

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 uses specific verbs ('Generate side-by-side comparison'), identifies the resource ('2-5 papers'), and describes the output ('structured grid: shared entities...'). It clearly distinguishes from sibling tools, many of which are search or retrieval tools, and there is no other comparison tool in the sibling list.

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?

The description explicitly says 'Use for systematic literature review, surveying competing approaches, identifying research gaps' indicating appropriate contexts. While it does not provide explicit alternatives or when-not-to-use, the context is clear and the use cases are well-stated.

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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