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HypePaper

paper_results

Every result row extracted from one paper's tables — its own numbers and the baselines it reports — each with its SOTA standing on that lane: rank, number of entities, whether it is the current leader, the leader and the gap to it. Use to verify a paper's claims or to see which benchmarks it competes on before calling benchmark_timeline. Unknown experimental conditions return comparison_status=insufficient_information and sota=null. When rows_truncated is true, total_rows says how many exist: pass dataset to get one benchmark's rows, or a larger limit — do not fill the gap from memory. Rows do not carry the paper's table numbers; do not cite a Table number you have not read in the paper itself. [Free · 3 credits]

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": {
      "paper_id": "<paper_id>"
    },
    "name": "paper_results"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoResult rows to return
datasetNoOnly rows on this dataset (any common spelling)
paper_idYesPaper UUID or arXiv id like 2401.12345

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations supplied, the description carries the full burden and does so: it discloses the output shape, the edge case where comparison_status=insufficient_information and sota=null, and the truncation contract (rows_truncated/total_rows) with explicit remediation. It also warns against backfilling truncated rows from memory or citing unread table numbers.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and row contents are front-loaded, then edge cases, then truncation handling. The sentences are dense but each carries operational information; the anti-hallucination cautions are useful though slightly repetitive in tone.

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?

There is no output schema, yet the description enumerates the meaningful return fields (rank, entity count, leader, gap, comparison_status, sota, rows_truncated, total_rows). Combined with the truncation and unknown-condition behavior, an agent has everything needed to call and interpret it.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description goes beyond it by explaining that dataset narrows to one benchmark's rows and that a larger limit is the alternative remedy for truncation. Paper_id semantics are left entirely to the schema.

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 names a specific resource and scope ('every result row extracted from one paper's tables') and spells out the content of each row (rank, entity count, leader status, leader, gap). It also implicitly separates itself from benchmark_timeline and leaderboard-style siblings by framing itself as the per-paper row source.

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?

It gives two concrete use cases ('verify a paper's claims' or 'see which benchmarks it competes on') and a sequencing rule ('before calling benchmark_timeline'). There is no explicit when-not guidance or named alternative for the leaderboard-style view, which keeps it short of a 5.

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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