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export_review

Export a CSV for human blind review from evaluation results, leaving auto-judgments blank to prevent anchoring bias.

Instructions

从运行结果导出人工盲评 CSV(自动判定留空,避免锚定效应)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
out_csvYes
result_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses a meaningful behavioral trait: automatic judgments are intentionally left blank ('自动判定留空') to avoid anchoring effects ('避免锚定效应'). This adds value beyond the schema, though it does not mention file-handling side effects like overwriting or directory creation.

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 a single compact sentence with the key behavioral notice in parentheses. Every element earns its place, and the core action is stated first.

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?

For a simple two-parameter export tool with an output schema and no nested objects, the description covers the source, output format, and intentional blank-judgment behavior. It is nearly complete, though explicit parameter semantics and clearer alternative routing would make it fully self-sufficient.

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

Parameters2/5

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, but it does not explain what result_json and out_csv should contain or whether they are paths, content strings, or file URLs. The description loosely connects '运行结果' to result_json and 'CSV' to out_csv, but this is insufficient given the complete lack of parameter documentation.

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 states a specific verb ('导出', export), a specific resource (人工盲评 CSV, human blind review CSV), and a source (运行结果, run results). It also distinguishes itself from siblings like import_review by making clear it is an export step rather than an import or evaluation step.

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?

The description implies usage context: after run results exist, export a CSV for human blind review. However, it does not explicitly state when to use this tool versus siblings such as import_review, run_eval, or compare_runs, leaving the agent to infer the workflow.

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