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SAKURAfan1023

Scholar Library

extract_fields

Extract specified fields and table headers from entire PDFs or images using TextIn, reserving all pages. Results require review and are not an evidence index; identical requests reuse the task.

Instructions

调用TextIn字段/表格抽取,会上传整个PDF或图片并预留全部页数;结果需复核,不成为原始证据索引。相同请求复用任务。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysYes
version_idYes
table_headersYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Beyond the annotations, the description discloses meaningful behavior: it uploads the ENTIRE PDF/image and reserves all pages, results require manual review and do not serve as the original evidence index, and identical requests reuse the same task. These are real operational traits (whole-document upload, idempotency, output trust level) that annotations do not convey.

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?

A single, dense sentence that front-loads the action (call TextIn extraction) and then packs behavior/caveats. It is efficient, though the compound clauses make it somewhat heavy.

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 non-read-only, open-world extraction tool with no output schema and zero parameter documentation, the description adequately covers behavioral expectations (upload scope, review requirement, idempotency) but leaves the three required inputs unexplained. It is viable but not fully complete.

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% for three required parameters. The description loosely hints that keys map to extracted fields and table_headers to table columns, but version_id (presumably the document version to upload) is never explained, and no format or constraint details are provided. It fails to compensate for the coverage gap.

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 names a specific operation (TextIn field/table extraction) and resource (PDF/image document), so an agent can tell it is the tool that triggers extraction. It does not, however, explicitly contrast itself with siblings like read_extraction, which reads the extracted output.

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?

Usage is implied (invoke when you need fields/tables pulled from a document) and the caveat that results need review plus the idempotent 'same request reuses task' note give some context. There is no explicit when-to-use/when-not or named alternative.

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