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SupplyGraph.AI.Daasmart

Park Issued Tender Company Count

park_issued_tender_company_num

基于具体园区名称近两年发起过招标的企业数量查询。 涉及指标/类型:近两年发起过招标的企业数量 不包含:其他企业分类的统计;企业名单明细 典型问法:中关村软件园近两年发起过招标的企业有多少;张江高科技园区近两年发起过招标的企业数量;苏州工业园区近两年发起过招标的企业有多少家

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 100, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo统计年份,如 2024;可选。
park_nameYes园区名称,如「中关村软件园」「张江高科技园区」「苏州工业园区」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response with the company count result. Also used for in-progress, failed, cancelled, or waiting-user messages.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the minimal openWorldHint annotation, the description discloses key behavioral scope: park-specific filtering, two-year window, count-only output, and exclusions. It also includes pricing information. It does not explain how the optional year interacts with the two-year window, but the output schema covers return structure.

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?

The description is compact and front-loaded with purpose, followed by exclusions, examples, and pricing. It is efficient overall, though the '涉及指标/类型' line slightly repeats the first sentence's content.

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 count tool with one required and one optional parameter and an output schema, the description is largely sufficient. Missing details around year semantics and explicit alternative tool naming are the main gaps, but they do not prevent correct selection or invocation in most cases.

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 baseline is 3. The description reinforces park_name with examples and clarifies the count scope, but it does not add meaningful semantics beyond the schema for the optional year parameter, especially its relationship to the '近两年' window.

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?

Description states a specific verb+resource+metric: query the count of enterprises that initiated tenders in a named park over the past two years. It also explicitly excludes company-list details and other classifications, clearly distinguishing it from sibling list/count tools.

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?

Typical question examples provide clear context for when to use the tool. The '不包含' section gives exclusion criteria relevant to sibling tools, but it does not explicitly name an alternative tool such as park_issued_tender_company_list, so it falls just short of full when/when-not guidance.

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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