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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Park Year5 Company Count

park_year5_company_num

基于具体园区名称存续5年以上的企业数量查询。 涉及指标/类型：存续5年以上的企业数量 不包含：其他企业分类的统计；企业名单明细 典型问法：中关村软件园存续5年以上的企业有多少；张江高科技园区存续5年以上的企业数量；苏州工业园区存续5年以上的企业有多少家

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. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With only openWorldHint as an annotation, the description carries the burden of behavioral disclosure. It adds useful scope constraints and exclusions but does not explain data coverage limitations, optional year behavior, or edge cases like unknown parks. No contradiction with annotations.

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 purpose is front-loaded, the inclusion/exclusion distinction is compact, and the typical questions are useful for intent recognition. The pricing block adds minor noise but does not undermine clarity.

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 two-parameter count tool with an output schema, the description is adequately complete: it defines the metric, gives examples, and excludes list results. It could be stronger by explicitly naming sibling tools for list or chain-level alternatives.

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%, and both parameters already have meaningful descriptions. The tool description reinforces park_name with examples but adds no semantic detail beyond 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 clearly states a specific query: counting companies surviving more than 5 years for a named park. It also differentiates itself from list-style tools by explicitly excluding 企业名单明细 and from other statistical categories by excluding 其他企业分类的统计.

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 user questions and explicit non-inclusions provide clear usage scope. The description does not name alternative tools like park_year5_company_list for detailed company lists, but the exclusions effectively communicate when not to use this tool.

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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TDQS

B3.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.

Naming Consistency4/5

Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.

Tool Count1/5

With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.

Completeness4/5

The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).

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