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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain Company Count

chain_company_num

基于具体地区（国家，省份，城市，区县）以及具体产业链名称企业数量查询（合并返回总量/生产型/销售型/依赖型文本）。 涉及指标/类型：企业数量；生产型企业数量；销售型企业数量；依赖型企业数量 不包含：其他企业分类的统计；企业名单明细 典型问法：2024年全国集成电路企业有多少；成都市新能源产业链企业数量；海淀区人工智能企业有多少家

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。
regionYes地区名称，如「全国」「成都」「北京市海淀区」。
chain_nameYes产业链或节点名称，如「集成电路」「新能源」「人工智能」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response. Includes merged results for total / product / sales / dependency company counts. 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

A4/5.0
Behavior3/5

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

The description mentions it returns combined text of total/production/sales/dependent counts. It clarifies it does not include other classifications or company list details. The openWorldHint annotation suggests the data is open-world, which the description doesn't contradict. It doesn't disclose potential ambiguity in region names or year availability, but the basic behavior is transparent.

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 concise and well-structured. It starts with the core function in one sentence, then lists metrics, exclusions, and examples. Every line adds value; no redundancy or fluff.

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 count-query tool with 3 parameters and a clear output schema, the description adequately covers the scope. It mentions typical questions, what's excluded, and the aggregation types. It doesn't describe output format, but with an output schema present, it's not heavily required. The optional year parameter is mentioned in schema but not in description examples; minor gap.

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 coverage is 100% - all three parameters (year, region, chain_name) have descriptions in the schema. The description adds examples of valid values (e.g., '全国', '成都', '集成电路') and mentions year is optional. But it doesn't add much beyond the schema's descriptions.

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 explicit purpose: query enterprise counts by region (country/province/city/district) and industry chain name, returning aggregated counts. Clearly distinguishes from sibling _list tools which return company details, and from other chain_*_num tools by the specific metrics (total/production/sales/dependent).

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?

Description provides clear context: 'based on specific region and industry chain name', including typical query examples. It explicitly lists what is NOT included (other classifications, company name details), which helps avoid misuse. However, it doesn't explicitly state when to prefer this over sibling tools (e.g., chain_company_list) but the examples and exclusions make it clear.

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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