SupplyGraph.AI Data

Chain Company List

chain_company_list

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列表查询（合并返回总量/生产型/销售型/依赖型文本）。涉及指标/类型：企业列表；生产型企业列表；销售型企业列表；依赖型企业列表不包含：其他企业分类的统计；仅返回数量不返回名单典型问法：2024年全国集成电路企业名单；成都市新能源产业链企业列表；海淀区人工智能企业有哪些

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

Input Schema

TableJSON Schema

Name	Required	Description
`year`	No	统计年份，如 2024；可选。
`region`	Yes	地区名称，如「全国」「成都」「北京市海淀区」。
`chain_name`	Yes	产业链或节点名称，如「集成电路」「新能源」「人工智能」。

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Text or Markdown response. Includes merged results for total / product / sales / dependency company lists. 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.

First observedAug 19, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations only provide openWorldHint, so the description carries the burden; it discloses that output is merged text with totals/lists by type and explicitly excludes other category statistics/count-only responses. Read-only is implied by '查询' rather than explicitly stated, but no side effects are suggested.

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 front-loaded with purpose, includes examples, exclusions, and pricing, and is reasonably compact. The '仅返回数量不返回名单' phrase is awkward and slightly conflicts with the tool's list-oriented purpose.

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 query/list tool with an output schema, the description sufficiently covers inputs, output categories, exclusions, and example phrasings. It is adequate for selecting among many sibling list/count tools, though it does not explicitly map to sibling names.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers all parameters (100%), but the description adds meaningful region granularity (国家/省/市/区县), chain-name semantics, and realistic examples. This enriches the schema rather than merely repeating it.

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?

Description clearly identifies a region + chain-name list query, enumerates output categories (total/production/sales/dependent) and gives typical examples. It does not explicitly compare to sibling list tools, and the '仅返回数量不返回名单' line creates some ambiguity for a tool named list.

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?

Typical questions and the region/chain criteria imply when to use, and exclusions such as '其他企业分类的统计' and count-only responses provide negative guidance. It stops short of naming alternatives like chain_company_num or the specialized list variants.

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).

Resources

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