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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain Specialized Company List

chain_specialized_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专精特新企业列表查询（合并返回总量/生产型/销售型/依赖型文本）。 涉及指标/类型：专精特新企业列表；生产型专精特新企业列表；销售型专精特新企业列表；依赖型专精特新企业列表 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年全国集成电路专精特新企业名单；成都市新能源产业链专精特新企业列表；海淀区人工智能专精特新企业有哪些

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 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.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

The description adds detail that the response is a merged text of total/production/sales/dependent types, which is beyond the openWorldHint annotation. It does not disclose pagination, authentication, or rate limits, but given the minimal annotations and simple list-query nature, it is adequate but not extensive.

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 well-structured with a clear main purpose, a list of included types, exclusions, and typical queries. It front-loads the action and resource, and every sentence serves a purpose. Slight redundancy in listing the types twice, but overall efficient.

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?

Given the existence of an output schema and the moderate complexity, the description adequately covers the tool's purpose, inputs, and typical usage. It does not explain the exact output structure, but that is provided by the output schema. It could be improved by explicitly naming the count-only counterpart, but it is complete enough for an agent.

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 descriptions cover all three parameters, and the description adds value by specifying region granularity (country/province/city/district), chain name examples, and marking year as optional. This goes beyond the schema alone, justifying a score above the baseline.

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 the tool queries specialized and innovative enterprise lists based on region and industry chain, explicitly listing the merged return types (total/production/sales/dependent). It distinguishes from the count-only sibling tool (chain_specialized_company_num) by noting it returns lists, not just quantities, and from other chain_list tools by specifying the specialized category.

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?

Provides typical query examples and explicitly excludes other enterprise classifications and quantity-only returns, guiding appropriate use. However, it does not directly mention the alternative count tool (chain_specialized_company_num) by name, though the exclusion implies it. This is clear context but could be more explicit.

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