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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain High Tech Company Count

chain_high_tech_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高新技术企业数量查询（合并返回总量/生产型/销售型/依赖型文本）。 涉及指标/类型：高新技术企业数量；生产型高新技术企业数量；销售型高新技术企业数量；依赖型高新技术企业数量 不包含：其他企业分类的统计；企业名单明细 典型问法：2024年全国集成电路高新技术企业有多少；成都市新能源产业链高新技术企业数量；海淀区人工智能高新技术企业有多少家

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

A4.3/5.0
Behavior3/5

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

The description indicates a query operation, implying read-only behavior, but the annotation openWorldHint=true suggests possible side effects. Since the description does not address this ambiguity or provide additional behavioral context beyond the annotation, transparency is moderate.

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 with sections for the main action, included metrics, exclusions, and typical queries. It avoids unnecessary verbosity while conveying all key information.

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?

It explains what the tool returns (total count plus breakdown by type) and what it excludes, covering the main use case. However, it does not specify the exact output format (e.g., JSON structure), which could be clarified, but this is not critical for a count-based tool.

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

Parameters5/5

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

All three parameters (year, region, chain_name) are described with examples and allowed scopes (e.g., region can be country/province/city/district). The descriptions fully cover the schema, and the typical queries reinforce the meaning of each parameter.

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?

Clearly states the tool queries the count of high-tech companies in a specific region and industry chain, and lists the types of counts returned (total, production, sales, dependency). The verb '查询' and resource '高新技术企业数量' are specific, and it distinguishes from list tools by excluding enterprise lists.

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 queries and explicitly excludes other company classifications and lists, guiding the agent to use alternative tools for those cases. However, it does not explicitly contrast with the many other 'num' sibling tools (e.g., chain_specialized_company_num), though the 'high-tech' specificity is 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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