SupplyGraph.AI Data

Chain Have Copyright Company Count

chain_have_copyright_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拥有软著的企业数量查询（合并返回总量/生产型/销售型/依赖型文本）。涉及指标/类型：拥有软著的企业数量；生产型拥有软著的企业数量；销售型拥有软著的企业数量；依赖型拥有软著的企业数量不包含：其他企业分类的统计；企业名单明细典型问法：2024年全国集成电路拥有软著的企业有多少；成都市新能源产业链拥有软著的企业数量；海淀区人工智能拥有软著的企业有多少家

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 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.

First observedAug 19, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With only openWorldHint provided, the description carries the transparency burden and does disclose output aggregation behavior and what is excluded. It adds useful scope context beyond annotations, such as merging total/production/sales/dependent counts and not returning other classifications or company lists.

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 first-sentence purpose, followed by included metrics, exclusions, examples, and pricing. It is somewhat wordy but each section earns its place and supports tool selection.

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?

The tool has an output schema, and the description sufficiently covers invocation semantics, region/chain/year usage, returned metric categories, and exclusions. Minor ambiguity remains around the exact '合并返回...文本' format, but selection and invocation are well supported.

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%, so the baseline is 3. The description adds minor context like region granularity (country/province/city/district) and typical phrasings, but does not substantially extend the parameter semantics already present in 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 this tool queries the count of enterprises with software copyrights by region and industry chain, specifying the aggregated return types (total/production/sales/dependent). The explicit exclusion of '企业名单明细' distinguishes it from the sibling list tool chain_have_copyright_company_list.

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 question formats and the '不包含' exclusions give clear usage context, indicating this is for counts rather than detailed lists. It does not explicitly name the alternative list tool, but the sibling presence and exclusion make the intended use evident.

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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