SupplyGraph.AI Data

Chain Tech Oriented Company Count

chain_tech_oriented_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科技型中小型企业数量查询（合并返回总量/生产型/销售型/依赖型文本）。涉及指标/类型：科技型中小型企业数量；生产型科技型中小型企业数量；销售型科技型中小型企业数量；依赖型科技型中小型企业数量不包含：其他企业分类的统计；企业名单明细典型问法：2024年全国集成电路科技型中小型企业有多少；成都市新能源产业链科技型中小型企业数量；海淀区人工智能科技型中小型企业有多少家

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

A4.4/5.0

Behavior4/5

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

描述明确说明合并返回总量/生产型/销售型/依赖型文本，披露了返回内容的结构；也说明不包含其他分类统计和企业名单明细。虽然annotations只有openWorldHint=true，未包含readOnlyHint/destructiveHint，但'查询'一词暗示非破坏性操作，且描述对返回内容的说明已超出基础schema，提供了有用的行为上下文。计费信息（Pricing）也提供了成本透明度。

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?

描述结构清晰：首句说明核心功能，然后列出涉及的指标/类型和不包含范围，最后给出典型问法示例，每部分都有实际信息量。虽稍长但无冗余填充，计费信息和描述在合理范围内。

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

工具参数少（3个）、有输出schema（has output schema: true）、schema覆盖率100%，描述已经足够完整，包含了功能、指标明细、排除范围、典型问法，还补上了计费信息。代理可以正确调用该工具而无需额外猜测，上下文完整性良好。

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描述覆盖率100%（3个参数全部有描述），但描述额外提供了参数的典型示例（如地区'全国''成都''北京市海淀区'，产业链'集成电路''新能源''人工智能'），并且解释了region和chain_name在逻辑上的关系（地区限定、产业链限定），这有助于代理构造正确的参数。因此虽未提供语法细节，但示例和场景补充提升了参数语义。

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?

描述明确说明基于地区和产业链名称查询科技型中小型企业数量，并列出合并返回的三种类型（生产型/销售型/依赖型）及典型问法，动词和资源明确，与sibling中的chain_tech_oriented_company_list（列表工具）形成明显区分，同时与chain_company_num等计数工具区分了统计口径（仅限科技型中小企业，不包含其他企业分类）。

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?

描述明确列出使用场景（具体地区+产业链名称查询数量），并通过'不包含'条目排除了统计企业名单明细和其他分类统计，让代理知道该工具不做什么。典型问法示例（2024年全国集成电路等）提供了清晰的调用场景，但未明确提及何时应使用sibling中的list工具而非本num工具，这算轻微不足。

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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