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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain Participated Tender Company Count

chain_participated_tender_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近两年参与过投标的企业数量查询（合并返回总量/生产型/销售型/依赖型文本）。 涉及指标/类型：近两年参与过投标的企业数量；生产型近两年参与过投标的企业数量；销售型近两年参与过投标的企业数量；依赖型近两年参与过投标的企业数量 不包含：其他企业分类的统计；企业名单明细 典型问法：2024年全国集成电路近两年参与过投标的企业有多少；成都市新能源产业链近两年参与过投标的企业数量；海淀区人工智能近两年参与过投标的企业有多少家

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

A4.6/5.0
Behavior4/5

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

The description discloses that the tool returns a merged total and breakdowns by production/sales/dependent types, and states what it excludes. It does not mention any side effects, but as a query tool this is implicitly read-only. The annotation openWorldHint does not contradict.

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 well-structured with separate lines for purpose, metrics, exclusions, and examples. It is informative without being overly verbose, efficiently covering necessary details.

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?

The description provides comprehensive context: what the tool does, what it returns, what it excludes, and typical usage examples. It is sufficient for an agent to understand the tool's scope and output.

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) have clear descriptions with examples in the input schema, and the tool description explains how they are used in the query, providing complete semantic coverage.

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's function: it queries the number of companies that participated in bidding in the last two years based on region and industry chain. It explicitly lists the metrics (total, production, sales, dependent) and excludes list details, making its purpose unambiguous.

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?

The description provides context for when to use the tool by indicating it returns counts rather than lists, and gives typical questions. However, it does not explicitly contrast with sibling list tools, though the naming convention ('_num') implies the distinction.

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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