Skip to main content
Glama

SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain Discredited Company Count

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

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

B3.4/5.0
Behavior3/5

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

The description mentions pricing details and states what metrics are returned, but does not disclose aspects like whether the result is cached, whether it requires any authentication, or any limitations on region granularity. With openWorldHint=true, the behavior is not fully transparent.

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 concise, with the main purpose in the first sentence, followed by metric details, exclusions, and examples. The pricing info is appended unnecessarily but not harmful. It is front-loaded and organized.

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

Completeness3/5

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

Given the tool's medium complexity, the description covers the purpose, metrics, and examples. The output schema exists, so return details are handled elsewhere. However, it lacks information on edge cases like empty results or validation of region/chain names, but it is adequate for a count query.

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?

All parameters have descriptions in the schema, and the description provides example values. However, it does not add the exact format or normalization rules for region names (e.g., '北京市海淀区' vs '海淀区') or chain names, which could be ambiguous. It adds minimal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it queries the count of discredited companies for a specific region and industry chain, and lists the metric types. It does not explicitly distinguish from the sibling 'chain_discredited_company_list' tool, but the tool name and 'count' semantics make the distinction implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides typical question phrasings and states what is not included, but does not explicitly say when to use this vs. the list variant or other similar tools. It lacks explicit conditions for use, though the examples help infer usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All SupplyGraph.AI Data toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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