Skip to main content
Glama

SupplyGraph.AI Data

by io.github.SupplyGraphAI

Chain Won Tender Company List

chain_won_tender_company_list

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有中标的企业列表查询（合并返回总量/生产型/销售型/依赖型文本）。 涉及指标/类型：近两年有中标的企业列表；生产型近两年有中标的企业列表；销售型近两年有中标的企业列表；依赖型近两年有中标的企业列表 不包含：其他企业分类的统计；仅返回数量不返回名单 典型问法：2024年全国集成电路近两年有中标的企业名单；成都市新能源产业链近两年有中标的企业列表；海淀区人工智能近两年有中标的企业有哪些

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 lists. 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

A4.1/5.0
Behavior3/5

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

The description discloses the time window (last two years), the merge of totals/types into text, and exclusions, but lacks details on output format, pagination, or any side effects. With only openWorldHint annotation, more behavioral context could be added, but it doesn't contradict annotations.

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: purpose first, then included types, exclusions, and examples. It's somewhat long but each section adds value, and the typical questions make it actionable without excessive verbosity.

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?

Given the output schema exists, the description doesn't need to detail return fields. It covers key usage aspects: region/chain combination, time window, exclusions, and examples. It could mention limits or pagination, but that's likely in output schema. Overall, it provides sufficient context for most queries.

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 covers 100% of parameters with descriptions, and the description adds concrete examples (region like '全国', '成都', chain_name like '集成电路') and typical question patterns that clarify usage of region, chain_name, and optional year. This goes beyond the schema's basic definitions.

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 queries companies that won tenders in the last two years based on region and industry chain, and explicitly lists the included result types and exclusions. It differentiates from sibling tools like chain_won_tender_company_num by specifying it returns the list, not just a count, and gives typical query examples.

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 typical questions that illustrate when to use this tool, and explicitly notes exclusions (other categories, count-only results). However, it doesn't explicitly recommend this over similar tools like chain_participated_tender_company_list, though the context implies it for won-tender lists.

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