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Glama

SupplyGraph.AI Data

by io.github.SupplyGraphAI

Enterprise Change Entrepreneur Image

enterprise_change_entrepreneur_image

基于具体企业名称，按企业查询雇主品牌方面的周期变化，用于查询企业家学历、社会任职与个人荣誉。不用于高管离职调岗或高管负面舆情查询。 涉及指标/类型：企业家最高学历；企业家社会任职；企业家个人荣誉 不包含：非本分类指标；按园区/产业链批量筛企业名单 典型问法：中国比亚迪股份有限公司企业家最高学历；美国Tesla, Inc.企业家社会任职；日本丰田自动车株式会社企业家个人荣誉

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 30, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes企业名称，如「比亚迪股份有限公司」「Tesla, Inc.」。
country_nameYes国家名称，如「中国」「美国」「Japan」「China」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response generated by the agent. Returned for completed results as well as 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

A4.3/5.0
Behavior4/5

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

The description adds context beyond the openWorldHint annotation by specifying the exact indicators covered and excluded, and notes pricing (30 credits per run). It doesn't detail output format or pagination, but the output schema exists and the description is sufficiently transparent about scope.

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 clear sections: purpose, exclusions, covered indicators, and examples. It's slightly verbose with the pricing JSON block, but the core content is front-loaded and each sentence serves a purpose.

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 tool's moderate complexity (2 params, output schema present, openWorldHint), the description is complete: it covers purpose, exclusions, indicators, and examples. The pricing info is a bonus. It doesn't explain return values, but the output schema handles that.

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 schema already documents both parameters (company_name and country_name) with examples. The description adds typical query examples that illustrate parameter usage, but doesn't add significant new semantics beyond 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 the tool queries employer brand periodic changes for a specific enterprise, listing exact indicators (entrepreneur's highest education, social positions, personal honors). It explicitly distinguishes from sibling tools by excluding executive departures/negative sentiment queries, and provides 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 Guidelines5/5

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

The description explicitly states when to use (query entrepreneur education, social positions, honors by company) and when not to use (not for executive departures, negative sentiment, non-category indicators, batch screening by park/industry chain). It also provides typical query examples for clarity.

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