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Glama

Enterprise Change License

enterprise_change_license

基于具体企业名称,按企业查询资质认证方面的周期变化,用于查询银行证券保险等金融牌照及集团牌照数量。不用于高新ISO等非金融资质认证查询。 涉及指标/类型:是否有银行牌照;是否有证券牌照;是否有保险牌照;是否有信托牌照;是否有期货牌照;是否有租赁牌照;所属集团公司拥有的金融牌照有多少种;所属集团公司旗下共有多少家金融机构;所属集团公司有多少家子公司是投资机构;旗下的银行牌照数量是多少;旗下的证券牌照数量是多少;旗下的保险牌照数量是多少等 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是否有银行牌照;美国Tesla, Inc.是否有证券牌照;日本丰田自动车株式会社是否有保险牌照

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 160, "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

A4.1/5.0
Behavior4/5

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

The description discloses the tool's scope in detail: covered indicators (bank/securities/insurance licenses, group license counts, etc.), exclusions, and the focus on periodic changes. This adds meaningful context beyond the openWorldHint annotation. No contradictions with 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 structured into purpose, covered indicators, exclusions, typical questions, and pricing. Though it lists many indicators, each line serves to clarify scope and prevent misuse. It is slightly long but appropriately detailed for a tool with many possible outputs.

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?

The description covers purpose, scope, exclusions, and examples, and is supported by a rich output schema. Some ambiguity remains about the 'periodic changes' time dimension, but for selecting and invoking the tool, the description is sufficient and clearly differentiates from sibling enterprise_change_* tools.

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?

The input schema already provides full descriptions for both parameters with examples like '比亚迪股份有限公司' and '中国'. The description adds typical questions showing parameter usage but no additional parameter-specific semantics (e.g., normalization or language handling). The baseline of 3 applies due to 100% schema 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 queries qualification/certification cycle changes for financial licenses and group license counts based on a specific enterprise name. It explicitly distinguishes from non-financial certifications like high-tech ISO, lists specific indicators, and provides typical questions, making it easy to differentiate from sibling tools.

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 explicit usage context: use for financial license queries, with clear exclusions such as non-financial certifications and batch filtering by park/industry chain. It includes typical questions that illustrate when to use, but does not name direct alternative tools, so the guidance is strong but not exhaustive.

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