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Glama

Enterprise Change External Policy Impact

enterprise_change_external_policy

基于具体企业名称,按企业查询经营活动方面的周期变化,用于查询是否受美欧日韩及亚洲/全球外贸政策事件影响。不用于查询政策原文或解读政策本身。 涉及指标/类型:是否受到美国外贸政策事件的影响;是否受到欧盟外贸政策事件的影响;是否受到日韩外贸政策事件的影响;是否受到亚洲其他国家外贸政策事件的影响;是否受到全球其他国家外贸政策事件的影响 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受到美国外贸政策事件的影响;美国Tesla, Inc.是否受到欧盟外贸政策事件的影响;日本丰田自动车株式会社是否受到日韩外贸政策事件的影响

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 50, "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

A3.9/5.0
Behavior3/5

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

Annotations only include openWorldHint: true, so the description carries the burden of behavioral detail. It mentions the tool examines '周期变化' (periodic changes) and assesses policy impact, which gives some context. However, it does not describe the nature of the output (e.g., boolean, score, or detailed report) beyond what might be in the (unshown) output schema. It also does not mention any side effects or prerequisites. This is acceptable but not rich.

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 reasonably concise, with the core purpose stated upfront. It includes essential exclusions and examples in a structured manner (via bullet-like lists). It avoids unnecessary repetition and is front-loaded. A small deduction because it mixes prose and list formatting, but overall it is clear and not bloated.

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 is present, the description need not detail return values. It covers purpose, exclusions, and examples sufficiently for an agent to understand when and how to invoke it. It also includes pricing information as extra context. It does not mention any limitations like rate limiting or required permissions, but those are not critical given the tool's apparent read-only nature and the presence of annotations. Overall, it is complete for its intended scope.

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 coverage is 100%: both 'company_name' and 'country_name' parameters are described with examples directly in the schema. The description reinforces these with example questions (e.g., '中国比亚迪股份有限公司' and '美国') but adds no new semantics beyond the schema. Since the schema already covers parameter meaning, the description provides minimal added value, aligning with the baseline score of 3 for high 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: querying periodic changes in business activities for a specific company to assess impact from foreign trade policy events across US/EU/Japan/Korea/Asia/global. It explicitly lists the five indicator types (e.g., '是否受到美国外贸政策事件的影响') and gives concrete example questions. This distinguishes it from sibling enterprise_change_* tools that focus on other dimensions (branch setup, employee benefits, etc.), making the 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 strong exclusions: it is not for querying policy text or interpreting policy itself, and it excludes non-category indicators or batch screening by park/industry chain. This helps an agent avoid misuse. However, it does not explicitly name alternative tools for those excluded cases, though sibling names (e.g., 'enterprise_change_domestic_policy_compliance') imply alternatives. The inclusion of typical questions aids usage understanding. Slight deduction for not explicitly pointing to alternatives.

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