company_import_export基于明确指定的企业名称，查询该企业拥有的进出口信用信息，包括海关注册编码、注册海关、是否注销、行业类别、经营类别、注册日期、报关有效期、海关信用等级等。
Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 0.2}
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 指定返回第几页结果，从 1 开始，默认 1；与 limit 配合使用。 | |
| limit | No | 指定单次请求最多返回的记录数，默认 20，最大 100。 | |
| company_name | Yes | 企业名称（必填）。用于查询该企业的进出口信用记录。示例：通威股份有限公司 |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only include openWorldHint, not readOnlyHint or destructiveHint. The description uses '查询' (query), implying a read-only operation, and lists the returned fields, adding context. However, it does not explicitly state side effects, limitations, or potential error conditions, leaving some behavioral ambiguity. It also includes pricing info, which is additional context but not deep behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary purpose and output summary, followed by pricing details. There is no redundant information, and every sentence serves a purpose. The structure is efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with an output schema available, so the description needs to cover purpose, parameters, and output behavior. It lists the key fields returned, mentions the required company name, and includes pricing. It does not explicitly cover pagination or edge cases, but the schema handles those. Given the abundance of sibling tools, the description adequately distinguishes this tool by its import/export focus, making it reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with detailed descriptions for all three parameters (company_name, page, limit). The description does not add parameter-specific meaning beyond what the schema states, though it reiterates the requirement of company_name. This meets the baseline for full schema coverage, so a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool queries import/export credit information for a specific company, listing key fields like customs registration code, registered customs, deregistration status, and credit rating. This is a specific verb+resource combination that distinguishes it from siblings such as company_basic_info or company_credit_rating, which focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it is for querying import/export credit information based on a company name, which makes it clear when to select this tool. However, it does not explicitly mention when not to use it or point to alternatives, though the tool's unique scope (import/export) is self-evident among the many siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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).