company_randomin_spection基于明确指定的企业名称，查询该企业涉及的抽查检查信息，包括检查机构、抽查类型、抽查日期、抽查结果等。
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）。用于查询该企业的抽查检查信息。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?
The annotations include openWorldHint: true, which indicates the tool may return data outside a predefined closed world, but the description does not contradict this. The description mentions 'price per run' which is useful operational context. However, the description does not disclose other behavioral traits such as the return format (though an output schema exists), whether pagination is supported (it is, via page and limit parameters, but those are also documented in the schema), or any potential side effects. Given the positive annotation, the description adds context about pricing, which is helpful but not extensive. The lack of behavioral disclosure beyond the schema and pricing keeps this at a moderate 3.
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 concise and front-loaded: it states the core purpose in one sentence, then lists the key output fields succinctly. It includes pricing information in the description, which is extra but useful. No fluff. The structure is clear and efficient.
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 has a moderate complexity (3 parameters, 100% schema coverage, and an output schema exists). The description explains what the tool does and the main purpose. It does not need to explain return values because an output schema exists. It lacks some contextual completeness regarding when to use this tool vs alternatives (e.g., if the user wants inspection history, this is the right tool), but given the schema and output schema, it is complete enough for basic usage. It also includes pricing which adds context. A score of 4 is justified, as it falls short of fully guiding an agent on alternative usage but covers the essential information.
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 has 100% description coverage: company_name is described as required, with an example; page and limit are described with defaults and limits. The description does not add much beyond the schema, but it does reinforce the key parameter (company_name) and provides an example. With full schema coverage, the baseline is 3. The description's mention of the data fields (检查机构, 抽查类型, etc.) relates to the output, not parameters. No additional parameter semantics are added beyond what the schema already provides.
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's purpose: to query inspection/check information for a specific company, including inspection agency, type, date, and results (基于明确指定的企业名称...查询该企业涉及的抽查检查信息...). It specifies the target resource ('企业名称') and the data fields returned, distinguishing it from sibling tools like company_illegal or company_punish which cover violations or penalties, whereas this focuses on random inspections (randomin_spection). The verb '查询' and the explicit scope make it distinct.
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 when to use this tool: it is for querying inspection records for a clearly specified company name (基于明确指定的企业名称). It also implies that the company name is required and an example is given. However, it does not explicitly mention alternatives or when not to use it (e.g., if the company name is not known, search_company_candidates could be used first; or if the user wants other types of company data, use other company_* tools). The exclusion is implicit, not explicit, so a score of 4 is appropriate rather than 5.
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).