company_engineeringanomaly基于明确指定的企业名称，查询该企业涉及的工程异常信息，包括文书号、处理类型、事由、处理结果、实施部门、决定日期等。
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）。用于查询该企业的工程异常信息。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 description uses the verb '查询' (query), which implies a read-only operation, and does not mention any side effects or destructive actions. However, it does not explicitly state that the tool is read-only or safe, and annotations do not include a readOnlyHint. The lack of explicit transparency is slightly mitigated by the query nature, but a definitive statement would improve clarity.
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 a single, focused sentence that conveys all necessary information without redundancy. It efficiently states the tool's purpose and expected output fields. The inclusion of pricing information is separate and does not detract from conciseness. The structure is clean and to the point.
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 description lists the key output fields (document number, processing type, reason, etc.), providing a good sense of what the tool returns. Since no output schema is provided, this list helps complete the context. It does not mention pagination or defaults, but those are covered in the input schema. The overall context is sufficient for a query tool.
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 schema already provides full descriptions for all three parameters (company_name, page, limit), so the baseline is 3. The description reiterates the importance of company_name but does not add additional meaning, constraints, or relationships beyond what the schema specifies. It adds no new semantic value for the parameters.
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 function: querying engineering anomaly information for a specified enterprise. It lists example fields (document number, processing type, reason, etc.), making the purpose unambiguous and distinguishing it from sibling tools like company_project or company_illegal.
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 indicates the primary usage: given an enterprise name, retrieve its engineering anomaly records. It implies this tool is appropriate when engineering anomaly data is needed, though it does not explicitly contrast with alternative tools or state when not to use it. The phrasing '基于明确指定的企业名称' provides sufficient guidance for typical query scenarios.
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).