company_change_record基于明确指定的企业名称，查询该企业的变更记录信息，信息维度包括变更前、变更后、变更内容、变更日期等。比如可以查询企业的法定代表人变更、注册资本变更、经营范围变更等各种企业工商信息变更。
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）。用于查询该企业的变更记录(工商变更)。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, with no readOnlyHint or destructiveHint. The description uses '查询' (query) implying a read-only operation, but does not explicitly disclose safety, permissions, or pagination behavior. Without annotations, the description carries the burden; it fails to clarify if data is mutable or any access restrictions. No contradiction with annotations, but insufficient 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 concise sentences (plus pricing info) that front-load the purpose and examples. There is no fluff or redundancy; it is directly informative for an agent.
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 simple with 3 parameters and an output schema exists, so return value details need not be in the description. The description covers the core functionality and examples, but omits potential nuances like sorting or handling multiple change records. Given the output schema, completeness is adequate, though slight gaps remain regarding edge cases.
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?
Schema description coverage is 100%, so the schema already documents page, limit, and company_name. The tool description adds examples of company changes but does not enhance parameter understanding beyond what the schema provides. Baseline 3 is appropriate since schema handles the heavy lifting.
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 a company's change records, with dimensions like before/after change, content, and date. Examples of specific changes (legal representative, registered capital) make the purpose concrete. It is distinct from sibling tools that focus on specific change types (e.g., enterprise_change_executive_change) by covering general changes, though it doesn't explicitly name alternatives.
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 when a specific company name is known ('基于明确指定的企业名称'), but provides no explicit guidance on when to prefer this over sibling tools like enterprise_change_* or company_basic_info. No exclusions or alternatives are mentioned. The context is clear but not fully elaborated.
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).