company_app基于明确指定的企业名称，查询该企业开发的APP信息，包括app名字、APP分类、简介等。
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）。用于查询该企业拥有的APP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?
With only openWorldHint as an annotation, the description carries the transparency burden. It clearly indicates a read-style query, lists the returned information fields, and even includes pricing information. It does not fully describe edge-case behavior, but for a straightforward query tool this is reasonably transparent.
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 core description is a single, front-loaded sentence that efficiently communicates purpose and output fields. The appended Pricing JSON is somewhat non-standard placement, but it still provides useful cost information without adding significant bloat.
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?
For a simple query tool with one required parameter, full schema documentation, and an output schema present, the description is nearly complete. It explains what is returned and emphasizes the required company name. It lacks explicit alternative guidance, but the overall context is sufficient.
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 all three parameters well. The description reinforces that company_name must be explicitly provided but adds little beyond the existing parameter descriptions. Baseline 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 states a specific action and resource: it queries APP information developed by a clearly specified company, including app name, category, and introduction. This is distinct from sibling tools that focus on other company-related assets, such as websites, WeChat, or general works.
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 phrase '基于明确指定的企业名称' implies the tool is appropriate when an exact company name is known, but it does not explicitly say when not to use it or mention alternatives among the many sibling tools. Usage context is implied rather than directly stated.
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).