company_stock_violation基于明确指定的企业名称，查询该企业涉及的违规处理信息，包括公告日期、处罚类型、处罚对象、违规行为、处分类型、处分措施、处理人、处罚金额等。
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）。用于查询特定上市企业的违规处理记录。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 provide only openWorldHint:true, which signals potential external effects but is vague. The description does not disclose any additional behavioral traits such as read-only nature, authentication requirements, rate limits, or behavior when company not found. Since the annotation does not cover safety profile, the description should carry more weight but only states the query fields, leaving transparency gaps.
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 concise sentence (plus pricing note) that front-loads the action and resource. It efficiently lists the key data attributes without excessive verbosity. Slightly loses a point because the list of fields is not strictly necessary and could be considered clutter, but overall it is well-structured and short.
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?
Given that an output schema exists, the description need not explain return values. However, the description does not clarify that this tool is specifically for listed companies (as indicated in the schema example) nor does it mention any limitations or relationship to other violation-related tools. The context could be richer for a tool in a large sibling set, making it moderately 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 fully describes all parameters (company_name with example, page and limit with defaults and max). The description does not add any parameter-specific context beyond the schema, so it meets the baseline of 3 for high schema coverage. The list of returned fields is not about parameters, so no additional value is provided.
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 violation handling information for a specific company by name, listing many relevant fields (公告日期, 处罚类型, 处罚对象, etc.). This is a specific verb+resource with scope, but it does not explicitly distinguish from similar tools like company_punish or company_illegal, so it loses a point for lack of explicit differentiation.
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 says '基于明确指定的企业名称' (based on a clearly specified company name), which implies the prerequisite of having a company name. However, it provides no guidance on when to use this tool over alternatives (e.g., company_punish, company_illegal) and no when-not-to-use scenarios. This is minimal guidance, scoring a 2.
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).