company_session_announcement基于明确指定的企业名称，查询该企业涉及的法院公告信息，包括公告日期、公告类型、案由（纠纷类型）、法院、当事人、公告内容等。
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）。用于查询该企业的法院公告信息。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?
除 openWorldHint 外没有 readOnlyHint 或 destructiveHint 等注释，描述本身说明了查询行为和返回字段，但没有披露精确匹配要求、空结果行为、权限或限制等额外行为。查询性质隐含安全，但细节不足。
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?
描述主体为一句话，信息密度高，前句即点明用途，后续字段列表和定价信息不会造成冗余。Pricing 不对工具行为产生语义贡献，但总体结构仍算紧凑。
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?
该工具是低复杂度查询，必需参数 company_name 和分页参数在 schema 中完整定义，输出 schema 也已存在。描述本身虽缺少边界情况说明，但结合 schema 已足够支撑常见使用场景。
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?
input-schema 已对全部 3 个参数（company_name、page、limit）提供完整说明，覆盖率达 100%。描述没有为参数增加超出 schema 的额外含义，因此给予中等分数。
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?
描述使用明确的动词“查询”和具体的资源“企业涉及的法院公告信息”，并列出公告日期、公告类型、案由、法院、当事人、公告内容等返回字段，能够清晰地将该工具与裁判文书、开庭公告等兄弟工具区分开。
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?
描述隐含了使用方式：在企业名称明确指定时查询其法院公告，符合输入要求。但没有说明与 company_judgement、company_session 等同族工具的选型边界，也没有给出不适用场景或排除条件。
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).