company_filing_information基于明确指定的企业名称，查询该企业涉及的立案信息，包括案号、公告/法院、立案日期、开庭日期、结束日期、承办法官、助理法官、当事人、原告、被告、案件状态等。
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）。用于查询该企业的立案信息。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?
Annotations only include openWorldHint:true, so the description carries most of the behavioral disclosure burden. It provides valuable context: the requirement for an explicit company name, the returned fields (case number, court, dates, judges, parties, status), and pricing (0.2 credits per run). It does not cover error cases or rate limits, but the field list and cost note add real substance beyond the annotation.
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 compact sentence that front-loads the core action and resource, followed by a concise field list and a separate pricing line. Every sentence earns its place with no redundancy or filler.
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 straightforward query tool with full schema coverage and an output schema, the description covers the essential context: purpose, required input, and return fields. It lacks explicit usage guidance vs alternatives, but the combination of schema, output schema, and description is mostly sufficient for correct selection and invocation.
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 already covers all 3 parameters with descriptions (company_name, page, limit), so the description adds little beyond the schema. It does emphasize that company_name must be explicit, which is useful but marginal. Baseline 3 is appropriate because the schema does the heavy lifting and the description does not meaningfully complement it.
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 uses a specific verb '查询' (query) and a clear resource '立案信息' (filing information), and enumerates the fields returned (case number, court, dates, judges, parties, status). It does not explicitly differentiate from siblings like company_judgement, but the resource and field set are specific enough that the tool's purpose is unambiguous.
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 states the input condition ('基于明确指定的企业名称') and the data returned, implying use when filing case details are needed. However, it provides no explicit when-to-use vs alternatives or exclusions, which is a notable gap given the large number of sibling company_* tools that could overlap.
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).