company_project基于明确指定的企业名称，查询该企业旗下的品牌项目信息，包括项目名称、融资轮次、成立时间、所属地、项目简介等。
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）。用于查询该企业的品牌项目。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?
The annotations include openWorldHint=true, but no readOnlyHint or destructiveHint. The description does not contradict annotations. It adds behavioral context by listing the types of information returned (project name, financing round, etc.) and notes pricing (0.2 credits per run). However, it does not disclose potential side effects, rate limits, or error conditions, leaving some gaps in transparency.
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 concise, conveying the main purpose in one sentence, plus pricing information. It is front-loaded with the core purpose and does not add unnecessary details. The structure is effective, though the inclusion of pricing in the same field is unusual but acceptable.
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 the tool's moderate complexity (3 parameters, 1 required, output schema present, no nested objects), the description covers the essential purpose and return fields. The output schema likely details return format, so description does not need to. The description is adequate for an agent to understand when and how to use the tool, though it could be more explicit about pagination usage in context.
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 description adds context beyond the schema by explaining what the company_name parameter is used for (querying brand projects) with an example. It also mentions pagination implicitly ('page' and 'limit' parameters are described in the schema). Since the schema already has 100% coverage, the description's added example and field explanation slightly exceed the baseline.
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's purpose: query brand project information for a specified company, including project name, financing round, founding time, region, and project introduction. It uses a specific verb 'query' and resource 'brand project information', and distinguishes it from siblings like 'company_financing' or 'company_basic_info' by focusing on brand projects.
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 needing brand project info for a specific company, but does not explicitly state when NOT to use it or mention alternative tools. The inclusion of '具体企业名称' (specific company name) and example suggests a clear use case, but no explicit exclusions or alternatives are provided.
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).