company_competing基于明确指定的企业名称，查询该企业的竞品信息，包括竞品项目名称、竞品项目标签、最新融资轮次、成立时间、所属地、项目简介、所属企业、品牌介绍、联系电话、邮箱、官方网址、地址等。
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）。用于查询该企业的竞品信息。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?
The description lists the output fields, which gives some behavioral context. The openWorldHint annotation is present, and the description doesn't contradict it. However, it doesn't disclose potential limitations like pagination behavior (though page/limit params exist), data freshness, or whether results are complete. The pricing info is included but that's not behavioral. With the annotation present, the bar is lower, and the description adds the field list, which is useful but not rich.
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 paragraph that front-loads the purpose and lists the output fields. It's efficient with no wasted words. The pricing info is appended, which is slightly extraneous but not harmful. It's appropriately sized for the tool's complexity.
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?
The tool has an output schema (not shown in detail) and 100% schema coverage, so the description doesn't need to explain return values. However, given the tool's complexity (many output fields, pagination), the description could benefit from clarifying the pagination behavior or noting that results may be incomplete (openWorldHint). It's adequate but not complete for a tool with this many fields.
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 (company_name, page, limit) with descriptions. The tool description adds the list of returned fields, which helps understand what the company_name parameter will retrieve, but doesn't add syntax or format details beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.
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 competitor information for a specified company, listing the specific fields returned (project name, tags, funding round, etc.). It distinguishes from siblings by focusing on 'competing' (竞品) information, which is unique among the many company_* tools. However, it doesn't explicitly contrast with similar query tools like company_basic_info or company_financing, so it's clear but not fully differentiated.
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: provide a company name to get competitor info. It doesn't explicitly state when to use this vs. alternatives (e.g., company_basic_info for company details, company_financing for funding). The openWorldHint annotation suggests it may return partial results, but the description doesn't clarify when this tool is preferred over others. No exclusions or alternative tool names are mentioned.
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).