按公司关键词、行业、标签、中国/海外地区、成立年份、发展阶段、千里马、独角兽、公司资质等条件统计对外可见公司总数。适合回答多少家公司、总计几家、数量是多少；不要为了计数遍历 search_companies 分页。
| Name | Required | Description | Default |
|---|---|---|---|
| cities | No | ||
| keyword | No | ||
| tag_ids | No | ||
| location | No | ||
| provinces | No | ||
| scope_ids | No | ||
| is_unicorn | No | ||
| born_year_end | No | ||
| is_horse_club | No | ||
| sub_scope_ids | No | ||
| tag_match_mode | No | union | |
| born_year_start | No | ||
| fund_status_ids | No | ||
| qualification_type_ids | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool counts only externally visible companies (对外可见公司总数), which is a behavioral trait. It does not mention rate limits, authentication, or side effects, but for a simple count operation these are less critical. The description is truthful and non-contradictory.
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 short paragraph with no superfluous content. It front-loads the filter conditions and ends with usage guidance. Every sentence adds value, achieving high information density.
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 description provides the overall purpose and usage guidance, but lacks detailed parameter semantics. The output schema presumably returns a count, but the description does not confirm this. For a tool with many optional filters, more explanation of how filters combine and what defaults apply would improve completeness.
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?
There are 14 parameters with 0% schema description coverage. The description lists filter categories (关键词, 行业, 标签, etc.) but does not map them to specific parameter names or explain their valid values. For example, the difference between tag_ids, scope_ids, and sub_scope_ids is unclear. This reliance on implicit understanding reduces usability for an AI agent.
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 counts externally visible companies by various filters (keyword, industry, tags, location, founding year, etc.). It provides specific use cases ('回答多少家公司、总计几家、数量是多少') and explicitly distinguishes from iterating search_companies, which differentiates it from sibling tools like search_companies.
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 explicitly states when to use this tool (for answering questions about company counts) and provides a clear warning against using search_companies for counting ('不要为了计数遍历 search_companies 分页'). This gives strong guidance on appropriate usage and alternatives.
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.
Each tool targets a distinct resource and action: counting vs searching vs retrieving profiles vs ranking vs resolving. There is no ambiguity as tools like count_companies and search_companies have clearly separated purposes (count vs list). Even similar tools like search_companies and search_closed_companies are distinguished by company status.
All tool names follow the verb_noun pattern (e.g., search_companies, get_company_profile, resolve_companies) using snake_case consistently. The naming is predictable and clear, with only minor variations like aggregate_funding_by_tags which still starts with a verb.
With 17 tools, the server covers a comprehensive set of operations for a company/funding database without being overwhelming. Each tool serves a specific need, and the count aligns well with the domain's complexity.
The tool set covers all major operations for querying companies, funding, investors, events, FA cases, tags, and lookups. It includes both aggregated counts and detailed listings, ranking, and name resolution. No obvious gaps for a read-only data retrieval server.
