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feishu_search_user

Search for employee information in Feishu/Lark by name, phone number, or email address. Returns matching profiles with details like name, department, and open_id.

Instructions

搜索员工信息(通过关键词搜索姓名、手机号、邮箱)。返回匹配的员工列表,包含姓名、部门、open_id 等信息。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes搜索关键词,用于匹配用户名(必填)
page_sizeNo分页大小,控制每次返回的用户数量(默认20,最大200)
page_tokenNo分页标识。首次请求无需填写;当返回结果中包含 page_token 时,可传入该值继续请求下一页
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return format ('返回匹配的员工列表,包含姓名、部门、open_id 等信息') which is helpful, but doesn't cover important behavioral aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two clear sentences. The first sentence explains the search functionality and scope, the second describes the return format. No wasted words, though it could be slightly more front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (search with pagination), no annotations, and no output schema, the description provides basic functional context but lacks important details. It explains what the tool does and what it returns, but doesn't cover behavioral aspects like pagination mechanics (beyond what's in the schema), authentication needs, or error handling. For a search tool without annotations or output schema, this is minimally adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description adds minimal value beyond the schema - it mentions keyword searching for name, phone, and email, which aligns with the 'query' parameter but doesn't provide additional syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '搜索员工信息(通过关键词搜索姓名、手机号、邮箱)' specifies the action (search) and resource (employee information) with search scope details. It distinguishes from sibling tools like 'feishu_get_user' which appears to fetch a specific user rather than search. However, it doesn't explicitly contrast with 'feishu_im_user_search_messages' which searches messages rather than users.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description doesn't mention when this search is appropriate versus using 'feishu_get_user' for specific user lookup or other search tools. It provides basic functional context but no decision-making criteria for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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