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get_employee_details

Retrieve employee details (role, department, email, ID) by searching with a name. Returns all matching employees.

Instructions

Look up an Appinventiv employee's profile by name — returns their role, department, email, and employee_id. If multiple employees match, all are returned. Use the employee_id with get_employee_dsrs() to see their DSR history. Prefer this over get_staff_directory() when you want a single employee's full details. Example: get_employee_details('Arjun Sharma') → {name, role, department, email, employee_id}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
name_queryYesPart of the employee's name to search for (case-insensitive). Example: 'Anish Katoch' or just 'Anish'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, but the description discloses that multiple employees may match and all are returned. Implies read-only operation through 'look up'. Does not mention failure scenarios, but adequate for a lookup tool.

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

Conciseness5/5

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

Three sentences plus an example, front-loaded with core purpose. Every sentence is informative and efficient, no wasted words.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, output schema exists), the description is complete: covers purpose, usage, example, and return fields. No gaps.

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

Parameters4/5

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

Only one parameter with 100% schema coverage. Description adds value by providing an example and clarifying case-insensitivity, which is already in schema but reinforced.

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

Purpose5/5

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

The description clearly states the tool looks up an employee's profile by name and lists the returned fields. It also distinguishes from a sibling tool, get_staff_directory, by specifying preference for single employee details.

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

Usage Guidelines5/5

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

Explicitly states when to use (single employee full details) and when to prefer get_staff_directory. Provides a use case and example, and advises to use the employee_id with get_employee_dsrs for DSR history.

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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