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josemachado-vp

SF Assistant MCP Server

search_employees

Search for employees by entering a natural language query. Supports filtering by name, department, country, status, location, manager, company, or user ID.

Instructions

Search for employees using natural language queries.

Supports searching by:

  • Name: "John", "John Smith", "Smith"

  • Department: "department 40000013", "dept HR"

  • Country: "country COL", "employees in Colombia"

  • Status: "active employees", "terminated employees"

  • Location: "location NYC"

  • Manager: "reports to admin", "manager 12345"

  • Company: "company 1000"

  • User ID: "user admin"

Combinations work too: "active employees in department 40000013"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query. Examples: 'John Smith', 'department 40000013', 'employees in Colombia', 'active employees in location NYC'
data_centerNo
max_resultsNoMaximum results to return (1-100)
auth_user_idNo
auth_passwordNo
search_fieldsNo
include_inactiveNoInclude terminated/inactive employees

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. It lacks information on side effects (e.g., idempotency, read-only nature), authentication requirements (despite auth params), rate limits, or behavior on missing queries. This is a significant gap.

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?

The description is concise, well-structured with bullet points, and provides relevant examples without unnecessary text. Every sentence adds value.

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

Completeness4/5

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

For a tool with 7 parameters and natural language input, the description covers usage comprehensively with examples. However, it lacks behavioral details (auth, idempotency) and does not explain how search matching works. Output schema exists but not included; overall adequate but not exhaustive.

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?

The description adds meaning beyond the schema by listing supported query fields and examples, but it does not explain parameters like data_center, search_fields, or auth params. Schema description coverage is 43%, so the description partially compensates.

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 'Search for employees using natural language queries' and enumerates specific searchable fields with examples, effectively distinguishing it from sibling tools like get_employee_profile or get_employee_history.

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

Usage Guidelines4/5

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

The description provides explicit searchable fields and example queries, including combinations. However, it does not discuss when not to use this tool or compare with alternatives, slightly limiting guidance.

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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