AI Takeover Tracker MCP Server

search_occupations

Search occupations by job title to retrieve AI displacement risk scores and identify automation vulnerabilities for career planning.

Instructions

Search 59,000+ occupations by job title. Returns matching jobs with AI displacement risk scores. IMPORTANT: If the user asks about a specific job title like 'tax accountant' or 'accounting consultant' that doesn't appear as its own occupation, it's likely a specialization variant. Search for the base occupation (e.g., 'accountant') then use get_specializations to find the exact variant with its adjusted risk score.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_occupations"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Job title to search for (e.g., 'accountant', 'software engineer', 'nurse')
`limit`	No	Max results to return

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.3Apr 16, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's scope (59,000+ occupations), return format (matching jobs with risk scores), and important behavioral nuance about specialization variants. However, it doesn't mention potential limitations like search algorithm behavior or error conditions.

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 perfectly structured with two sentences: the first states the core functionality, the second provides crucial usage guidance. Every word earns its place, and the 'IMPORTANT' flag appropriately highlights critical information.

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 search tool with no annotations and no output schema, the description provides excellent context about what the tool does and how to use it effectively. The only gap is the lack of information about return format details, but given the tool's relatively simple purpose, this is a minor omission.

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 documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline score of 3 reflects adequate coverage through the schema alone.

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's purpose with specific verbs ('search', 'returns') and resources ('59,000+ occupations', 'matching jobs with AI displacement risk scores'). It distinguishes from sibling tools like 'get_occupation_risk' by emphasizing search functionality rather than direct retrieval.

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?

The description provides explicit guidance on when to use this tool vs alternatives: it instructs to search for base occupations when specific titles aren't found, then use 'get_specializations' for variants. This clearly differentiates from sibling tools and addresses common usage scenarios.

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

Deploy Server