ai_job_exposure_calculatorAI Job Exposure Score — An AI job exposure score built from published research (Eloundou et al. 2023, O*NET, Goldman Sachs): estimate your task mix, not a prediction about you.
| Name | Required | Description | Default |
|---|---|---|---|
| physical | Yes | ||
| interpersonal | Yes | ||
| routineCognitive | Yes | ||
| creativeCognitive | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses the research sources and the important caveat that the result is an estimate, not a prediction about an individual. However, it does not explain the output format, interpretation, or any limitations beyond the 'not a prediction' caveat, leaving room for more transparency.
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, well-structured sentence that immediately states the tool's purpose, includes credible sources, and adds a key caveat. Every word earns its place; there is no redundancy or filler.
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?
Given the tool's relative simplicity, the description is adequate but has clear gaps: it does not state what the output looks like (score range, units, or interpretation) and does not elaborate on how to use the result. The research citations and caveat provide some context, but without annotations or an output schema, more completeness would be expected.
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?
Schema description coverage is 0%, and the description does not explain any of the four parameters. It only says 'estimate your task mix' without detailing how routineCognitive, creativeCognitive, interpersonal, or physical should be interpreted or whether they should sum to a particular value. The description fails to compensate for the lack of schema-level parameter descriptions.
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 that this tool computes an AI job exposure score based on published research, using a specific verb ('estimate') and resource scope ('task mix'). It also distinguishes itself from the many other calculators by naming its research basis and adding the caveat that it is 'not a prediction about you.'
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 implies the tool is for estimating AI job exposure from a task mix, but it does not explicitly state when to use it or mention alternatives. There is useful context (research-based, not predictive), but no direct when-to-use or when-not-to-use guidance, so it falls at the 'implied usage' level.
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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Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.