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by io.github.GoodTurnStudio
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The person's work history, read from their saved CV

get_work_history
Read-onlyIdempotent

The person's work history, read from their saved CV. For filling in application forms, LinkedIn or a profile, or "how many years have I been a nurse?". Each role with its title, employer, place, start and end month, months in the role and its bullet points (most recent first, as on the CV), plus education lines and total years of work. It is a best guess from the CV text, so check it with the person before it goes into a form. A ready-to-read summary is in say.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_work_history"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds genuinely useful behavior beyond that: the data is a best guess parsed from CV text and should be confirmed with the person, ordering is most recent first, and a ready-to-read summary is available in `say`.

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?

Front-loaded with what the tool is and where the data comes from, then use cases, then contents. Every sentence carries information, though the embedded quoted question and the long contents sentence make it denser than it needs to be.

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?

With no output schema, the description carries the return-shape burden and does it well: role fields, education lines, total years, ordering and the `say` summary. It omits the empty/null case (what is returned when no CV is saved), which an agent calling this blind would want to know.

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?

The tool takes zero parameters, so the baseline applies. The description spends its budget on output semantics instead, which is the right allocation for a no-argument read.

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?

States a specific verb and resource plus its source: work history read from the saved CV, broken into roles, education lines and total years. It does not distinguish itself from adjacent read tools such as get_profile or get_about_me, so an agent must infer which of these holds employment data.

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?

Gives concrete situations that should select this tool: filling application forms, LinkedIn or a profile, and answering 'how many years have I been a nurse?'. No exclusions or named alternatives are given, which is the only gap.

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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