Compass OS: Career

get interview prep

get_interview_prep

Read-onlyIdempotent

Read the saved interview prep for one tracked role (company and title as the pipeline lists them): likely questions with the stories that answer them, questions to ask, a 30-60-90 day plan, and the saved company research (what the company does, what the role cares about, where the user's background fits, what to check). Use it before an interview; when nothing is saved yet, or it's out of date, make_interview_prep makes it. Read-only.

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": {
      "company": "<company>"
    },
    "name": "get_interview_prep"
  }
}

Input Schema

Name	Required	Description	Default
`title`	No	The role's title as the pipeline lists it; leave it empty when the user named only the company
`company`	Yes	The employer's name, e.g. "Stripe"

Output Schema

Name	Required	Description
`text`	Yes	The answer, or what was done, in plain words for the user: the same text as content
`undo`	No	On a saved write: its undo handle, for undo within 24 hours
`widget`	No	The card the host draws with it, e.g. "pipeline"; "confirm" means a write waits for the user's Yes and nothing is saved yet

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context: the lookup is scoped to a tracked role identified by company and title, and the response is the saved prep contents. It does not describe what happens when the role is not found, which keeps it from a 5.

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?

The definition is front-loaded with the core action and its return contents, then the usage rule and alternative. It is dense but every clause carries information; the trailing 'Read-only' is redundant with the annotations and is the only wasted wording.

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 an output schema and full schema descriptions present, the description need not explain return values, and it correctly focuses on scope and the generation alternative. It is nearly complete for a two-parameter read tool, missing only not-found or stale-result behavior beyond the routing hint.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning by emphasizing that company and title must match how the pipeline lists the role, and that title is omitted when the user named only the company, reinforcing the identity semantics beyond the schema text.

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 names a specific verb and resource — reading saved interview prep for one tracked role — and enumerates the contents (questions with stories, questions to ask, 30-60-90 plan, company research). It is clearly distinguishable from make_interview_prep, which generates rather than reads.

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?

It explicitly states when to use it ('before an interview') and names the alternative with the condition that selects it ('when nothing is saved yet, or it's out of date, make_interview_prep makes it'). Nothing is left to inference.

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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