performix-site

Self-diagnose against one book's model (computes)

self_diagnose_book

Read-only

The runnable library's weakest-lever read, computed here from the committed book model: rate how present each of the book's levers is on your team (1–5) and get the one to work on first, ranked by weakness then causal leverage — the SAME function performix.app/learn/library uses. Levers come from list_books' exec-shelf slugs (e.g. the-culture-code). N=1, self-report, not psychometrically calibrated: tier INFERRED. Refuses degenerate ratings (fewer than two levers rated, all ratings equal, or values outside 1–5) instead of inventing an answer.

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": {
      "slug": "<slug>",
      "ratings": "<ratings>"
    },
    "name": "self_diagnose_book"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	Exec-shelf book slug, e.g. the-culture-code.
`ratings`	Yes	Lever id → rating 1..5 (lever ids come from the book's model constructs; rate at least two).

Output Schema

Name	Required	Description	Default
`slug`	Yes
`title`	Yes
`ranked`	Yes
`honesty`	Yes
`weakest`	Yes
`itemsTotal`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 22, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already indicate readOnlyHint=true, but the description goes beyond by explaining the function is the same as performix.app, notes that it is N=1 self-report and not psychometrically calibrated (tier INFERRED), and explicitly states it refuses degenerate ratings. This is excellent disclosure of behavior beyond the annotations.

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 description is a single long sentence but front-loads the key purpose and computation. It reads densely, but every part earns its place. Some might prefer it broken into two sentences for readability, but it's not wasteful.

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?

Given the complexity (2 params, one nested) and the presence of an output schema, the description covers the essential inputs, constraints, and behavioral notes. The output schema presumably details the return format, so not explaining it is acceptable. Minor gap: it doesn't explain what 'tier INFERRED' means or how to interpret the ranked output, but the output schema may cover that.

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 schema already provides 100% coverage, describing slug as an exec-shelf book slug and ratings as a mapping of lever id to rating. The description adds the example slug and reinforces the requirement to rate at least two levers, but mostly it re-states what the schema already says.

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 a specific verb ('self-diagnose'), the resource (a book's model), and the computed output (ranked weakest lever). It distinguishes itself from siblings like list_books by explicitly noting the lever slugs come from list_books' exec-shelf slugs.

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 explicitly states when to use this tool (for a self-diagnosis against a book) and what it computes, and it implies when not to use it (when you just need to list books, use list_books). It also provides clear constraints: rate at least two levers, ratings between 1-5, and not all equal, which is practical usage 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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