Designers

Tree test scorer

tree_test_scorer

Read-only

Score tree test (findability) data. Give results as CSV with a header row and one row per participant per task, with columns task, success (yes/no or 1/0), and optionally direct (reached the answer without backtracking), first_click (the top-level label clicked first), time (seconds). Optionally expectedFirstClicks as lines 'Task name = Label, Label'. Returns per task: participants, success rate with 95% Wilson interval and a band (80+ strong, 60 to 79 mixed, under 60 weak, our rule of thumb), directness, direct-success rate, first-click distribution and the share on expected labels, median time, and notes (small sample, success that needed backtracking, wrong first clicks). Reports skipped rows rather than failing.

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": {
      "results": "<results>"
    },
    "name": "tree_test_scorer"
  }
}

Input Schema

Name	Required	Description	Default
`results`	Yes	CSV text (or an array of row objects) with columns task, success, direct, first_click, time
`expectedFirstClicks`	No	Lines like 'Task name = Label, Label'

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly/non-destructive/closed-world, so the bar is lower, yet the description adds real behavior: bands (80+ strong, 60–79 mixed, <60 weak), Wilson intervals, and that it reports skipped rows rather than failing. That error-handling disclosure is exactly the kind of context annotations can't carry.

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-loads the purpose in one clause, then packs input format and output contents densely. Every clause carries information, though the single long sentence with nested parentheticals is slightly harder to scan than it could be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must describe returns, and it does so thoroughly: per-task participants, success rate with 95% Wilson interval and band, directness, direct-success rate, first-click distribution and expected-label share, median time, and notes. Nothing an agent needs to invoke or interpret the result is missing.

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 baseline is 3, but the description adds meaning beyond the schema one-liners: success accepts yes/no or 1/0, direct means reaching the answer without backtracking, first_click means the top-level label, and time is in seconds. It also clarifies the expectedFirstClicks syntax ('Task name = Label, Label').

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?

States a specific verb and resource — 'Score tree test (findability) data' — and its input requirement (one row per participant per task) immediately distinguishes it from siblings like trunk_test, analyse_card_sort, or usability_stats. An agent can tell what this computes without opening the schema.

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 clear context for invocation: supply `results` as CSV with a header, and `expectedFirstClicks` only if expected labels are defined. It does not explicitly exclude or route to a sibling tool, so there is no named when-not, but the conditions for use are unambiguous.

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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