Ottom

Get the public benchmark for a store category

get_benchmark

Read-onlyIdempotent

Returns Ottom’s published benchmark for a store category: how far AI agents get at each stage, the stalls that stop them most often, and the score spread. Aggregate figures only, never a named store, published under CC BY 4.0. Use "all" for every store. An unknown category answers with the list of published ones.

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": {
      "category": "<category>"
    },
    "name": "get_benchmark"
  }
}

Input Schema

Name	Required	Description	Default
`category`	Yes	A category slug, e.g. "fashion", or "all"

Output Schema

Name	Required	Description
`url`	Yes	The benchmark page on ottom.io.
`name`	Yes	The dataset’s name.
`license`	Yes	The licence the figures are published under.
`metrics`	Yes	The figures: how far AI agents get at each stage, the most common stalls and the score spread. The same document as /benchmarks/{category}/data.json.
`summary`	Yes	The benchmark in a few sentences.
`category`	Yes	The category slug, or all.
`methodology`	Yes	How the figures are made.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description still adds real context beyond them: the data is aggregate only and never names an individual store, it is published under CC BY 4.0, and an unknown category answers with the published list rather than erroring — behavior an agent could not infer from 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three compact sentences, front-loaded with what is returned and immediately followed by the privacy and licensing constraints and the argument edge cases. No filler and nothing repeated from the title or annotations.

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 one required parameter, an output schema, and full annotation coverage, the description supplies what is missing: data granularity, licensing, and unknown-category behavior. The only loose end is the absence of any routing guidance toward or away from the sibling tools.

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% and the pattern plus example already document the slug format. The description nonetheless adds meaning the schema does not encode: the sentinel value "all" for the whole corpus and the recovery path when a category is unknown, both of which directly affect how the argument is chosen.

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?

The description names a specific verb and resource ("Returns Ottom's published benchmark for a store category") and enumerates the payload: stage attainment, top stalls, and score spread. That is far more than a restatement of the title. It doesn't explicitly contrast itself with check_store_readiness, but the content is distinctive enough that an agent can tell what it gets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Guidance is limited to parameter usage: use "all" for every store, and an unknown category returns the list of published ones. That is useful fallback behavior, but the description never says when to reach for this versus the sibling readiness/check tools, leaving the selection decision 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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