flayr

Experiment readout

get_experiment_readout

Read-onlyIdempotent

[read-only] n per arm, pooled reach engagement rate (or average percentage watched when selected), a 90% interval and a hedged verdict (likely_winner, no_clear_winner or not_enough_data). Missing numbers are "not reported" (null), never 0. Say "likely", never "proven". (Wraps GET /api/v2/experiments/{id}/readout.)

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": {
      "id": "<id>"
    },
    "name": "get_experiment_readout"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes
`window`	No		48h

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 4, 2026

TDQS

B3.2/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description still adds real behavior: missing numbers are reported as null and never 0, the verdict is hedged into one of three fixed labels, and it wraps GET /api/v2/experiments/{id}/readout. These are substantive facts 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.

Conciseness4/5

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

The text is compact and information-dense, with no filler sentences. It leads with output contents rather than the action, and the trailing parenthetical about the wrapped endpoint is minor but useful provenance.

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

Completeness3/5

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

With no output schema, the description competently covers the return shape (sample sizes, metric, interval, verdict), which is the main risk here. However, it leaves both input parameters undocumented and gives no routing guidance among the many experiment siblings, so it is only adequate for this tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description bears the full burden for the two parameters, and it explains neither. The 'id' parameter is unmentioned, and the 'window' parameter with its 48h/7d/30d/latest enum and 48h default is never referenced; 'when selected' is too vague to map onto a parameter.

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 the resource (experiment readout) and specifies precisely what the readout contains: n per arm, pooled reach engagement rate or average percentage watched, a 90% interval, and one of three verdict labels. That is concrete enough for an agent to know what it gets back, but it never distinguishes this from siblings such as get_experiment_results or get_experiment_rounds.

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

Usage Guidelines2/5

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

There is no statement of when to call this instead of get_experiment_results or get_experiment, and no prerequisites or exclusions are given. The only guidance present ('Say "likely", never "proven"') governs how to phrase the output, not tool selection.

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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