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ai-nglish

Ainglish MCP Server

Official
by ai-nglish

mint_attempt

Preregister a measurement attempt before spending reader resources: submit the exact manifest and frozen commitment, with predeclared abort conditions for verifiable closure.

Instructions

AUTH: preregister one measurement attempt BEFORE reader spend. Supply the exact manifest object you will later file; the server freezes its canonical sha256 commitment. A completed measurement or an evidenced abort must close the attempt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe proposal slug.
estimandYesWhat this design estimates, frozen before spend.
manifestYesThe exact re-runnable manifest that submit_measurement will later file. It must contain metric, matching that filing.
planned_sampleYesPlanned item, arm and reader counts.
proposal_revisionNoOptional exact proposal surface: slug or slug@revision. Defaults to the slug.
admissibility_gatesYesPredeclared conditions that would abort the run.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the server freezes a canonical sha256 commitment, that this is an auth-required preregistration, and that the attempt must later be closed. This goes well beyond the bare schema and covers the most consequential behavioral trait: immutability of the manifest commitment. It could also mention idempotency or failure modes, but the main side effects are transparent.

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?

Two sentences with zero filler. The first sentence states the action and the timing; the second explains the commitment mechanism and the closing obligation. Every clause earns its place, and the most important constraint is front-loaded.

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?

The description captures the core contract of the tool: timing, manifest exactness, server-side commitment, and closing requirement. For a tool with 6 parameters and nested objects, it relies correctly on the 100% schema coverage. It does not describe return values, but there is no output schema, and the main operational risks (mismatched manifest, unclosed attempts) are addressed.

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?

Schema description coverage is 100%, so each parameter is already documented. The description adds lifecycle semantics, especially the requirement that the manifest be the exact object later filed, and that the estimand is 'frozen before spend.' This is valuable but does not fundamentally compensate for anything missing in the schema; it reinforces rather than clarifies parameter meaning.

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 uses a specific verb ('preregister') with a clear resource ('measurement attempt') and a precise temporal constraint ('BEFORE reader spend'). It also distinguishes itself from the sibling tools submit_measurement and abort_attempt by describing the preregistration step in the lifecycle. This is unmistakably a distinct operation rather than a generic action.

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?

The description provides clear lifecycle context: use this before reader spend, supply the manifest that will later be filed, and ensure the attempt is closed by either a completed measurement or an evidenced abort. It implies but does not explicitly state exclusions, such as 'do not use this to file results — use submit_measurement instead.' Since sibling names make the alternatives inferable, this is strong but not fully explicit.

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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