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SetLuca

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by setluca

Read this period's message count

luca_usage_messages
Read-onlyIdempotent

Check used, allowed, and remaining direct-message credits for the current Luca workspace period before bulk drafting replies to avoid exceeding limits.

Instructions

Read the message pool: used, allowed, and remaining for the period. Check this before starting work that drafts in bulk.

Luca operation: GET /api/usage/messages Required scopes: usage:read Required OAuth scope: luca:read; data sensitivity: redacted Does not require idempotency. Use workspaceId or workspaceSlug to override the default workspace configured by environment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceIdNoOptional Luca workspace id override.
workspaceSlugNoOptional Luca workspace slug override.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/openWorldHint=false, so the safety profile is covered. The description adds real context beyond that: required scopes (usage:read, luca:read), data sensitivity 'redacted', and that the default workspace comes from environment config. The line 'Does not require idempotency' is slightly loose wording against idempotentHint=true, but it refers to an idempotency key, not idempotency itself, so it is not a contradiction.

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?

Purpose and the pre-flight usage tip are front-loaded in two tight sentences, followed by clearly labeled metadata lines (operation, scopes, sensitivity). The boilerplate is structured rather than rambling, though a few lines are administrative noise for a simple read.

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?

For a zero-required-parameter read tool with an output schema, the description covers what is read, when to read it, auth scope, and workspace override behavior. Nothing essential is missing; the only soft spot is the ambiguous idempotency phrasing.

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 goes further by explaining that workspaceId/workspaceSlug override a default workspace 'configured by environment' and that either one may be used. That default-workspace behavior is not derivable from the schema strings alone, so it earns above baseline.

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 first sentence gives a specific verb and resource ('Read the message pool') and even enumerates the returned values (used, allowed, remaining) scoped to 'the period'. This is clearly a usage/quota read, distinguishable in spirit from the sibling luca_usage_spend, though the description never names that sibling explicitly to draw the contrast.

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?

It states a concrete usage condition: 'Check this before starting work that drafts in bulk,' which tells the agent when to call it. There is no when-not guidance or explicit alternative (e.g., use luca_usage_spend for cost rather than message counts), so it stops short of a 5.

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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