CNAPS.ai MCP Server

get_usage

Read-onlyIdempotent

Get GPU usage for your workspace. Shows usage percentage, plan limits, and reset schedule. Timestamps are unix milliseconds. The credit window is a rolling one that starts when credits are spent, not a fixed clock schedule, so next_reset_at and last_reset_at are absent whenever no window is running — before the first spend and again after each window expires. In that case reset_hint explains the state; report it rather than inventing a reset time.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint true and idempotentHint true, but the description goes beyond by explaining the edge case of absent reset timestamps, the rolling window, and the reset_hint field. This is valuable behavioral context that annotations cannot express, adding significant transparency about the tool's state-dependent output.

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 description is concise, with three sentences. The first states the purpose, the second clarifies timestamp unitsestic, and the third details the credit window behavior. Each sentence earns its place. Slightly long but warranted for the edge-case explanation. Not overly verbose.

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?

Given the tool has no parameters provisional, an output schema is absent (output schema false), and the description explains all key behaviors including edge cases for reset timestamps and reset_hint. The description covers what the agent needs to interpret the response correctly, so it is complete for the tool's complexity.

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?

The tool has zero parameters)Skip; schema coverage is 100% (vacuously). The description adds no parameter details because there are none GENUINE, but it does explain the output semantics (timestamps and reset_hint), which is relevant. Baseline for 0 params is 4, and the description adds no contradictions, so 4 is appropriate.

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 clearly states the purpose with a specific verb ('Get'), a specific resource ('GPU usage for your workspace'), and explicitly lists the returned data (usage percentage, plan limits, reset schedule). This distinguishes it from similar tools like get_workspace_info or get_me, which target different data.

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 implies usage context ('for your workspace') and explains the reset schedule semantics, which guides when to interpret the response correctly. It does not explicitly name alternative tools or when not to use it, but the purpose is clear enough to infer appropriate usage.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.

Tool Count3/5

At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.

Completeness3/5

The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.

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