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Glama

misakanet_usage_status

Check remaining free lesson reads, credits, and registration status before submitting usage; register if write access is needed.

Instructions

Check current usage status and remaining quota. Use before calling misakanet_submit_usage to see how many free lesson reads remain and whether registration is needed. Call misakanet_register if is_registered is false and you need write access. Input semantics: user is optional (defaults to 'anon:mcp-default'); pass the token from misakanet_register (e.g. 'token:xxx') to check a registered agent's quota. Output schema: JSON with {user, free_reads_used, free_reads_limit, free_reads_remaining, credits, is_registered, next}. Error cases: none (returns defaults on failure). Side effects: none. Auth: none. Rate limits: none.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userNoOptional user identifier (e.g. 'anon:iphash' or 'token:xxx'). Defaults to 'anon:mcp-default'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.14.0

TDQS

A4.9/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so: it declares side effects (none), auth (none), rate limits (none), and error behavior ('returns defaults on failure'). It also describes the return payload field-by-field, which is critical since no output schema exists.

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?

Purpose and usage are front-loaded, then tightly grouped labeled blocks (Input semantics, Output schema, Error cases, Side effects, Auth, Rate limits). Dense but zero-waste; every clause answers a question an agent would otherwise have to guess at.

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?

With a single optional parameter, no output schema, and no annotations, the description compensates fully by describing the returned JSON fields, failure behavior, and safety profile. Nothing needed to call this tool correctly is missing.

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 already 100%, so baseline is 3, but the description adds workflow-level meaning the schema lacks: where the token value comes from (misakanet_register), the 'token:xxx' format, and the 'anon:mcp-default' fallback. It stops short of explaining any validation or format errors for a malformed user string.

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?

States a specific verb and resource ('Check current usage status and remaining quota') and immediately distinguishes itself from siblings by naming misakanet_submit_usage and misakanet_register as the tools it feeds into. An agent can place it in the workflow without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('Use before calling misakanet_submit_usage') plus a conditional branch ('Call misakanet_register if is_registered is false and you need write access'). Both the trigger and the follow-up alternative are spelled out, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.