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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower. The description adds real value beyond that: it discloses the source-label enum semantics (unlimited/no-ai/tier/top-up-only) and the behavioral intent of gating AI-consuming calls. It stops short of stating auth needs or what happens on cap exhaustion, keeping it at a 4.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, both earning their place: the first enumerates returned fields, the second gives usage guidance. The action-critical 'use before...' instruction is front-loaded logically and there is no filler.

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

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

No output schema exists, and the description compensates by listing exactly what is returned. Combined with the usage guidance and annotation-provided safety profile, an agent has everything it needs to call this zero-param tool correctly.

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

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

The tool takes zero parameters, so per the rubric the baseline is 4. The description correctly does not invent parameter guidance, and its field enumeration describes output rather than inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Read') and resource (the caller org's AI token budget) and enumerates the exact fields returned (monthly cap, per-seat cap, active members, top-up tokens, source label). This clearly distinguishes it from near-siblings like get_my_quota_usage or get_capacity by scoping to the org budget.

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

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

Explicitly prescribes when to call it — 'BEFORE calling tools that burn AI tokens' — and names concrete alternatives (summarize_project, analyze_project_risks, generate_status_report). It even gives the decision rationale: fail fast or fall through to a non-AI path.

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