usageCheck how many Rams reviews this workspace has used and has left (rolling 30 days, shared between the GitHub App and MCP). Free to call — does not consume a review.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idiempotent/non-destructive, and the description adds valuable behavioral context: rolling 30-day window, quota shared between GitHub App and MCP, and the fact that calling it does not consume a review. This goes well beyond the annotations and informs agent-side decision-making.
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 concise sentences deliver the essential information with the main purpose front-loaded. The second sentence adds two useful behavioral details without bloat.
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?
For a zero-parameter, read-only quota check with no output schema, the description fully covers what an agent needs: the metric, the time window, the shared nature, and the non-consumption guarantee. Nothing important is missing.
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 has zero parameters, so there is nothing for the description to explain at the parameter level. The description still clarifies what the quota represents and its scope, which is appropriate for a parameterless tool.
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?
The description states a specific verb ('Check') and a specific resource ('how many Rams reviews this workspace has used and has left'). It clearly differentiates this from the sibling review-execution tools by framing it as a quota query rather than an action.
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?
The description implies when to use this tool: to inspect the shared rolling 30-day quota before or while performing reviews. It explicitly notes 'Free to call — does not consume a review,' which helps an agent choose this over review tools. It does not explicitly name alternatives or say 'use this instead of X,' but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The four tools are largely distinct: quick_review and review_files both review UI, but are clearly differentiated by depth/return (no score vs score+patch) and cost; usage and verify_fixes are distinct. Slight overlap between quick_review and review_files could cause selection confusion if an agent misreads their purposes, but descriptions are strong.
All names use a snake_case verb_noun pattern (quick_review, review_files, usage, verify_fixes). The verbs 'quick' and 'verify' are adjectival/modifying, but the pattern is consistent and predictable, with only 'usage' being a bare noun rather than verb_noun.
At four tools, the set is tightly scoped for a UI design review server. Each tool addresses a distinct step: quick check, deep review, quota status, and fix verification. No redundancy or bloat; the count is appropriate for the purpose.
The server covers the core review lifecycle: detect issues (quick_review), detailed review with score (review_files), check usage (usage), and verify fixes (verify_fixes). A minor gap is no explicit tool for bulk codebase audits or historical reports, but the primary workflow is complete and no dead ends exist.