Check remaining reservation capacity for a business facade slot before create_reservation. HTTP equivalent: GET /api/v1/b/{handle}/availability?date=YYYY-MM-DD&time=HH:mm.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Reservation date (YYYY-MM-DD) | |
| time | Yes | Reservation time (HH:mm) | |
| handle | Yes | Public facade handle (AgentID) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, open-world, and non-destructive behavior, so the description does not need to restate those. It adds that this is a preflight capacity check and provides the HTTP GET equivalent, but it leaves the exact response shape and how capacity is represented to inference.
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 compact sentences front-load the purpose and workflow position, then add the HTTP equivalent. There is no filler or redundant restatement of the tool name.
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 low-complexity check with rich annotations and full parameter documentation, the description is largely sufficient. It could be more explicit about what the returned availability looks like, but the name and 'remaining reservation capacity' phrasing provide enough context for correct invocation.
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 input schema already documents all three parameters with their formats, giving 100% schema coverage. The description's HTTP query string repeats those formats but does not add meaning beyond the schema.
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 action—checking remaining reservation capacity for a business facade slot—and explicitly positions it as a preflight step before create_reservation. This clearly distinguishes it from sibling tools like get_business_facade or create_reservation.
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?
It explicitly says to use this tool 'before create_reservation', giving clear contextual placement in the workflow. It does not spell out when not to use it, but the intended use case is unambiguous given the sibling tool list.
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.
Most tools are clearly distinct: search, verify, get full profile, get official registry, get merged profile, and get business facade each have a specific role. The only borderline overlap is between get_registry and get_merged_profile, but the descriptions explicitly position one as the preferred enriched read, reducing confusion.
All tool names follow a clean verb_noun snake_case pattern with get_, search_, and verify_ as the leading verbs. There are no mixed naming conventions or vague verbs, making the set highly predictable.
Seven tools is well-scoped for a trust/registry lookup service. Each tool serves a clear step in the discovery-to-verification flow without redundancy or unnecessary bloat.
The surface covers the main lifecycle: search registry, search agents, verify existence, fetch agent details, fetch business facade, fetch raw registry, and fetch a merged profile. A minor gap is the lack of dedicated provenance-specific endpoints beyond get_registry, but this is largely addressable via existing tools.