Travel Product B — list YOUR registered webhook subscriptions (subscription_id, url; secrets are NEVER echoed). Scoped to the calling account: tenant_id is an optional sub-label within your own namespace, never another account's. Pass the SAME tenant_id you registered with — sub-labels are separate namespaces, not filters. Needs an authenticated key.
| Name | Required | Description | Default |
|---|---|---|---|
| tenant_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds valuable behavioral disclosures: secrets are never echoed, the listing is scoped to the calling account, tenant_id cannot reference another account, and an authenticated key is required. These details meaningfully inform the agent about security and access expectations.
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?
The description is compact and front-loaded, with the core purpose in the first line. Every subsequent sentence adds essential context (secrets, scoping, tenant_id behavior, authentication) without unnecessary fluff. The line breaks improve readability without bloating the text.
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?
Given the simple schema (one optional parameter), rich annotations, and no output schema, the description covers all necessary aspects: what the tool lists, the returned fields, tenant scoping rules, authentication requirements, and data privacy. It is complete for an agent to select and invoke the 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 schema only provides the parameter name 'tenant_id' with a default of null, but the description thoroughly explains its semantics: it is optional, a sub-label within the caller's namespace, never another account's, and must match the tenant_id used during registration. This fully compensates for the lack of schema-level parameter descriptions.
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 clearly states 'list YOUR registered webhook subscriptions' with specific output fields (subscription_id, url), making the tool's purpose explicit. It distinguishes itself from sibling tools like register_disruption_webhook and list_disruption_events by focusing on 'your' registered webhooks rather than events or registration.
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 provides clear usage context: it explains the calling-account scoping, that tenant_id must be the same one used during registration, and that sub-labels are separate namespaces rather than filters. It does not explicitly name alternatives or state when not to use the tool, but the context is sufficiently clear for correct selection.
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.
There is notable overlap among search, search_web, search_restaurants, and search_salons, as well as between filter_restaurants/filter_salons and search with constraints. However, descriptions clarify the intended vertical or corpus, and entity getters are distinct. The overlap is manageable but could cause misselection.
Names mostly follow a get_/list_/search_/register_/delete_/submit_/vote_ pattern in snake_case. Minor deviations like 'recall', 'remember', 'research', and 'travel_health' are less predictable but still readable. Overall consistent and clear.
38 tools is on the heavy side for a single MCP server, exceeding the typical well-scoped range. While the server covers multiple subdomains (search, travel disruptions, memory, feedback, research), the sheer number may overwhelm agents and suggests potential consolidation.
The tool surface covers core workflows: search and entity retrieval for restaurants/salons, disruption monitoring with standing queries and webhooks (register/list/delete), research submission/polling, and memory/feedback mechanisms. Minor gaps exist (e.g., no cancel for research jobs, no explicit entity list endpoint), but these are workable and do not break typical agent tasks.