Search restaurants using Google Places. Returns ratings, reviews, features, and pre-filled OpenTable/Resy booking links.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| time | No | ||
| limit | No | ||
| price | No | 1=cheap 2=moderate 3=expensive 4=very expensive | |
| covers | No | ||
| cuisine | No | ||
| location | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states that it returns ratings, reviews, features, and booking links, but does not explicitly confirm it's read-only or mention any limitations (e.g., that booking links are just links, not actual reservations). This gives moderate transparency but leaves some gaps.
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 a single, front-loaded sentence that states the action and key outputs with no wasted words. It is concise and immediately understandable.
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?
With 7 parameters, no annotations, and no output schema, the description is too sparse for an agent to fully understand how to invoke the tool correctly. It lacks guidance on required vs optional parameters, what 'covers' means, how to format date/time, and the structure of the returned data. The mention of outputs helps but the overall context is incomplete.
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?
Schema description coverage is only 14% (only 'price' is described), and the description does not compensate. It does not explain any of the seven parameters, including potentially ambiguous ones like 'covers' or how 'limit' works. The description adds minimal value 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 uses a specific verb 'Search' with a clear resource 'restaurants' and identifies the data source 'Google Places'. It also lists concrete outputs (ratings, reviews, features, booking links), which differentiates it from other search tools like search_drive or search_email.
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 this tool is for restaurant discovery but does not explicitly state when to use it over alternatives or when not to use it. No exclusions or alternative tool references are provided, leaving the usage context implicit.
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.
Many tools are duplicated across services (e.g., get_inbox vs get_outlook_inbox vs get_unified_inbox; create_calendar_event vs create_outlook_event), causing confusion. Summary tools like catch_me_up, morning_briefing, and weekly_preview overlap significantly in function. The sheer number of similar-sounding tools makes selection error likely.
Names generally follow a verb_noun pattern, but verbs are inconsistent (get, check, lookup, fetch, etc.) and some tools use service-specific prefixes (hubspot_, sf_, qb_) while others do not. Mixed conventions like email_action vs send_email and append_to_doc vs append_to_excel reduce clarity, but the overall pattern is still readable.
107 tools is far beyond the typical scope for a single server, making it difficult for an agent to navigate and choose appropriately. The count feels like a kitchen-sink collection of every possible integration rather than a curated set, severely impacting usability.
The server covers a wide range of domains (email, calendar, contacts, documents, CRM, finance, messaging), but there are notable gaps like no delete for contacts or files, no update for Slack messages, and no way to list all documents. Core workflows exist for most areas, but depth is inconsistent.