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find_meeting_time

Find ranked meeting suggestions for multiple locations. Resolves relative date phrases like 'this week' or 'next Friday' automatically for scheduling.

Instructions

Return ranked meeting suggestions from the production meeting search endpoint. For relative-date inputs ("this week", "next Friday", "tomorrow"), route to answer_time_question, which resolves the relative date and dispatches internally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional ISO date to anchor the meeting search.
locationsYes
_endUserIdNoOptional opaque per-call end-user attribution token populated by the MCP host, not the LLM. Use a non-PII hash or token; never send raw email, name, or platform user ID.
countryCodesNo
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'production meeting search endpoint', which implies a live query but does not explicitly state read-only behavior, side effects, authentication requirements, or other operational traits. It also does not disclose potential failure modes or rate limits.

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

Conciseness5/5

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

The description is two sentences, immediately stating the purpose and then giving a critical routing rule. It is front-loaded with the main action and wastes no words, making it easy to parse quickly.

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

Completeness2/5

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

The tool has no output schema, so the description should explain the return format. It only says 'ranked meeting suggestions', which is vague and does not specify the structure, ranking criteria, or pagination. There is no mention of error handling or edge cases, and with no annotations the agent gets little guidance for production use.

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

Parameters2/5

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

Schema description coverage is 50%, so the description should compensate for the remaining parameters. It adds minimal value: it only hints that 'date' should be absolute by routing relative dates elsewhere, but does not explain semantics for locations, countryCodes, or _endUserId beyond what the schema already states. The description does not clarify constraints, formats, or relationships between parameters.

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

Purpose5/5

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

The description clearly states it returns ranked meeting suggestions from a specific endpoint, using a specific verb ('return') and resource ('production meeting search endpoint'). It also distinguishes itself from the sibling tool answer_time_question by explicitly mentioning the routing condition for relative-date inputs.

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

Usage Guidelines5/5

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

The description provides explicit guidance: for relative-date inputs ('this week', 'next Friday', 'tomorrow'), it instructs routing to answer_time_question. This is a clear when-not-to-use condition, and the mention of 'production search endpoint' implicitly defines its intended use for absolute dates. The alternative tool is named.

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

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