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get_tabs

Retrieve tab autocomplete usage data including suggestions shown, accepted, and rejected, with optional date range and user filters.

Instructions

Get tab autocomplete usage: suggestions shown vs accepted vs rejected, with line-level detail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usersNoComma-separated emails to filter by specific users
endDateNoEnd date. Formats: "YYYY-MM-DD", "today", "yesterday". Default: "today"
startDateNoStart date. Formats: "YYYY-MM-DD", "7d", "30d", "today", "yesterday". Default: "30d"
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. It only states the basic purpose and mentions 'line-level detail', but does not disclose behavioral traits like whether the data is aggregated, if sensitive information is exposed, or any authorization requirements. The term 'line-level' is ambiguous without further explanation.

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

Conciseness4/5

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

The description is a single sentence that front-loads the key action: 'Get tab autocomplete usage'. It is concise and avoids unnecessary words, but it omits important details, such as the meaning of 'line-level detail' and the structure of the response.

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?

Given no output schema, the description should compensate by explaining the return format. It mentions metrics but does not specify whether the result is a list of events, aggregated counts, or per-user breakdown. With 3 parameters and no annotations, the description is insufficient for complete understanding.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents all three parameters (users, startDate, endDate) with descriptions. The tool description adds no additional semantic value beyond what the schema provides, earning a baseline score of 3.

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 the tool retrieves 'tab autocomplete usage' and specifies the metrics: suggestions shown, accepted, rejected. It also mentions 'line-level detail', distinguishing it from sibling tools like get_commands or get_mcp_usage, which cover different domains.

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

Usage Guidelines2/5

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

No usage guidance is provided. The description does not indicate when to use this tool over alternatives, nor does it specify prerequisites or limitations such as date range constraints or user filtering behavior beyond what is in the schema.

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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