lbm_get_history

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context about response structure varying between time_range and start_date/end_date parameters, and specifies the return type.

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?

Front-loaded 'WHEN TO USE' and 'REQUIRES' sections, efficiently using four sentences to convey purpose, prerequisites, returns, and a behavioral note. No unnecessary words.

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 complexity (8 params, no output schema), the description provides a clear overview of purpose, return type, and a key behavioral caveat. It lacks examples but the schema covers parameter details, making it sufficiently complete.

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 coverage is 100%, so parameters are well-documented. Description adds meaning by clarifying the overriding relationship between start_date and time_range, and notes response structure differences, which goes 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?

Description clearly states the tool retrieves competitor mention trends over time, with per-model breakdowns. It distinguishes from sibling tools like lbm_list_results or lbm_get_transcript by focusing on historical trend analysis.

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?

Provides explicit context for use (competitive trend analysis and reporting) and a prerequisite (project_id from lbm_list_projects). However, it does not explicitly exclude alternative tools or state when not to use it.

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