meeting_prep_query_meeting_memories

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

The description explains auto-conversion of recurring event IDs, the difference between 'list' and 'query' modes (LLM invocation), and output structure. No annotations present, so description carries full burden. It provides good behavioral context without contradictions.

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?

Two sentences that front-load the main purpose and key features. No redundant information. Efficient and clear.

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?

No output schema, but description explains return structure for both modes (memories array, llm_response). Covers behavior for 7 parameters and 5 required. Missing details on error handling or limits, but generally complete given complexity.

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 baseline 3. Description adds value for event_id (wildcard auto-conversion) and mode (list vs query). Other parameters like kb_scope, sort_order, context_limit are adequately described in schema; description adds little extra.

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 the tool queries meeting knowledge bases with metadata filtering, date sorting, and optional LLM Q&A. It distinguishes from siblings by focusing on querying stored memories, not generating prep docs or analyzing transcripts.

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 usage for retrieving meeting memories but does not explicitly state when to use this tool versus other meeting prep tools (e.g., analyze_transcript, assemble_meeting_document). No when-not or alternatives are provided.

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