ai_search_similar

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

No annotations are provided, so the description carries the full burden. It explains the embedding process, the per-DB Qdrant collection, and return format (ranked hits with score+snippet). It lacks details on error cases, rate limits, or behavior with empty results but is fairly transparent for a search tool.

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 with critical information front-loaded: tool purpose, mechanism, filters, and output. Every phrase adds value, and the structure is efficient and easy to parse.

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 7 parameters and no output schema or annotations, the description covers the core search functionality and return format. It lacks details on pagination, error handling, and interpretation of score_threshold, which would be helpful but not critical for this type of tool.

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 only 43%, with only query, model, and company_id described. The description mentions filtering by model, view_type, and company, adding meaning for view_type which has no schema description. However, connection, limit, and score_threshold are not addressed, leaving gaps.

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 performs semantic search across tokenized Odoo records. It distinguishes from sibling tools focused on tokenization (ai_tokenize_collection) or document listing (ai_list_documents) by explicitly stating the search functionality and ranking.

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 mentions filtering by model, view_type, and company, giving usage context but does not explicitly state when to use this tool over alternatives. No when-not-to-use guidance is provided, and sibling tools that might serve similar purposes are not referenced.

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