find_relevant_tables

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

Annotations already convey safety (readOnly, idempotent). Description adds critical behavioral details: returns cosine-ranked hits, shows matched column and its LLM description, and reveals the embedding model used. These details help the agent understand the return format and reasoning, beyond what annotations provide.

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?

Three sentences, front-loaded with primary use case, no redundant information. Every sentence adds distinct value: purpose, differentiation, and composition advice. Highly efficient.

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?

Despite the presence of an output schema (not shown), the description explains key return elements (cosine scores, matched column, LLM description) and provides composition chains with sibling tools. Given the tool's moderate complexity and 11 siblings, the description fully equips the agent to decide and invoke correctly.

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?

Input schema covers both parameters with good descriptions, achieving 100% coverage. The description adds valuable usage guidance for the limit parameter ('Use a small value (3-5)...'), enhancing the agent's ability to select appropriate values. This extra context raises the score above baseline 3.

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?

Clearly states the tool's purpose: 'Use this when the user describes tables semantically' with concrete examples ('the table with customer orders'). Directly distinguishes from sibling tool `describe_table`, making purpose unmistakable.

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?

Explicitly specifies when to use ('semantic descriptions'), when to use an alternative ('Use describe_table instead when the user names a specific table'), and suggests multi-step compositions with `describe_table` and `suggest_joins`. No ambiguity.

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