search_tables

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

With no annotations provided, the description fully covers behavioral traits: it searches both names and descriptions, uses case-insensitive matching, supports partial matches, returns results from all spaces or a specific space when filtered, and includes table metadata (type, columns, row counts). This gives the agent a strong understanding of the tool's runtime behavior.

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?

The description is well-structured with clear headers for use cases, behavior, tips, examples, and next steps. It is longer than strictly necessary, but each section serves a distinct purpose and the front-loaded summary makes the core meaning immediately 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?

Given a simple 2-parameter search tool with no output schema, the description is comprehensive: it explains what the tool does, when to use it, how search works, provides practical tips, example queries, and next steps. It leaves little ambiguity for an agent to select and invoke the tool 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?

The input schema already provides 100% parameter descriptions for space_id and search_term with examples. The description adds extra semantic value by explaining search behavior (case-insensitive, partial matches) and offering search tips like 'Partial matches work: cust finds CUSTOMER_DATA.' This goes beyond the schema but is not essential given the schema's completeness.

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 opens with a specific action and target: 'Search for tables and views across all Datasphere spaces by name or description.' This clearly defines the tool's scope and differentiates it from siblings like get_table_schema (single table) or list_catalog_assets. The example queries reinforce the purpose.

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?

A dedicated 'Use this tool when' section lists four concrete scenarios, such as 'User asks Find tables with customer data' and 'Searching across multiple spaces.' It provides clear context for when to use the tool, though it does not explicitly mention when not to use it or name alternative tools for different search types.

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