list_indicators

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context that this is a browsing/discovery tool with no side effects, and explains the scope (2600+ datasets) and filtering capabilities. No contradiction.

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 concise (3 sentences), front-loaded with purpose, and every sentence adds value. No fluff or redundancy.

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?

For a browsing tool with no output schema, the description adequately explains what the tool returns (list of indicators with metadata, filterable by topic, unit, frequency, etc.) and how it fits into the workflow (discovery before querying). Fully covers the tool's role.

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 description coverage is 100%, so the schema already documents all 7 parameters. The description adds examples of filter values (e.g., 'economy, health, energy') and emphasizes the filtering purpose, but doesn't provide substantial new semantics beyond what the schema offers. Baseline 3 is appropriate.

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 it browses the Autario indicator registry, a semantic layer over 2600+ datasets, and lists the metadata fields (topic, unit, frequency, entity_type). It directly distinguishes from search_datasets.

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 says 'Use this to discover what data is available before querying it' and contrasts with search_datasets: 'Much more precise than search_datasets when you know what topic or unit you need.' Provides clear when-to-use guidance.

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