search_elasticsearch

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

With no annotations provided, the description carries the burden of disclosing behavior. It notes supports for Elasticsearch query features (match, term, range, bool, aggregations) and a default size of 500, which adds useful context. However, it does not mention read-only nature, error handling, rate limits, or what happens if the query is invalid, leaving some uncertainty.

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: a one-line definition, a brief use-case paragraph, and clearly labeled Args with examples. Every sentence adds value, and the format makes it easy to scan. It is appropriately sized for the tool's complexity.

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?

The tool is complex (arbitrary Elasticsearch DSL), yet the description covers use cases, parameters, and examples. An output schema is present, so return values need not be described. The inclusion of example queries and aggregations makes the description sufficiently complete 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 has no descriptions (0% coverage), but the description fully compensates by explaining each parameter: index, query_body, and size, including the effective default (500). Examples demonstrate valid query_body structures, providing meaningful guidance beyond the schema's bare type declarations.

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 runs Elasticsearch queries (JSON DSL) against download/package data, with a specific verb ('Run') and resource. It also lists concrete use cases (downloads by product, package type, etc.), which distinguishes it from sibling tools like query_clickhouse or es_list_indices.

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 explicitly says 'Use this for product download data' and lists example uses, providing clear context for when to choose this tool. It does not explicitly state when not to use it or name alternatives, but the sibling tool names (e.g., query_clickhouse) imply the boundary. Lacks explicit exclusions, so it falls shy of a 5.

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