es_count

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 discloses that the tool performs a count, which is a safe read operation. However, it does not elaborate on any edge cases, error behavior, or response structure, which could be beneficial.

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 a single sentence with no filler words. It efficiently conveys the core functionality and the optional nature of the query. Every word earns its place.

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 simple count tool, the description covers its primary behavior. However, it omits details about the output format (e.g., returns a number, structure of response) and does not mention potential pitfalls like timeouts or behavior with no matching indices. With no output schema, this gap is noticeable.

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 describes all parameters. The description adds value by providing an example for the 'query' parameter ('{ "match": { "title": "foo" } }') and noting the default match_all behavior. This aids understanding beyond the schema.

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?

Description clearly states the tool counts documents in an index, optionally filtered by a query. The verb 'count' and resource 'documents in an index' are specific, and it immediately distinguishes from sibling tools like es_search which return documents, not counts.

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?

Description explicitly states when to use: to count documents matching a query or all documents. It implicitly contrasts with fetching tools, but no explicit exclusions or alternatives are mentioned. Still, the usage context is clear.

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