Repstack.Ingest Log

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

With no annotations provided, the description carries the full burden, and it does well: it explicitly discloses statelessness, the shape of the response, and the presence of meta.llm_available and meta.llm_used. It does not mention failure modes or exact behavior when allow_llm is false, but the key behavioral traits are disclosed.

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?

Two sentences, front-loaded with the core action and return value, followed by the most important behavioral and configuration notes. Every sentence earns its place and there is no redundant wording.

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 description covers the input formats, the stateless contract, the LLM requirement, and the key response fields. With an output schema present and a single flexible parameter, this is mostly complete, though the vague payload semantics prevent a perfect score.

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 only one payload object with no property descriptions and 0% schema coverage, so the description must compensate. It partially does by mentioning allow_llm=true and the three input formats, but it does not specify the exact payload structure or how text/CSV/JSON content should be placed inside the payload. This leaves significant room for ambiguity.

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 verb 'Ingest' and a clear resource ('a workout log'), explicitly listing accepted formats (text, CSV, JSON) and the expected output (canonical structured JSON, issues, summary). It also differentiates itself from the sibling tools by framing itself as the ingestion step versus compute_metrics and search_exercises.

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 provides clear usage context: it is stateless, and text inputs require allow_llm=true along with an LLM parser configuration. However, it does not explicitly state when to prefer this tool over its siblings or include when-not-to-use guidance, so it falls short of a perfect score.

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