read_results

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

With no annotations, the description carries the full burden and does well: it discloses the 50-series cap, the even thinning behavior driven by max_points, the row-0-is-initial-values convention, and the constraints start <= end and max_points >= 1. It omits error behavior (missing/uncompiled model) and confirms nothing explicit about read-only safety, which keeps it from a 5.

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?

Front-loaded with the core action, then dense per-parameter guidance with no filler. Every clause carries usable information; the only minor cost is that the parameter notes run together as a long semicolon chain rather than being visually separated.

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 6-param tool with no annotations and no output schema, the description covers the time-axis semantics, filtering, thinning, and the return-size limit. It does not describe the shape of the returned series, which is the main remaining gap given the absence of an output schema.

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 0%, so the description must compensate, and it explains variables (name or name+instance), start/end (row number = time step, row 0 = initial, start <= end), and max_points (thinning target, >=1). Only 'model' and 'results_file' lack explicit semantics, though both are largely self-evident from the opening sentence.

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?

Opens with a specific verb+resource: 'Read time series from one result file (.res.gz) in the model folder.' The file type and location are concrete enough to distinguish it from siblings like read_equations or list_models. It stops short of explicitly naming an alternative tool, but the purpose is unambiguous.

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?

Usage is implied (inspect simulation output stored in a .res.gz file), but there is no explicit when-to-use/when-not guidance and no named alternatives among the many siblings. An agent can infer the context but must reason about it rather than being told.

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