tla_parse

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

No annotations are provided, so the description must convey behavioral traits. It explains the tool uses SANY, returns errors and module list, and optionally writes raw output to a file. This is adequate but does not detail error format or side effects (none expected for a parse tool).

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 two sentences, no fluff, with all information front-loaded. Every sentence earns its place: first states action and tool, second specifies return value.

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 parse tool with no output schema, the description sufficiently explains what the tool does and returns. However, it could be more complete by mentioning error format or that the tool may be used before other TLA+ operations.

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 baseline is 3. The description adds value by explaining that output_file causes raw SANY output to be written and the response to include output_file instead of raw_output, beyond what the schema alone provides.

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's action ('Parse and syntax-check a TLA+ module using SANY') and its output ('Returns parse errors and the list of modules parsed'). This distinguishes it from sibling tools like tlc_check (model checking) or pcal_translate (translation).

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 implies usage for syntax checking, but does not explicitly contrast with siblings or state when not to use. However, the context of sibling tool names (e.g., tlc_check, tla_evaluate) makes the purpose clear enough for an agent to select appropriately.

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