squire

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

With no annotations provided, the description carries the full burden. It successfully conveys 'disposable remote runtime' (indicating ephemeral state) and available libraries, but lacks critical behavioral details for a code-execution tool: isolation guarantees, side effects, network access, or how outputs are captured (beyond the schema parameters).

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?

A single 16-word sentence that front-loads the action ('Run Python...') and efficiently packs in the runtime characteristics ('disposable remote') and capabilities ('pandas, polars, and pyarrow'). Zero waste, 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 5-parameter code execution tool with no output schema and no annotations, one sentence is minimally sufficient. It covers the execution environment and available libraries, but lacks completeness regarding safety model, artifact lifecycle, or execution guarantees that would be expected for arbitrary code execution.

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 coverage is 100%, establishing a baseline of 3. The description adds crucial semantic context for the 'script' parameter by specifying the available data-processing libraries (pandas/polars/pyarrow), which informs the agent what kind of Python code can successfully execute. However, it does not elaborate on input/output mechanics despite the schema being present.

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 provides a specific verb ('Run') plus resource ('Python data-processing jobs') and clearly distinguishes from siblings like 'sql', 'solve', and 'quantum_simulate' by specifying the pandas/polars/pyarrow data-processing environment. It establishes both the action and the specific domain.

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?

No explicit when-to-use or alternatives are listed, but the mention of pandas, polars, and pyarrow provides implied usage guidance—suggesting this tool is for DataFrame-based data manipulation rather than general computation or SQL queries. However, it lacks explicit guidance for choosing between this and siblings like 'sql' or 'solve'.

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