execute

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

With no annotations, the description carries the full burden of behavioral disclosure, and it is exceptionally transparent. It reveals that execution happens in a remote sandbox ('not here'), restricts the available function surface to only call_tool, lists supported imports, forbids collections, disables str.format, and states hard limits on time, memory, and recursion depth. This is far beyond a generic 'executes code' statement.

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 dense but every sentence carries needed operational detail. The opening 'Catalog entry.' is mild noise, but the rest is front-loaded with the location and primary action, followed by requirements and limits. The length is justified by the number of constraints the agent must respect to successfully invoke the tool.

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 1-parameter code-execution tool with no output schema and no annotations, the description covers all known operational aspects: where code runs, the callable API, allowed imports, forbidden syntax, and hard limits. It even states that the code must return the final value. Nothing an agent needs to call it correctly is missing.

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?

Although the schema already defines the code parameter (100% coverage), the description adds substantial semantics: the exact async style (await call_tool), the required use of f-strings instead of str.format, the allowed/intended import set, the function signature, and the resource limits. This tells the agent exactly how to write valid code and exceeds 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 what the tool does: it runs Python code in the user's Kuudo deployment, not in the AI's environment, specifically for chaining await call_tool(...) calls. The verb 'Chain' and the resource 'your Kuudo deployment' make the purpose concrete and distinguish it from the sibling tools (tags, search, get_schema), which are direct data-retrieval tools.

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 explains when to use this tool: when you need to compose multiple call_tool invocations in a single Python block and return a final value. It does not explicitly state when not to use it or name alternatives, but the use case is unambiguously conveyed, so clear context is provided even without exclusions.

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