check_computation_capabilities

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

With no annotations provided, the description carries the full burden. It discloses that the tool checks two distinct capabilities and that it reports availability, which implies a non-mutating read operation. However, it does not describe what happens when capabilities are missing, whether there are side effects, or how detailed the report is.

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 a single, front-loaded sentence that conveys exactly what the tool checks. There is no redundant information, filler, or repetition of the tool name.

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 zero-parameter capability check with an output schema available, the description fully answers what the tool does. It is simple enough that no further detail about return values or invocation is required to use it correctly.

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 tool has zero parameters, so the parameter schema is trivially complete and the description does not need to explain parameter meaning. The baseline for zero-parameter tools is 4, and nothing in the description detracts from that.

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 uses a specific verb ('Report') and clearly identifies the two resources being checked: local Python and the fixed R/eRm Rasch engine. This distinguishes the tool from its analysis-focused siblings, which perform data inspection, statistics, or modeling rather than capability checks.

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?

There is no explicit guidance about when to call this tool versus the sibling tools. While it is implicitly a preflight check before using Python-dependent or R/eRm-dependent tools, the description does not state this or list any alternatives or exclusions, leaving the agent to infer usage context.

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