ofp_model_provenance

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

Annotations already communicate readOnlyHint, idempotentHint, and destructiveHint=false. The description adds useful context about deployment scoping and the 'free' aspect, but does not go further into behavior like rate limits, freshness guarantees, or what happens if the model is unavailable. This is adequate given the annotations.

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?

Two sentences with no waste. The first sentence states what it returns, and the second gives the usage scenario. Key information is front-loaded.

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?

Given zero parameters, rich annotations, and no output schema, the description fully covers what the tool returns and why to use it. There are no obvious missing pieces for an agent to call it successfully.

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 and schema coverage is 100%, so the baseline is 4. The description adds no parameter details because none are needed; it correctly focuses on output meaning instead.

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 starts with a specific verb ('Show') and a specific resource ('which snapshot of the Open Footprint standard this deployment serves'), and lists concrete outputs: source commit, date, entity/sector counts. This clearly distinguishes it from sibling tools like ofp_models or list_standards by focusing on deployment provenance rather than model listing.

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?

It gives an explicit usage condition: 'Use it when you need to know how current the model you are designing against is.' This provides clear context for when to call the tool, though it does not mention alternatives or when not to use it.

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