ai_chat

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

With no annotations, the description carries the full burden, and it does disclose two genuinely useful traits: max_tokens is capped at 4096 per call and usage is metered (billable). It does not cover auth requirements, rate limits, error behavior, or what happens when a model ID is invalid, which leaves meaningful gaps for a billed network call.

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?

Three short sentences, front-loaded with what the tool does, followed by the two constraints that matter (token cap, metering). No filler, no repetition of the schema.

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 simple four-parameter chat proxy with no output schema, the description covers the return shape (reply + token usage) and the token ceiling. It is still missing model discovery, temperature guidance, and failure modes, so an agent can call it but not fully predict behavior.

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 only 25% — just the 'model' field has a description, and it is circular ('Model ID to call'). The description adds nothing about the messages array structure, role semantics, or temperature behavior, and its 4096 cap restates the schema's maximum rather than adding meaning. Low coverage with no compensating detail puts this below the 3 baseline.

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?

States a specific verb and resource: calls the Badgr AI API as an OpenAI-compatible chat completion and returns the reply plus token usage. That is unambiguous and clearly distinct from the deployment/lifecycle siblings (deploy_model, badgr_run, check_result). It stops short of explicitly naming where an alternative might apply, which keeps it at 4 rather than 5.

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 when-to-use guidance and no exclusions. Nothing tells the agent when to prefer this over launch_agent_task or deploy_model, nor what prerequisites exist (model availability, credentials). The 'OpenAI-compatible chat completion' phrase only implies usage; the agent must infer it.

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