Agent Traffic Lab

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. It does list behaviors (picking, choosing, fallback, switching), but it does not disclose what the tool returns, whether it has side effects, error handling, or what 'ATL' is. The lack of an output schema amplifies this gap, leaving the tool's behavior under-specified.

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, dense run-on sentence that packs multiple actions. It is not front-loaded with the most critical information and could be broken into clearer segments. It is not overly long, but its structure hampers readability.

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 the tool's complexity (14 params, no annotations, no output schema), the description is far too high-level. It explains the general purpose but omits necessary context such as what 'task' should contain, how constraints/preferences are structured, what the decision output looks like, and any operational prerequisites. This is incomplete for effective tool selection and invocation.

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 schema has 14 parameters with 0% description coverage, so the description must compensate. It hints at 'region/policy constraints' and 'cheaper, faster, reliable, available options' which map to `constraints` and `preferences`, but it does not explain the meaning or format of any specific parameter, including the required `task`. This is insufficient for the parameter count.

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 the tool's purpose: to pick the best provider/model/service/tool, choose an execution route, find a fallback, and switch after provider failure. This is a specific verb+resource combination that distinguishes it from the sibling tool atl_outcome, as it is explicitly positioned as a pre-execution decision tool.

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 provides clear usage context: 'Before execution' tells when to use it, and the list of actions (pick, choose, fallback, switch) implies common scenarios. However, it does not explicitly mention alternatives or when not to use it, such as contrasting with atl_outcome for post-execution analysis.

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

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. It mentions the feedback purpose (improving latency, cost, and failure evidence) but does not state whether the call is idempotent, what happens on duplicate reports, whether it mutates routing state, or if special permissions are needed. These are notable gaps for a reporting tool.

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 sentence with no filler, front-loaded with the action ('Call this') and timing ('after executing an ATL decision'). Every word contributes to the core message, making it highly efficient and well-structured.

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?

The tool has 15 parameters, 3 required, no output schema, and no annotations, yet the description provides only a one-line purpose. It does not explain how to construct a valid request, what values are expected, or what happens when the tool is invoked, making it severely incomplete for a tool of this complexity.

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 description coverage is 0%, and there are 15 parameters, so the description was expected to compensate. It only hints at latency, cost, and failure concepts without mapping them to specific fields like observed_latency_ms, observed_cost, or failure_type, and it entirely ignores the three required parameters (decision_reference, outcome_correlation_token, outcome_status).

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 resource ('the real result of an ATL decision'), and the phrase 'after executing an ATL decision' clearly distinguishes it from the sibling tool atl_decide, which presumably makes the decision. This unambiguously identifies the tool's role in the workflow.

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 explicitly states when to call the tool ('after executing an ATL decision'), providing clear temporal context. It does not name the sibling tool or offer explicit exclusions, but the timing inherently separates this from atl_decide, making the usage context reasonably clear.

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