Screwtape.ai

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not describe return format, what false-consensus risk entails, permissions, or mutation/read semantics.

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?

One front-loaded sentence with no wasted words. However, for a three-parameter tool with no annotations, the structure is under-developed rather than fully polished.

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?

No annotations, no output schema, and low schema coverage leave major gaps: task and retain semantics, return values, and usage context are all missing. The single sentence is insufficient for this tool's 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 33%, with only the answers parameter documented. The description does not explain the task or retain parameters and adds no syntax or format detail beyond the minimal schema text.

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 action: score agreement across several agents' answers and flag false-consensus risk. Clear verb+resource, but does not differentiate from the sibling judge or clarify what consensus scoring adds over judging.

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?

No explicit when-to-use guidance, prerequisites, or alternatives. The description implies a scenario with multiple agents, but does not say when to use this tool versus judge.

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 behavioral burden, yet it discloses nothing about determinism, cost, latency, or what happens to the inputs. The retain/API-key behavior exists only in the schema, not the description, so the description adds no behavioral context beyond the return shape.

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?

A single front-loaded sentence with no filler; the verdict domain and return payload come first. It is efficient, though the brevity contributes to the coverage gaps elsewhere rather than earning extra credit.

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?

Five parameters at 20% schema coverage, no annotations, and no output schema means the description should do much more. It hints at the return contents (score, reasons) but leaves criteria, evidence, and the retain behavior unexplained, which is inadequate for a judgment tool with this many inputs.

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 only 20% (just 'retain'), so the description must compensate for task, criteria, evidence, and agent_output, and it does not. It implies score and reasons are outputs but never explains what 'criteria' or 'evidence' inputs mean or how they affect the verdict.

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 ('Return') and resource ('verdict for one agent output'), and even enumerates the verdict domain (pass, fail, retry) plus the payload (score, reasons). The phrase 'one agent output' hints at the single-output scope, but it never names or contrasts the sibling 'consensus', so differentiation is left implicit.

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 or when-not-to-use guidance and no named alternative. An agent must infer from 'one agent output' that this is the single-output counterpart to 'consensus', which is a guess rather than guidance.

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