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thetacog-detect

Analyzes conversation to detect the correct cognitive room, returning room suggestion, confidence score, and switching signals.

Instructions

Analyze conversation to detect which cognitive room you should be in. Returns room suggestion, confidence score, and switching signals. Use when user says things like "let's think bigger picture" or "time to ship".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe conversation text to analyze for room detection signals
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool returns a room suggestion, confidence score, and switching signals, implying a read-only, analytical operation. However, it does not explicitly state that no state changes occur, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise, consisting of two short sentences. Every sentence adds value: the first explains the core function and output, the second provides usage examples. There is no unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description adequately covers the purpose and usage context. It explains what the tool does, what it returns, and when to use it. It lacks only details about the output format (e.g., confidence score range) but the simplicity makes the description reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for the single parameter 'text', which is described as 'The conversation text to analyze for room detection signals'. The tool description rephrases this without adding significant new semantic information, so the added value is minimal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: analyzing conversation to detect the appropriate cognitive room. It specifies the action (analyze, detect), the resource (conversation text), and the output (room suggestion, confidence score, switching signals). This distinguishes it from sibling tools like thetacog-switch (which performs a room switch) and thetacog-status (which shows status).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit examples of when to use the tool, such as when the user says 'let's think bigger picture' or 'time to ship'. It gives clear context for usage but does not specify when not to use it or mention alternatives, which would elevate the score to 5.

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

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