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thetacog-next

Predict next steps across nine rooms by synthesizing chat transcripts, repository logs, and board punch lists. Returns structured JSON with optional narration and HTML rendering.

Instructions

Predict the next steps across all 9 rooms from THREE real sources — chat transcripts (read backwards, bounded), the repo + git logs per each room's owned file/dir surface, and the board (room punch lists). EVERY room is filled with a ≥2-sentence themed narrative (the direction + the actual area); the current room gets the exhaustive narrative + a numbered actionable to-do list. This is the subdivision substrate the mesh runner uses for inter-room contracts + task hand-off. Returns structured JSON; set render=true to also get TTS narration + HTML.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roomNoThe current room/seat to lead with: builder, architect, operator, vault, voice, laboratory, performer, navigator, or network. Defaults to navigator.
renderNoAlso return rendered narration (for premium TTS) + HTML in the response.
repo_rootNoAbsolute path to the thetadrivencoach repo root. Auto-located via cwd / git if omitted.
Behavior3/5

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

No annotations present, so description bears full burden. It discloses data sources and optional rendering, but does not mention authorization, rate limits, or side effects like whether this is a read-only operation. 'Predict' implies read but is not explicit.

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

Conciseness4/5

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

Description is relatively concise given complexity, with key information front-loaded. All sentences contribute meaning, though some phrasing ('subdivision substrate') could be clearer.

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?

Inputs are well-covered; output format described as structured JSON with optional narration. Without an output schema, this is adequate. The tool's role in inter-room contracts is noted, adding system context.

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

Parameters4/5

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

Schema coverage is 100%. Description adds value by explaining defaults (room defaults to navigator) and auto-location for repo_root, and clarifies the effect of render flag, going beyond the schema's descriptions.

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

Purpose4/5

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

The description clearly states the tool predicts next steps across 9 rooms using three specific sources (chat, repo/git, board). It distinguishes itself from sibling tools by its specific function, though it does not explicitly contrast with them.

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

Usage Guidelines3/5

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

Provides some usage context (e.g., render flag for TTS/HTML), but no explicit guidance on when to use this tool versus alternatives or conditions requiring exclusion. The mention of 'subdivision substrate' adds context but is vague.

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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