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ClintMoody

deep-think-mcp

by ClintMoody

advance_subagent_round

Advances subagent reasoning by one step, processing candidate solutions and scores through specified engines to continue multi-round thought until the budget is exhausted.

Instructions

Advance the subagent thought by one step.

    `engine="necort"`: runs the next Nash round, re-seeding the current
    best candidate (`candidate`/`scores` are ignored). `engine="manual"`
    (T13): records the current specialist's `candidate` + 7-dim `scores`
    (tolerant input -- JSON or "correctness: 0.8, ..." text) and hands the
    next specialist's prompt, or -- when the roster is exhausted -- runs
    the deterministic selection and returns the round result. Calling with
    no `candidate` at a round boundary (re)starts the next round's first
    specialist. The round budget (`subagent.max_rounds`) is enforced here.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoresNo
candidateNo
session_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must carry full behavioral disclosure. It details engine-specific actions (necort ignores candidate/scores; manual records them and may run selection) and budget enforcement. However, it does not describe side effects on session state, the structure of the returned 'round result', or error conditions. This leaves gaps in complete transparency.

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?

The description is compact yet informative, using bullet points to separate engine behaviors. Every sentence adds value, and the main action is front-loaded. A slight improvement would be to separate engine details into clearer subsections, but overall it is concise without being dense.

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

Completeness3/5

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

Given the tool's complexity (two engines, parameter nuances, budget enforcement) and the lack of annotations, the description covers essential behaviors but omits the output format (despite an output schema existing), error conditions, and prerequisites (e.g., active subagent session). It is adequate but not fully comprehensive.

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?

Schema coverage is 0% (no parameter descriptions in schema), so the description must compensate. It explains the 'candidate' and 'scores' parameters for the manual engine, including format ('correctness: 0.8, ...' text). It also clarifies that omitting candidate restarts the round. However, it does not explain the 'session_id' parameter at all, and the description of scores as '7-dim' is not reflected in the schema type (anyOf object/string/null). Partial but insufficient for full clarity.

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 action: 'Advance the subagent thought by one step.' It specifies the resource (subagent round) and distinguishes two engine modes ('necort' and 'manual'), making it clear and distinct from sibling tools like advance_stage or commit_subagent_thought.

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?

The description provides context for when to use each engine (Nash round vs. manual candidate recording), and mentions budget enforcement and round boundary behavior. However, it does not explicitly compare with sibling tools (e.g., when to use advance_subagent_round vs. commit_subagent_thought), leaving some ambiguity about alternatives.

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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