ChiefLab

chieflab_suggest_next_move

P75 — Next Move Engine. USE WHEN measurement just came in (chiefmo_post_launch_review fired automatically at 24h, OR the user manually called it) and you want to know what the operator should do next. Reads metrics + the original launch's brief and emits a deterministic suggestion: {kind: 'follow_up_email' | 'founder_dm' | 'thread_reframe' | 'landing_iteration' | 'lessons_learned_post' | 'seo_title_test' | 'wait' | 'noop', priority, reasoning, draftBrief: {channel, headline, body, cta?, recipients?}, measurementGroundingFacts}. Pure-function; same input → same output, no LLM. Pair with chieflab_create_next_move_action to turn the suggestion into an approval-gated draft.

Input Schema

TableJSON Schema

Name	Required	Description
`runId`	No	Optional. Source run whose brief grounds the suggestion (so the draft references the actual product / market / audience instead of templates).
`channel`	Yes	Channel the measurement is for (linkedin, x, email, landing_hero, product_hunt, hacker_news).
`metrics`	Yes	Measurement metrics — engagements, clicks, opens, replies, traffic, conversions, upvotes, rank, etc. Synonyms (engagement / openRate / clickRate / visits) are accepted.
`workspaceId`	No	Optional workspace id.

TDQS

A4.9/5.0

Behavior5/5

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

Despite no annotations, the description fully discloses behavioral traits: it is a 'Pure-function; same input → same output, no LLM,' meaning deterministic and side-effect-free. It also describes the output structure in detail (kind, priority, reasoning, draftBrief, measurementGroundingFacts), so the agent knows exactly what to expect.

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 well-structured and front-loaded: starts with the identifier 'P75 — Next Move Engine' and immediate usage condition. Every sentence adds necessary information without redundancy. It efficiently covers purpose, trigger, behavior, output format, and pairing with another tool.

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

Completeness5/5

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

Given the tool's complexity and lack of output schema, the description provides a comprehensive view: input conditions, deterministic nature, output structure, and relationship to sibling tools. No gaps remain for an agent to understand when and how to use this tool effectively.

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%, so baseline is 3. The description adds value by explaining the semantics of each parameter beyond the schema: runId 'grounds the suggestion' to actual product/market/audience, channel lists examples, and metrics 'accepts synonyms' like engagement/openRate. This extra context justifies a score above baseline.

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: 'Next Move Engine' that suggests what the operator should do next after measurement. It specifies the trigger condition (measurement just came in) and distinguishes from sibling tools like chieflab_create_next_move_action by mentioning the pairing relationship. The verb 'suggest' and the resource 'next move' are specific and unambiguous.

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

Usage Guidelines5/5

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

Explicit usage guidance: 'USE WHEN measurement just came in' with specific references to automatic firing of chieflab_post_launch_review or manual user call. It also tells what to do after obtaining the suggestion: 'Pair with chieflab_create_next_move_action to turn the suggestion into an approval-gated draft,' which provides clear next-step context.

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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TDQS

A3.7/5.0

Disambiguation4/5

Most tools have distinct purposes, e.g., approve_action vs execute_approved_action vs publish_approved_post. However, alias overloading (e.g., chieflab_launch_product and chieflab_get_users_after_build pointing to the same handler) introduces some ambiguity. The detailed descriptions mostly mitigate confusion, but an agent might still struggle to choose between near-identical aliases.

Naming Consistency4/5

Tools predominantly follow a 'chieflab_verb_noun' pattern (e.g., chieflab_approve_action, chieflab_connect_provider). A few exceptions exist (chieflab_help, chieflab_inbox, chieflab_boot) that are single nouns, but these are clearly distinct and the overall consistency is high.

Tool Count3/5

32 tools is on the high side for an MCP server, but the domain of a growth/marketing launch platform naturally requires many operations (launch, approve, execute, measure, iterate, connect providers, etc.). The count is borderline but still manageable; it doesn't reach the 50+ extreme.

Completeness4/5

The tool set covers the full launch lifecycle: create, approve, execute, measure, and iterate. It includes provider connections, manual fallback, brain summary, and work requests. Minor gaps exist (e.g., no explicit tool for deleting a launch or revoking approval), but core workflows are fully supported.

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