ChiefLab

chieflab_redraft

USE WHEN the user says 'make this more casual' / 'shorter' / 'less salesy' / 'add a specific number' / 'redraft this' about a published draft. Pulls the existing brief for the actionId, merges the instruction, returns an updated brief your LLM should render. After rendering call chieflab_record_rendered_copy({ assetId, body }) to commit. Multi-turn flow stays coherent: each redraft increments action.metadata.revision; original brief preserved in metadata.history.

Input Schema

TableJSON Schema

Name	Required	Description
`body`	No	Optional. If your LLM already rendered the redrafted body, pass it here and ChiefLab updates the action's brief directly.
`actionId`	Yes	The publishAction.id from chiefmo_launch_product.
`instruction`	Yes	Plain-English instruction. 'shorter', 'more casual', 'add the $40 number from our pricing page', etc.
`workspaceId`	No

TDQS

A4.7/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: pulls the existing brief for the actionId, merges the instruction, returns an updated brief for the LLM to render, and explains the multi-turn flow (revision increments, original brief preserved in history). This provides rich behavioral context beyond a simple function call.

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 front-loaded with the usage trigger ('USE WHEN') and is concise at 4-5 sentences. Every sentence adds value: usage context, workflow steps, and behavioral notes on multi-turn coherence. No wasted words.

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?

The description explains the full flow (pull, merge, return, render, commit) and post-conditions (increment revision, preserve history). However, it does not mention error cases or prerequisites (e.g., that the draft must already be published). Given no output schema, this is a minor gap.

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

Parameters5/5

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

Schema coverage is 75%, but the description adds significant meaning: it explains that instruction should be plain-English ('shorter', 'more casual'), body is optional if already rendered, and actionId comes from chiefmo_launch_product. This goes beyond the schema's descriptions and clarifies usage.

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 explicitly states the tool is for redrafting a published draft when the user requests changes like 'make this more casual' or 'shorter'. It clearly identifies the verb (redraft) and resource (published draft), distinguishing it from sibling tools like 'chieflab_regenerate_visual_asset' which handles visuals.

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 starts with 'USE WHEN' and lists specific user phrases (e.g., 'make this more casual', 'shorter') that trigger this tool. It also provides follow-up steps (call chieflab_record_rendered_copy) but does not explicitly state when not to use it or exclude 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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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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