ChiefLab

chieflab_record_manual_publish

USE WHEN the user has manually posted to a channel returned by chieflab_use_manual_fallback (Product Hunt / HN / Reddit / Discord / etc.) and wants to feed the live URL back to ChiefLab so the closed loop continues. Records the URL on the original publishAction (status flips from 'approved' to 'executed' with metadata.executedManually=true + metadata.publishedUrl), persists a proof_asset to the P9 company brain, and queues 24-hour metrics readback via chiefmo_post_launch_review. Without this tool, manually-posted channels are lost to ChiefLab's measurement loop.

Input Schema

TableJSON Schema

Name	Required	Description
`notes`	No	Optional. Any context on how the post performed in the first hour (early upvotes, top comment, reactions). Persists into the P9 brain.
`actionId`	Yes	The publishAction.id that the user manually published (from chieflab_use_manual_fallback). Required.
`publishedAt`	No	Optional ISO 8601 timestamp. Defaults to now.
`workspaceId`	No	Optional workspace id.
`publishedUrl`	Yes	The live URL of the manual post (e.g. producthunt.com/posts/<slug>, news.ycombinator.com/item?id=<id>, reddit.com/r/SaaS/comments/<id>).

TDQS

A4.2/5.0

Behavior5/5

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

With no annotations provided, the description fully carries the burden, disclosing all key behaviors: status flip, metadata settings, proof_asset persistence, and queuing of metrics readback. It clearly explains the tool's side effects and importance.

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 a single paragraph with a clear front-loaded usage condition. It lists actions efficiently, though some internal details (e.g., 'flips from approved to executed') could be trimmed. Still well-structured for the amount of information.

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?

Covers purpose, usage, and side effects well, but lacks description of return value or output, which is important since no output schema is provided. Also does not mention required permissions or prerequisites beyond referring to another tool.

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 100% with good descriptions. The description adds context (e.g., actionId from chieflab_use_manual_fallback, URL examples) but does not significantly enhance meaning beyond the schema. Baseline 3 is appropriate.

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?

Clearly states the tool records a manual publish URL to maintain the measurement loop, distinguishing it from sibling tools like chieflab_publish_approved_post and chieflab_use_manual_fallback by specifying the precise post-fallback scenario.

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?

Explicitly uses 'USE WHEN' to specify the condition (after manual fallback and posting). Clearly describes the need (to feed back URL for closed loop) and consequence (lost measurement without it), but does not explicitly state when not to use or list 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.

Resources

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