ChiefLab

chieflab_set_publishing_targets

P79 — set per-workspace publishing target defaults so chiefmo_approve_action({ autoExecute: true }) doesn't need the agent to pass platform / recipient ids on every call. One-time setup per workspace. channelTargets is a map { linkedin: { accountId }, x: { accountId }, email: { fromEmail, recipientListId } }. Pass partial maps to update specific channels; pass null for a channel value to remove it. Persisted via deps.publishingTargetsStore when wired, otherwise in-process Map (Vercel function lifetime). Returns the merged channelTargets + storage location ('persistent' or 'in_memory').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`workspaceId`	No	Optional workspace id (defaults to the auth workspace).
`channelTargets`	Yes	Map of channel → target. Examples: linkedin/x → { accountId: '<zernio_account_id>' }; email → { fromEmail: 'founder@product.com', recipientListId: '<list_id>' }. Pass null as a channel value to remove it.

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 fully carries the behavioral disclosure burden. It reveals that storage may be in-memory (Vercel function lifetime) or persistent depending on wiring, and states the return format includes storage location. This is comprehensive and honest.

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 four sentences with no wasted words. It starts with a clear purpose (P79 — set...), then explains the 'why', then details parameter behavior, then storage and return. Front-loaded and efficient.

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 no output schema, the description adequately explains the return value (merged channelTargets + storage location). It covers purpose, parameter semantics, storage behavior, and usage nuance. No gaps for the tool's complexity.

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%, but the description adds value beyond schema by explaining that partial maps update specific channels (not in schema) and describing the return value format. The schema already mentions null removal, so description complements it.

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 verb 'set' and resource 'publishing target defaults' with scope 'per-workspace'. It explains why the tool exists (to avoid passing IDs on every call) and distinguishes it from the sibling 'chieflab_get_publishing_targets' (which retrieves).

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 says 'One-time setup per workspace' and implies this tool is for initial configuration or updates. It mentions partial maps and removal via null, guiding usage. However, it does not explicitly state when not to use it or compare to alternative tools, though context from sibling names suggests get_publishing_targets for reading.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ChiefLab tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?