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chieflab_request_specialist

P87 — delegate to a specialist agent. USE WHEN the launch needs more than copy: a graphic brief (kind='design'), a demo video shot list (kind='video'), a prospective-customer research plan (kind='research'), personalized outreach drafts (kind='outreach'), landing-page SEO recommendations (kind='seo'), or a verdict + recommended next move from measurement data (kind='analytics'). Specialists are deterministic stubs today — they return structured briefs / plans / drafts the founder can act on immediately. Future LLM-backed implementations swap in transparently. Each request creates an approval-gated action so the brief lands in the Launch Room. Pass runId for grounding in the originating launch's intelligence brief.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesdesign | video | research | outreach | seo | analytics
briefNokind-specific brief fields (asset/channel for design; durationSeconds/platform for video; goal for research; contacts/channel for outreach; url/targetTerms for seo; metrics/channel for analytics)
runIdNoOptional. Source run id; the specialist grounds in that launch's brief (productName / audience / market / positioning).
workspaceIdNoOptional workspace id.
autoCreateActionNoDefault true. When true, creates an approval-gated action in actionStore so the brief lands in the Launch Room. When false, returns the specialist output without persistence (use for one-off / preview).

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that specialists are deterministic stubs today, that each request creates an approval-gated action landing in the Launch Room, and that `runId` grounds output in the originating launch brief. It does not mention auth requirements or exact output envelope details, but the key side effects are covered.

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 front-loaded with the core purpose and uses a scannable 'USE WHEN' pattern with the kind list. It is slightly longer than necessary due to the 'Future LLM-backed implementations swap in transparently' sentence and some redundancy with the `autoCreateAction` schema description, but still organized and efficient.

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?

For a 5-parameter tool with a nested `brief` object and no output schema, the description covers purpose, use cases, deterministic behavior, side effects, and runId grounding. It could more explicitly state the return shape beyond 'structured briefs / plans / drafts' and mention error cases, but the provided information is sufficient for an agent to select and invoke correctly.

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 the baseline is 3. The description adds meaning by translating the `kind` enum values into concrete real-world artifacts and by explaining the purpose of `runId`—grounding in the launch brief. It relies on the schema for `brief`'s internal structure, but that's acceptable given the schema descriptions are already detailed.

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 names the resource ('specialist agent') and uses a specific verb ('delegate'), then maps six `kind` values to concrete deliverables (graphic brief, video shot list, research plan, outreach drafts, SEO recommendations, analytics verdict). This clearly distinguishes it from sibling tools like `list_specialists` or `create_work_request` by scoping the purpose to specialist delegation.

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 opens with 'USE WHEN the launch needs more than copy' and enumerates specific scenarios per `kind`, giving an agent clear conditions to invoke the tool. It does not explicitly name alternative tools or provide when-not-to-use guidance, so it lacks full exclusion semantics.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., multiple launch and measurement tools), and the presence of aliases like chiefmo_* can confuse an agent. While descriptions are detailed, the sheer number of tools covering similar workflow steps makes it hard to distinguish the right one without careful reading.

Naming Consistency4/5

All public tools start with 'chieflab_' and mostly follow a verb_noun pattern (e.g., chieflab_approve_action, chieflab_set_company_profile). However, some internal names like 'chiefmo_*' appear as aliases, which slightly breaks consistency from the user's perspective.

Tool Count1/5

45 tools is excessive for a single MCP server. While the domain is broad, this many tools likely overwhelms agents and users. Most well-scoped servers have 3-15 tools; 45 indicates poor scoping and should be split into focused sub-servers.

Completeness5/5

The tool surface is extremely comprehensive, covering the full lifecycle from signup and installation to launch, approval, execution, measurement, next-move suggestions, manual fallback, and specialist delegation. No obvious gaps for the stated purpose of growth and distribution.

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