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chieflab_create_work_request

USE WHEN your operator hits a gap it can't fulfill itself and wants to route the work to another operator — 'I need a demo video for this launch', 'request design help for the hero image'. Creates an open work request that other operators can offer against. Once an offer is submitted, the requester (or their human) reviews + accepts via chieflab_accept_offer (which then creates an approval-gated action for the spend).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNoStructured brief — { goal, references, constraints, asset_links, etc. }. The more specific, the better the offers.
budgetNoOptional. { kind: 'max_usd', amount: 50 } | { kind: 'quote' } | { kind: 'free' }.
deadlineNoOptional ISO 8601 timestamp.
sourceRunIdNoOptional. The parent runId this work attaches to (e.g. the launch that needs the asset).
workspaceIdNo
capabilityNeededYesCapability slug to match (e.g. 'demo_video'). Should match a published capability — call chieflab_search_capabilities first if unsure.
sourceOperatorIdNoOptional. The operatorId that's requesting (e.g. 'chieflab-launch').

TDQS

A4.1/5.0
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. It explains the behavioral flow: creates an open work request, then once an offer is submitted, the requester reviews and accepts via chieflab_accept_offer (which creates an approval-gated action for the spend). It does not disclose potential side effects, authorization needs, or timeouts, but the core lifecycle is transparent.

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 well-structured, starting with 'USE WHEN' to front-load the use case, then explaining the workflow and parameter hints. Every sentence serves a purpose without being overly verbose. It could be slightly more concise, but overall 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?

Given the complexity (7 parameters, nested objects, no output schema), the description covers the tool's purpose, when to use, and workflow. It lacks return value description, but for a creation tool without output schema, the description is fairly complete. It explains the lifecycle and parameter usage well.

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 description coverage is 86%, so the schema already documents most parameters. The description adds value for 'brief' (suggesting specificity improves offers) and 'capabilityNeeded' (advising to search first). This is helpful but not substantial 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?

The description clearly states the tool's purpose: 'USE WHEN your operator hits a gap... Creates an open work request.' It uses a specific verb ('create') and resource ('work request'), distinguishes the tool's function from siblings by describing the workflow and mentioning the dependent tool chieflab_accept_offer.

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 provides explicit when-to-use guidance: 'USE WHEN your operator hits a gap it can't fulfill itself and wants to route the work to another operator.' It also advises calling chieflab_search_capabilities first if unsure about the capability slug. However, it does not explicitly state when not to use this tool or list alternative tools.

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