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Start AI Execution

start_ai_execution

Start a durable AI execution to process a previously delivered context package. Use after confirming context delivery and before recording consumption.

Instructions

Use when: Start the durable AIExecution that will consume an already delivered ContextPackage. The API, not MCP, owns authorization and may return a request-scoped execution grant; MCP must pass that result through without interpreting billing or payment state. Call this after confirm_context_delivery and before record_context_consumption. Do not retry automatically because this creates a new execution. Do not use when: a narrower tool better matches the intent, the project scope is unresolved, or the user has declined the action. Requires: authenticated API authority and an API-authorized selected Project. Effect: canonical mutation. This mutation has no client idempotency key in the current contract; do not retry it automatically after an unknown outcome. Human approval: not required for this read or staging action. Then: follow typed result state; review pending proposals/drafts before any canonical apply. On failure: login_required → login; project_not_selected → list_projects/select_project; permission_denied → stop; stale_version or conflict → read current state; projection_pending → report canonical success separately and wait.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
statusNo
taskIdYes
providerNo
projectIdNoOptional project id. Defaults to the project chosen with select_project. If omitted and no project is selected, the call is rejected with instructions to select one.
sessionIdNo
connectionIdNo
inputSyncReceiptIdsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
statusYes
warningsYes
authorityYes
projectIdYes
reviewUrlYes
idempotencyYes
nextActionsYes
reviewRequiredYes
canonicalVersionYes
projectionStatusYes
Behavior5/5

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

Discloses the mutation nature (no readOnlyHint), lack of idempotency, authorization delegation to API, and no human approval needed, adding value beyond annotations.

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?

Well-structured with clear sections, but somewhat verbose; sections like 'Human approval' could be more concise given annotations already cover this.

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?

Covers failure modes and workflow placement well, but lacks description of return value structure and parameter details, given the tool's complexity.

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

Parameters2/5

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

With only 13% schema description coverage, the description does not explain the meaning of critical parameters like taskId or inputSyncReceiptIds, leaving a significant gap.

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 'Start the durable AIExecution that will consume an already delivered ContextPackage' and positions it between confirm_context_delivery and record_context_consumption, clearly differentiating it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Includes explicit 'Use when' and 'Do not use when' clauses, retry warnings, and a structured 'On failure' section covering common errors, providing comprehensive guidance.

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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