AI Design Blueprint Doctrine

Wait for the Next Cockpit Steer (long-poll)

me.await_steer

Read-onlyIdempotent

Pro/Teams. BLOCK until the session owner posts the next steer event to a Governed Session from the AIDB Studio cockpit, then return it. DELIVERY GUARANTEE: the durable cursor read against the session log is authoritative (at-least-once: a lost response is safely re-issuable with the same cursor, and timed_out is only returned after a final confirming read). The in-between wake-up is a best-effort in-process push: usually sub-second, but a steer is never lost if a wake-up is missed; the confirming read catches it. See the after_event_id and timeout_s parameter descriptions for the semantics. THE LOOP: finish a task -> post me.session_event handoff -> call me.await_steer -> on a steer, FIRST post me.session_event event_type=ack ('Started: '), then execute, then handoff, then call me.await_steer again; on timed_out, call again with the returned after_event_id. REQUIRES team mode on the session (toggled by the owner in the web app); owner-scoped, so foreign session ids read as not found. Read-only: this tool never writes events. REJECTION CODES (invalid_request): 'Session not found.' (not yours, or no such id); 'Team mode is off for this session.' (owner enables it on the session page). Auth: Bearer , Pro/Teams plan.

Input Schema

TableJSON Schema

Name	Required	Description
`timeout_s`	No	Seconds to wait before returning timed_out. Clamped to 5-240, DEFAULT 45: safe under Claude Code's 60-second first-response-byte timer for HTTP servers. Longer waits require the per-server timeout raised in the MCP client config (e.g. "timeout": 300000 in .mcp.json).
`session_id`	Yes	The Governed Session to watch. Must be YOURS and have team_agents enabled; list sessions via me.sessions.
`after_event_id`	No	Cursor: highest session-event id you have already seen (0 = deliver any existing steer). Pass the value from your previous await_steer result or me.sessions read. Non-destructive at-least-once delivery: re-calling with the same cursor returns the same steers again, so a lost response never loses a steer.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

The description explicitly states 'Read-only: this tool never writes events', consistent with annotations. It details delivery guarantee (at-least-once), wake-up mechanism, and rejection codes, going beyond annotation hints.

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 lengthy but each sentence adds value; it is front-loaded with purpose and loop pattern. However, it could be better structured with sections, but the detail justifies the length.

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 the tool's complexity (long-poll, cursor, delivery guarantee), the description covers all aspects: loop pattern, error codes, prerequisites, and return behavior. Output schema exists, so return values are covered. Truly complete.

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

Parameters5/5

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

All three parameters have schema descriptions (100% coverage). The description adds context: timeout_s explains default safe under Claude Code's timer and suggests increasing timeout; after_event_id explains cursor semantics and non-destructive re-issuability.

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 blocks until the next steer event is posted and returns it. It distinguishes from siblings like me.session_event and me.sessions by detailing the intended loop and contrasting with other tools.

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?

The description provides explicit usage: after finishing a task, call me.session_event handoff, then await_steer; on timed_out, re-call with returned after_event_id. It also states requirements (team mode, owner-scoped) and when not to use (foreign session IDs).

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

A4.6/5.0

Disambiguation5/5

Each tool targets a clearly distinct purpose. For example, architect.validate vs architect.validate_consensus differ in single-shot vs consensus; handoffs.agency, handoffs.operator, and handoffs.partnership are separated by engagement type. No significant overlap.

Naming Consistency4/5

Tools follow a consistent dot-notation grouping (architect.*, clusters.*, examples.*, guides.*, handoffs.*, me.*, principles.*, signals.*, team.*) with predictable verbs (validate, list, get, search, add, etc.). Minor deviation: some underscore within names (e.g., me.add_evidence) but overall pattern holds.

Tool Count4/5

24 tools is on the higher side but justified given the broad domain spanning validation, certification, learning, handoffs, and feedback. Each tool has a clear role, and the count reflects the platform's comprehensive scope without feeling bloated.

Completeness5/5

The tool surface covers the full workflow (validate → consensus → certify), discovery (principles, clusters, examples, guides), personal progress (learning path, coaching, evidence), handoffs (support, partnership, agency), feedback, and team summaries. No obvious gaps for the stated purpose.

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