Subindex MCP Server

wait_for_conversation

Read-only

Polls a conversation until the AI finishes its turn, providing live synthetic progress updates. Returns when user input is needed, approval is triggered, or a timeout occurs.

Instructions

Wait for a conversation turn to complete, emitting live synthetic progress.

Preferred over manually polling get_conversation. Since conversation processing has no native progress signal, this tool emits time-based synthetic progress — advancing quickly at first, then slowing as it approaches expected_seconds — so the MCP host shows a "still thinking" indicator rather than a frozen bar.

Returns when any of these conditions are met: user_reply_needed=True → AI asked a question; call send_conversation_reply trigger_execution=True → AI approved execution; preview is auto-queued, switch to wait_for_job(session_id) Non-processing status → unexpected terminal (inspect status field) Timeout → returns last known state with _wait_timeout note

Applies to all conversation types: upload interview, table-maker interview, config refinement.

expected_seconds: typical AI response time for this turn (default 120). First table-maker turn (research + planning): ~120–180s. Upload interview first turn (CSV analysis + plan): ~90–150s. Follow-up confirmations ("yes, proceed"): ~30–60s. poll_interval: seconds between status checks (default 8). timeout_seconds: max wall time before returning (default 900). Upload interview turns can take up to 15 minutes — set accordingly.

Input Schema

TableJSON Schema

Name	Required	Description
`conversation_id`	Yes	Conversation ID to wait on.
`session_id`	Yes	Session ID associated with the conversation.
`expected_seconds`	No	Expected AI response time in seconds — used to shape synthetic progress curve (default 120).
`timeout_seconds`	No	Maximum wall-clock seconds to wait before returning last known state (default 900).
`poll_interval`	No	Seconds between status poll cycles (default 8).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Tool Definition Quality

A4.8/5.0

Behavior5/5

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

The description discloses the synthetic progress behavior, timing curve, and detailed return conditions. Annotations already indicate readOnlyHint=true, and the description adds significant behavioral context beyond that.

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 thorough and well-structured with bullet points for return conditions and parameter guidance. Although lengthy, every sentence adds value and the organization aids readability.

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 output schema exists, the description covers all necessary aspects: purpose, usage, parameter guidance, return conditions, and scope. No gaps remain for effective tool selection and invocation.

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?

While the input schema covers 100% of parameter descriptions, the description adds contextual guidance (e.g., expected_seconds ranges for different turn types) that enhances understanding beyond the schema.

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 waits for a conversation turn to complete and emits synthetic progress. It specifies return conditions and distinguishes itself from manually polling get_conversation.

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 explicitly recommends this over polling get_conversation, provides context for all conversation types, and gives detailed guidance on expected_seconds values based on turn type.

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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