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Guitarmaniac24

GetABrain🧠 | Live Human-in-the-Loop MCP for AI Agents

wait_for_responses

Read-only

Wait for human responses to a submitted query, blocking up to 50 seconds to collect enough answers, returning a complete result or a pending status to continue waiting.

Instructions

Poll for human responses to a query, blocking for up to max_wait_seconds (default 50, max 50) before returning. Use right after submit_query to wait for real answers in one call instead of manually re-checking with get_responses. DOES NOT GUARANTEE COMPLETION -- if min_responses have not arrived within the time budget it returns status "pending" (with a hint to call again) rather than erroring; call again to keep waiting. Returns status "ready" with the responses array once enough have arrived (or the query otherwise completed). Read-only / free -- cost was already charged by submit_query. Disambiguation: unlike get_responses (instant, one-shot, may return 0 responses), this actively waits, trading time for a higher chance of a complete result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idYesThe id returned by submit_query, identifying which query to wait on.
min_responsesNoHow many responses must arrive before returning status "ready" (default: the query's required_responses).
max_wait_secondsNoMaximum seconds to poll before giving up and returning status "pending" if not enough responses arrived yet (default 50, max 50).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
statusNo
responsesNo
required_responsesNo
completed_responsesNo
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description reveals non-guarantee of completion, return statuses, and cost implications. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise yet comprehensive: every sentence adds distinct value (purpose, usage, behavior, alternatives). Well-structured and front-loaded with key information.

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 polling/blocking complexity, the description fully covers behavior, return values, edge cases (timeout), and relation to sibling tools. Output schema exists, so return format explanation is optional.

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 100%, so baseline 3 applies. The description mentions defaults for max_wait_seconds and min_responses but adds little beyond schema; acceptable but not outstanding.

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 polls for human responses, blocks up to max_wait_seconds, and returns either 'ready' or 'pending'. It explicitly distinguishes from sibling get_responses by noting the active waiting behavior.

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?

Explicitly advises using immediately after submit_query and contrasts with get_responses for one-shot checks. Also mentions read-only/free status and when to call again on pending, providing clear usage boundaries.

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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