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quantumproxies

QuanticData MCP server

run_collector

Execute a data collector by slug and input to retrieve structured rows or a run_id for async polling. Pay only for delivered results, with zero cost for empty runs.

Instructions

Run one Collector with its semantic input. Short runs return the rows directly; long runs return a run_id to poll with collector_run_status. Billed per delivered row — zero rows costs zero.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCollector slug, e.g. google_maps_places. Call list_collectors first if unsure.
asyncNoForce background processing and return a run_id immediately.
inputYesThe collector's input, matching its published input_schema.
Behavior4/5

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

With no annotations provided, the description carries the burden. It discloses billing per delivered row (zero rows cost zero), and the sync/async behavior with run_id. It doesn't mention error handling or rate limits, but the key behaviors are covered. No contradiction with annotations since none exist.

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?

Three sentences, each earns its place: the core action, the sync/async distinction, and the billing model. Front-loaded with the key action and then important behavior. No wasted words.

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 no output schema and 100% param schema coverage, the description covers the essential behaviors: sync vs async, billing, and how to poll. It could mention error conditions or rate limits, but for a run tool it's reasonably complete. The sibling list_collectors is hinted for slug discovery.

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?

Schema coverage is 100%, so the schema documents the parameters well. The description adds meaning for the 'async' parameter (force background processing), and explains the input is semantic and matches the collector's published input_schema. It provides value 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 states clearly it runs a Collector with its semantic input, and distinguishes the sync vs async behavior. It also names the sibling collector_run_status for polling, which helps differentiate from 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 Guidelines4/5

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

The description gives context on when to use synchronous vs asynchronous (short vs long runs), and implicitly suggests using list_collectors first if unsure about the slug. It doesn't explicitly say when not to use this tool or mention alternatives beyond the polling tool, so a slight gap.

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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