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quanticdata-mcp

by io.github.quantumproxies

run_collector

Run a Collector by slug with a semantic input (see list_collectors for each collector's inputSchema and example). Short runs return the rows inline; long runs return 202 with a run_id + statusUrl — poll with collector_run_status. Results are billed per delivered row (never for failures). Set async true to force background execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCollector slug from list_collectors, e.g. 'google_maps_places'
asyncNoForce background execution and return a run_id to poll
inputYesInput fields matching the collector's inputSchema (e.g. { keyword: 'dentist', location: 'Austin, TX', max_results: 20 })

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds substantial behavior beyond annotations: short runs return rows inline, long runs return 202 with run_id and statusUrl, results are billed per delivered row (never for failures), and async forces background execution. This complements the openWorldHint and readOnlyHint annotations effectively.

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 tightly written sentences with no filler. The action is front-loaded, followed by behavior, billing, and async guidance—each sentence earns its place.

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?

For a tool with no output schema, the description explains the return contract (inline rows vs 202 + run_id/statusUrl), the polling path, billing, and async behavior. An agent has everything needed to call and follow up on the run.

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 baseline is 3. The description adds a concrete input example, references list_collectors for per-collector inputSchema, and explains the async parameter's behavioral effect, going beyond the schema descriptions.

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?

States a specific verb and resource: 'Run a Collector by slug with a semantic input.' It names the related tools list_collectors and collector_run_status, which clarifies its distinct role in the workflow.

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?

Provides clear context: consult list_collectors for the inputSchema and example, poll with collector_run_status after long runs, and set async true to force background execution. It does not explicitly contrast against alternative run/execute tools, but the collector-specific workflow is well defined.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.

Resources