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quanticdata-mcp

by io.github.quantumproxies

dataset_status

Read-onlyIdempotent

Poll a dataset job for progress, the collection trace (steps) and the rows so far. Polls are incremental: pass the previous response's nextCursor as since to receive only rows delivered after your last poll. Set mode 'summary' to omit rows and get only progress + steps (light poll). When status is completed, the response includes signed CSV/JSON download URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosummary: progress + steps only, no rows
jobIdYesThe dataset job id returned by create_dataset
sinceNoRow cursor from the previous poll's `nextCursor` — returns only newer rows

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds useful behavior beyond that: polls are incremental, summary mode reduces payload, and completed jobs return signed CSV/JSON download URLs. This gives the agent a solid behavioral model without contradicting 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?

Three dense sentences each carry essential information: what the tool returns, how incremental polling works, how to lighten the poll, and what completion yields. There is no filler or repetition of annotation data.

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?

The description covers the key outputs an agent needs to know: progress, steps, rows, `nextCursor`, and signed download URLs. There is no output schema, so a bit more detail on status values or pagination edge cases could be helpful, but this is complete enough for correct invocation and basic handling of responses.

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 coverage is 100%, so the description does not need to compensate for missing parameter docs. It does add a small amount of context, such as calling summary mode a 'light poll' and tying `since` to the prior response's `nextCursor`, but this largely mirrors the schema. Baseline 3 is appropriate.

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 uses a specific verb and resource: 'Poll a dataset job' for progress, collection trace, and rows. This clearly distinguishes dataset_status from sibling status tools like batch_status, crawl_status, and collector_run_status by naming the exact job type it targets.

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 provides clear usage context: incremental polling with `since`, the `summary` mode for lighter polls, and download URLs on completion. It does not explicitly name alternatives or say when not to use this tool, but the dataset job framing makes the intended context unambiguous.

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