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Preview an event-driven data reduction

preview_pipeline
Read-onlyIdempotent

Preview event-window reduction outcomes: supply a SQL predicate and pre/post seconds to detect transitions, debounce, and merge windows, then review intervals and kept fraction before execution.

Instructions

Preview an event-window reduction without writing artifacts. Inspect source and topic schemas first, then provide a SQL boolean predicate and nonnegative pre/post seconds. Detects false-to-true transitions, debounces nearby events, merges overlapping windows, and returns event timestamps, intervals, total seconds, and kept seconds/fraction. Report these results and obtain user confirmation before executing the reduction with run_pipeline or run_pipeline_batch. Does not predict output byte size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsNo
pathYes
predicateYes
event_topicYes
pre_secondsYes
post_secondsNo
debounce_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.1

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses concrete behavior: detecting false-to-true transitions, debouncing events, merging overlapping windows, and returning timestamps, intervals, and kept seconds/fraction. The explicit caveat that it does not predict output byte size adds useful limitation context.

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?

The description is three dense but purposeful sentences: purpose, input constraints and behavior, then workflow and limitation. Every sentence earns its place and the core purpose is front-loaded.

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?

It covers behavior, outputs, input constraints, the required confirmation workflow, and a known limitation, while the output schema handles return-value detail. The remaining gap is the under-specified path, event_topic, and args semantics, which keeps this from being fully complete.

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?

With 0% schema description coverage, the description must compensate, and it partly does: predicate is a SQL boolean, pre/post seconds are nonnegative, and debounce_seconds maps to debouncing nearby events. However, path, event_topic, and especially the catch-all args parameter remain semantically unexplained beyond their schema titles.

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 opens with a specific verb and resource: 'Preview an event-window reduction without writing artifacts.' It also distinguishes itself from the run_pipeline siblings by explicitly framing this as a pre-execution preview step.

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?

It gives clear usage context: inspect source and topic schemas, provide a SQL predicate and nonnegative pre/post seconds, then report results and get user confirmation before running run_pipeline or run_pipeline_batch. It does not provide explicit when-not-to-use exclusions beyond the byte-size caveat.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.