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OP3 MCP

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TypeScript
Hybrid

New vs returning

op3_new_vs_returning
Read-onlyIdempotent

Split a podcast show's listeners in a chosen window into first-time and returning by comparing against a longer prior baseline. See whether the show reaches new people or repeats the same audience.

Instructions

Split a show's listeners in a window into first-time and returning, by comparing against a longer baseline period immediately before it. Answers whether a show is reaching new people or serving the same audience repeatedly. The baseline length matters: a short baseline calls a monthly listener new, so it defaults to four times the window and both sizes are reported so you can judge the answer.

Input Schema

NameRequiredDescriptionDefault
endNoWindow end, same formats as start. Defaults to now.
botsNoInclude downloads from known bots. Off by default, which matches how OP3's own download counts are computed.
startNoWindow start. A relative value like -30d, -8w or -3m, a date like 2026-08-01, or an ISO timestamp. Defaults to -30d. Wider windows are slower: this reads raw rows, not a rollup.
identifierYesAn OP3 show uuid, a podcast:guid, or the show's RSS feed URL.
baseline_multiplierNoHow many window-lengths of history to treat as the baseline. Higher is more accurate and slower. 4 means a 30-day window is compared against the 120 days before it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.0.0
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv1.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds useful context beyond that: the default baseline multiplier and why it matters, that both sizes are returned, and that wider windows read raw rows and are therefore slower. 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.

Conciseness4/5

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

Four compact sentences, front-loaded with what the tool computes before the caveat about baseline length. Every sentence carries information; only the final clause slightly restates the reporting behavior.

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?

With no output schema, the description does tell the agent that both the first-time and returning sizes are returned, which is the key shape information. It leaves the exact response structure unspecified, but for a read-only analytical tool with full schema coverage this is nearly complete.

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 baseline is 3; the description still adds interpretive value by motivating baseline_multiplier's default (a short baseline mislabels a monthly listener as new) and reinforcing the multiplier semantics. It does not add format detail for start/end/identifier beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific analysis: splitting a show's listeners in a window into first-time vs returning via comparison against a prior baseline. The framing ('reaching new people or serving the same audience repeatedly') conceptually separates it from a retention-style metric, though it never names a sibling tool to route against.

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?

Explains the question this answers and adds guidance on the key tradeoff (baseline length changes what counts as 'new'). It does not explicitly say when to prefer this over op3_listener_retention or op3_audience_summary, so the routing decision is left partly implicit.

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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