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Prowlo — Reddit & X for AI agents

by com.prowlo
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search_dataset

Read-onlyIdempotent

Semantic or keyword search across your Dataset (vector + full-text over your indexed corpus). NOT a live Reddit call — free, rate-limited only. Supports the full record filter: subreddits, platforms, any (brand-variant OR-group), include/exclude, intent, score/comments, date range, tags, watcherId. Records whose keyword match was AI-judged coincidental are hidden by default (pass includeSuppressed:true to see them; each record carries a verification field). Long record bodies are trimmed to keep results scannable — use get_record for a match’s full body. (requires a free Prowlo account — call it to get a signup link)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoISO date upper bound (created on platform)
anyNoMatch if title/body contains ANY of these terms (brand monitoring → your brand + variants)
fromNoISO date lower bound (created on platform)
modeNoSearch mode: "semantic" (vector) or "keyword" (full-text) (default semantic)semantic
tagsNoRestrict to records carrying any of these rule tags
tierNoSemantic only: "deep" reranks (slower, sharper)
limitNoMax results to return, 1-40 (default 25); page with cursor for more
queryYesThe search query
cursorNoPagination cursor from a previous response
intentNo(COMING SOON — not yet available) Filter by author intent: BUYING, SELLING, or DISCUSSING. Posts are not intent-classified yet, so this filter currently returns no matches.
excludeNoNone of these may appear in title/body
includeNoStricter: EVERY term must appear in title/body
minScoreNoMinimum post score (platform upvotes/points)
platformsNoe.g. ["reddit","hackernews"]
watcherIdNoRestrict to records from one of your Watchers (by id)
subredditsNoRestrict to these subreddits/channels (no r/ prefix)
minCommentsNoMinimum number of comments
includeSuppressedNoInclude records whose keyword match was judged "coincidental" by AI mention verification (the term appeared but the post is not about it). Default false — such records are hidden. Every returned record carries a `verification` field (confirmed | coincidental | pending | clear) either way.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / includeSuppressed
      Added value: +{
+  "default": false,
+  "description": "Include records whose keyword match was judged \"coincidental\" by AI mention verification (the term appeared but the post is not about it). Default false — such records are hidden. Every returned record carries a `verification` field (confirmed | coincidental | pending | clear) either way.",
+  "type": "boolean"
+}
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only state read-only and idempotent, but the description adds extensive behavioral context: rate limiting, key default behavior for suppressed records, verification field, body trimming, and account prerequisite. This goes far beyond annotations and enriches agent understanding.

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?

The description is a single, dense paragraph that front-loads the core purpose and then efficiently covers key behaviors. It is longer than absolutely necessary but every sentence adds meaningful value without redundancy.

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 carries the burden of explaining response behavior. It mentions verification fields, trimming, and implies pagination via cursor references in the schema. It could be more explicit about the overall response structure, but is adequate for a complex search tool.

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%, but the description adds valuable semantics for key parameters like `any` (brand-variant OR-group), `includeSuppressed` behavior, and the verification field. It also summarizes the broader filter set, making param behavior clearer than schema alone.

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 clearly states 'Semantic or keyword search across your Dataset' with specific detail on vector and full-text search. It distinguishes from siblings by explicitly stating 'NOT a live Reddit call' and pointing to get_record for full bodies, making its scope precise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Provides explicit usage context: it is not live, has rate limits, requires a Prowlo account, and directs users to get_record for full bodies. This offers clear when-to-use guidance relative to alternative tools.

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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