Prowlo — Reddit & X for AI agents

list_records

Read-onlyIdempotent

List records in your Dataset (Prowlo’s indexed posts from crawled sources). NOT a live Reddit call — free, rate-limited only. Filter by view (all/matched), subreddits, platforms, tags, keyword, any (brand-variant OR-group), include/exclude, intent, score/comments, date range, watcherId. Paginated via cursor. Note: tags / watcherId are record-level, so passing either uses the "matched" view automatically. Records whose keyword match was AI-judged coincidental are hidden by default (pass includeSuppressed:true to see them; each record carries a verification field). A filtered page can hold fewer than limit items — page until nextCursor is null. Long record bodies are trimmed to keep the page scannable — use get_record for a record’s full body. (requires a free Prowlo account — call it to get a signup link)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	No	ISO date upper bound (created on platform)
`any`	No	Match if title/body contains ANY of these terms (brand monitoring → your brand + variants)
`from`	No	ISO date lower bound (created on platform)
`tags`	No	Restrict to records carrying any of these rule tags
`view`	No	"matched" = only records that matched a Watcher rule	all
`limit`	No	Max records to return, 1-40 (default 25); page with cursor for more
`cursor`	No	Pagination cursor from a previous response
`intent`	No	(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.
`exclude`	No	None of these may appear in title/body
`include`	No	Stricter: EVERY term must appear in title/body
`keyword`	No	Single free-text term in title/body
`minScore`	No	Minimum post score (platform upvotes/points)
`platforms`	No	e.g. ["reddit","hackernews"]
`watcherId`	No	Restrict to records from one of your Watchers (by id)
`subreddits`	No	Restrict to these subreddits/channels (no r/ prefix)
`minComments`	No	Minimum number of comments
`includeSuppressed`	No	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.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 6, 2026

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"
+}

First observedAug 5, 2026

TDQS

A4.9/5.0

Behavior5/5

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

The description goes well beyond the readOnly/idempotent hints, disclosing pagination quirks (page can hold fewer than limit), suppression of coincidental matches, the `verification` field, automatic switch to 'matched' view for tags/watcherId, and trimming of long bodies. These are important behavioral traits not in 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?

The description is dense but every sentence adds value: purpose, non-live caveat, filter summary, pagination, behavioral quirks, and full-body redirect. It is well-structured and front-loaded with the core purpose.

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?

Given 17 parameters and no output schema, the description covers key return behavior (verification field, trimming), pagination semantics, account requirement, rate limit, and relationship to get_record. This is a fully-specified context for a complex 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%, so baseline is 3. The description adds meaningful parameter context: explains `any` as brand-variant OR-group, the side effect of tags/watcherId forcing matched view, and the includeSuppressed behavior. This goes beyond 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?

The description clearly identifies a specific verb (List) and resource (records in your Dataset), and distinguishes itself from a live Reddit call and from get_record (which retrieves full bodies). This is a specific, non-generic purpose that differentiates from sibling tools.

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?

Explicitly states when NOT to use it ('NOT a live Reddit call') and redirects to an alternative for a related need ('use get_record for a record’s full body'). This provides clear usage context versus siblings.

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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list_records

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

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