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Prowlo — Reddit & X for AI agents

research_run

Start a hosted Community research run for a product: Prowlo reads ~1,000 recent posts and ~3,000 comments across candidate subreddits (7 angles: brand, competitor, problem, use-case, alternative-seeking, semantic, audience overlap), then reports ranked communities with quoted evidence, rules, what content wins, competitors, voice of customer, and an engagement plan. Takes ~10 minutes; poll research_get. First run per org is free; later runs use AI credits (see get_usage). Do NOT reproduce this with social_* reads — those are metered per call. (requires a free Prowlo account — call it to get a signup link)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoOptional steer, e.g. "developer-tool buyers, not hobbyists"
productIdNoProduct to research; defaults to the org's primary product
extraCompetitorsNoCompetitor names to track beyond the product profile
excludeSubredditsNoSubreddits to leave out
includeSubredditsNoSubreddits that must be considered (with or without r/)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only say readOnlyHint=false and destructiveHint=false; the description adds the important behavioral context of duration (~10 minutes), asynchronous polling, credit consumption after the first free run, and account requirement. This gives an agent a clear sense of side effects and cost 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.

Conciseness4/5

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

The description is long but front-loaded with the action before diving into output, timing, cost, and alternative-routing. Every sentence carries distinct operational information, and the density is justified by the tool's asynchronous, credit-consuming nature. It is not bloated, though not as terse as the ideal.

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?

For a complex side-effecting tool with no output schema and no required parameters, the description supplies the necessary context: what it processes (posts/comments and angles), what it returns (ranked communities and plans), how to retrieve results (research_get), and cost/prerequisites. The only minor ambiguity is which tool to call for the signup link, but this does not block correct invocation.

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?

The input schema covers 100% of the five optional parameters with descriptive text for focus, productId, extraCompetitors, excludeSubreddits, and includeSubreddits. The description contributes context about what the run does with subreddits but does not add per-parameter semantics beyond the schema, so 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 opening clause names a concrete action and resource ('Start a hosted Community research run for a product') and the rest enumerates the exact research scope and output. It also distinguishes itself from research_get ('poll research_get') and social_* reads, so an agent need not confuse it with 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?

The description gives explicit when-and-how: it is asynchronous ('Takes ~10 minutes; poll research_get'), metered after the first run ('later runs use AI credits (see get_usage)'), and explicitly says not to reproduce it with social_* reads. It also states the prerequisite of a free Prowlo account and where to get a signup link.

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

A3.6/5.0
Disambiguation2/5

Several tool pairs have overlapping purposes: keyword_* and tag_rule_* are duplicates, keyword_monitor_create is a shortcut for a subset of watcher_create, and alert_list vs get_alerts have confusingly similar names. Even though descriptions are detailed, the redundancy and naming overlap make it easy to pick the wrong tool.

Naming Consistency2/5

The set mixes object-verb names (alert_create, watcher_create) with verb-object names (get_account, list_records) and prefix-based categories (social_*). The deprecated keyword_* tools are renamed tag_rule_*, and keyword_monitor_create doesn't follow the watcher_* pattern, creating an inconsistent naming scheme.

Tool Count2/5

At 42 tools, the surface is far above the typical well-scoped range, with 5 deprecated tools and redundant paths like keyword_monitor_create vs watcher_create inflating the count. A leaner set focusing on core operations would be more manageable.

Completeness3/5

The Reddit/HN monitoring and alerting side is well covered with CRUD for watchers, tag rules, alerts, and trends plus dataset and social reads. However, the server advertises X support but has no X tools, and there's no way to post comments or manage integrations beyond viewing account info, leaving notable gaps.