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JavaScript
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reddit_leads

Find Reddit buying-intent leads by scanning search results for people asking for products or services, scoring each post 0-10 and returning them ranked highest-first.

Instructions

Find Reddit buying-intent leads. Scans a Reddit search page for people actively asking for a product or service, scores each post 0-10 for buying intent, and returns them ranked highest-first with the signals that fired. Self-promotion, hiring posts, freelancer service adverts, revenue-milestone posts, duplicate reposts, and Title Case article headlines are filtered out before scoring. A deterministic prefilter always runs; when classifier resolves to llm the surviving candidates are additionally refined in one batched model call. A 503 with a Retry-After header means Reddit is temporarily throttling the request; wait that many seconds and retry. Native Reddit search failures can use the internal Redlib provider while the existing lead-response fields and credit weights are preserved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesWhat you offer, in plain language
sortNoSort: relevance, hot, new, top, or comments
timeNoTime window: hour, day, week, month, year, or all
limitNoMaximum leads returned, defaults to 25 and clamps to 100
min_scoreNoMinimum buying-intent score to return, 0-10, defaults to 4
subredditNoRestrict the search to a subreddit name, without r/
classifierNoClassifier: auto uses the model when configured, heuristic skips it, llm requires it

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.17.5
    • addedInput schema / properties / classifier / enum
      Added value: +[
+  "auto",
+  "heuristic",
+  "llm"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "hot",
+  "new",
+  "top",
+  "comments"
+]
    • addedInput schema / properties / time / enum
      Added value: +[
+  "hour",
+  "day",
+  "week",
+  "month",
+  "year",
+  "all"
+]
  2. Addedv1.16.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: it discloses filtering rules (self-promotion, hiring, freelancer ads, reposts, Title Case headlines), a deterministic prefilter that always runs, an optional batched LLM refinement, and a 503/Retry-After throttling contract. This is exactly the behavioral context an agent needs before invoking.

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?

Front-loaded with the core purpose and scoring/filtering behavior before the operational details. Sentences are dense but each carries information; the final Redlib-fallback sentence is somewhat tangential but still relevant to reliability expectations.

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?

For a 7-parameter pipeline tool with no output schema, the description covers the return shape (ranked highest-first with firing signals), the scoring scale, filtering, classifier resolution, and failure/throttle handling. An agent has enough to invoke it correctly.

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?

Schema description coverage is 100%, so the schema already documents every parameter including enums and defaults. The description adds a bit of meaning for the classifier modes (prefilter always runs; llm refines candidates in a batched call), but largely restates what the schema provides, warranting the baseline 3.

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?

Starts with a specific verb+resource ('Find Reddit buying-intent leads') and immediately details the mechanism (scans a Reddit search page, scores posts 0-10, ranks them). It is clearly distinguishable from sibling tools like reddit_search or reddit_post.

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?

The description establishes a clear use case (surfacing people actively asking for a product/service) and explains the classifier modes and when the LLM path kicks in. However, it never names an alternative sibling (e.g. reddit_search) or states when NOT to use this tool, so the routing guidance is implicit rather than explicit.

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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