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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 posts 0-10, and ranking the best matches with their intent signals.

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. The lead-response fields and credit weights are preserved regardless of the underlying Reddit data source.

Input 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 well: it enumerates what is filtered out before scoring, discloses the deterministic prefilter plus optional batched LLM refinement, explains the 503/Retry-After throttling behavior, and notes that lead-response fields and credit weights are preserved. These are non-obvious operational traits an agent needs.

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?

Purpose is front-loaded and each sentence carries distinct information (scoring, filters, classifier, throttling, field preservation). It is dense rather than bloated, though the later sentences about throttling and credit weights slightly extend an already full paragraph.

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 tool with no output schema and no annotations, the description covers the pipeline, filtering, ranking, classifier modes, throttling, and the shape of returned lead data ('ranked highest-first with the signals that fired'). Nothing essential for correct invocation is missing.

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 coverage is 100% and every parameter (sort, time, limit, min_score, subreddit, classifier) is documented in the schema itself, so the schema does the heavy lifting. The description restates the classifier resolution behavior but adds little syntax or format detail beyond what the schema already provides, matching the baseline of 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?

States a specific verb+resource ('Find Reddit buying-intent leads') with exact mechanics: scans a Reddit search page, scores posts 0-10 for buying intent, and returns them ranked. It is clearly distinguishable from generic siblings like reddit_search because it produces scored, filtered leads rather than raw posts.

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?

Implies the use case strongly ('people actively asking for a product or service') and explains the classifier modes (auto/heuristic/llm), giving the agent a clear operating context. It does not explicitly name sibling alternatives like reddit_search or state when NOT to use it, so it stops short of a 5.

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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