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jain-eshan

Google Trends MCP

by jain-eshan

reddit_signal

Find real Reddit discussions, complaints, and questions about any topic. Get qualitative community insights from post titles, bodies, and subreddit context to complement search trend data.

Instructions

What real people on Reddit are saying, asking, or complaining about - qualitative community signal, as opposed to Trends/Wikipedia's passive search/reading numbers.

Works with no setup via Reddit's public search feed (which returns post text but not scores, and rate-limits after a burst of requests). If REDDIT_CLIENT_ID and REDDIT_CLIENT_SECRET are saved via /gutcheck setup, uses the official API instead, which adds score and comment counts.

Args: query: search terms. subreddits: optional subreddit names to restrict the search to (e.g. ["startups", "SaaS"]). limit: max results, capped at 100. time_filter: "week", "month", "year" (default), or "all".

Keep the query short (2-4 words) and pass subreddits when obvious communities exist. Reddit's keyless search matches loosely on long questions, so results that share no meaningful word with the query are dropped and the rest are ranked by how many query words they match.

Returns: A list of records with "title", "subreddit", "snippet" (first ~300 chars of the post body), "url", "created". "score" and "num_comments" are included only in API mode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
subredditsNo
time_filterNoyear

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

A4.8/5.0
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 thoroughly: it discloses rate limits, mode differences (public feed omits scores; API adds score and comment counts), setup prerequisites via /gutcheck setup, and the loose-matching/ranking behavior of the keyless search. This is strong behavioral disclosure beyond typical one-line definitions.

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?

Despite length, the description is front-loaded (purpose first) and every section earns its place: signal type, mode setup, args, querying heuristics, and return format. The structured Args/Returns layout makes the extra behavioral context navigable rather than bloated.

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?

Because there is no output schema, the description supplies the return record fields and mode-dependent omission of score/num_comments, which is essential. It also covers prerequisites, rate limiting, and parameter behaviors, leaving little an agent needs to infer before calling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it defines all four parameters: query as search terms, subreddits as optional restriction with examples, limit as max results capped at 100, and time_filter with valid values and default. The only schema-default it omits is limit's 25, which the schema itself exposes.

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 sentence identifies the resource (Reddit), the kind of data (qualitative posts by real people), and explicitly contrasts it with 'Trends/Wikipedia's passive search/reading numbers,' making sibling differentiation obvious. The query argument and return record names make it clear this is a Reddit post-search/signal tool.

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?

It positions the tool as qualitative community signal versus Trends/Wikipedia numeric data and gives concrete query/subreddit guidance ('Keep the query short (2-4 words) and pass subreddits when obvious communities exist'). It clearly explains when API mode will be used versus the keyless feed, but it never states an explicit 'use when / do not use when' rule, so no hard exclusion is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.