search_redditReddit keyword search (/v1/reddit/search, top-ranked) — a goldmine for the customer's OWN words (pain points, objections, language) to mine into ad hooks and copy. Returns compact JSON {desc (title+selftext), subreddit, upvotes, comments, link} per post. Spends about a credit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max posts returned (1–25, default 8) | |
| query | Yes | what to search Reddit for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint, destructiveHint), the description discloses the return format ('Returns compact JSON {desc (title+selftext), subreddit, upvotes, comments, link} per post') and the cost ('spends about a credit'). This adds significant behavioral context that annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with zero waste. It front-loads the purpose and value proposition, then efficiently covers return format and cost. Every sentence earns its place, and the structure is easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, but the description fully specifies the return structure and cost. It doesn't mention pagination, error handling, or prerequisites, but for a simple search tool with two parameters and a clear return format, this is nearly complete. A minor gap is the lack of mention of how results are ranked or any usage tips, but it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters (query and limit) are already documented in the schema. The description adds no additional meaning beyond what the schema provides, matching the baseline for high coverage. No extra clarifications on parameter usage are given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (search), the resource (Reddit), and the specific purpose (mining customer's own words for ad hooks). It distinguishes itself from other search tools by highlighting its top-ranked status and focus on customer language, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a clear usage context: to mine customer language for ad copy. While it doesn't explicitly exclude alternatives or name siblings like search_tiktok, the description implies when to use this tool (when Reddit customer phrasing is needed) and even notes it's 'top-ranked'. No explicit when-not guidance, but the context is strong enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.
The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.
293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.
The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.