Search Twitter/X posts.
| Name | Required | Description | Default |
|---|---|---|---|
| pages | No | Number of pages to fetch, 1-20 (default: 1) | |
| query | Yes | Search keyword (max 500 characters) | |
| sort_by | No | Sort order: "most_recent" or "relevance" | most_recent |
| get_sentiment | No | Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / pages / descriptionPrevious value: -"Number of pages to fetch, 1-10 (default: 1)"New value: +"Number of pages to fetch, 1-20 (default: 1)"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. 'Search Twitter/X posts' only states the action and resource; it does not disclose read-only behavior, pagination, rate limits, authentication needs, or how the optional sentiment analysis affects results.
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 a single, efficient sentence with the action and resource front-loaded and no filler. It is concise, though arguably too thin for the tool's complexity; still, it earns its place without redundancy.
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?
With no output schema and no annotations, the description does not explain return values, usage context, or how this tool relates to the many sibling Twitter and search tools. The full parameter schema compensates only partially; overall selection and invocation context remains incomplete.
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%, with all four parameters individually documented. The tool description adds no parameter semantics, so the baseline score of 3 is appropriate because the schema already does the heavy lifting.
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 names a specific resource, Twitter/X posts, and the action, search, which distinguishes it from sibling tools like search_twitter_users or twitter_trends. However, it stops short of saying it performs keyword-based searches or defining the scope beyond 'posts'.
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?
The description gives no guidance on when to use this tool versus alternatives such as twitter_user_tweets, twitter_tweet_details, or search_twitter_users. No conditions, exclusions, or sibling references are provided, leaving tool selection to inference.
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.
Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.
The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.
74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.
The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.