Search Threads (Meta's Threads app) posts by keyword. Returns post text, author, engagement metrics, media, and post permalinks. Billed $0.006 per request.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keyword (max 500 characters) | |
| 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the cost per request ('Billed $0.006 per request') and outlines the response contents, which is valuable behavioral context. It also mentions the optional sentiment analysis and its surcharge. It does not explicitly state read-only status or rate limits, but the 'Search' verb implies non-destructive behavior.
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 concise: two sentences that front-load the purpose and return types, then add cost information. Every word earns its place, and there is no redundant filler.
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, the description adequately conveys the key return fields (text, author, engagement metrics, media, permalinks) and the optional sentiment enhancement. It covers cost and the optional parameter, but does not mention pagination, sorting, or date filters, which might be expected for a search tool. Overall, it is sufficient for basic usage understanding.
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?
The schema itself documents both parameters with full coverage (100%), so the description does not need to repeat parameter details. It adds marginal context by mentioning the sentiment surcharge, but does not elaborate on parameter syntax or formats beyond the schema. Baseline 3 is appropriate when the schema handles semantics.
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 tool's purpose: 'Search Threads (Meta's Threads app) posts by keyword' and lists the specific types of returned data (text, author, engagement metrics, media, permalinks). This distinguishes it from sibling tools like search_threads_users, which focuses on users rather than 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 explicit context for when to use the tool: when you need to search Threads posts by keyword. It does not explicitly mention alternatives or exclusions, but the context is clear enough that an agent can infer when to use it. A higher score would require naming alternate tools or scenarios to avoid.
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.
