Get detailed information for a single tweet by its ID. Returns full content, engagement metrics, and author info.
| Name | Required | Description | Default |
|---|---|---|---|
| tweet_id | Yes | Numeric tweet ID | |
| get_sentiment | No | Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small surcharge per page, or per request on single-request endpoints. |
Changes observed during successful MCP inspections.
Input schema / properties / get_sentiment / descriptionPrevious value: -"Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge."New value: +"Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small surcharge per page, or per request on single-request endpoints."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral burden. The verb 'Get' and the promise of returned data imply a read-only operation, and the output categories are disclosed. However, it does not address rate limits, authentication needs, deleted/private tweets, or error 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?
Two short sentences that are front-loaded with the core action and resource, followed by the return summary. No filler or redundant wording.
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?
For a low-complexity tool with only one required parameter and no output schema, the description adequately states the input and the high-level return contents. It could mention caveats like sentiment surcharge or error cases, but the schema already covers the optional sentiment parameter.
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 the schema already documents both tweet_id and get_sentiment. The description adds little parameter-level meaning beyond confirming the tool targets a single tweet by ID.
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 uses a specific verb ('Get') and resource ('detailed information for a single tweet by its ID'), and clarifies exactly what is returned: content, engagement metrics, and author info. This clearly separates it from sibling tools like twitter_tweet_comments, twitter_tweet_quotes, and twitter_tweet_retweets.
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 intended use is implied: use this tool when you need details of a single tweet, including content, engagement, and author information. However, it does not explicitly mention alternatives or state when not to use it, leaving some selection burden on the agent.
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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