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search_tweets

Search recent tweets from the past 7 days using keywords, hashtags, user mentions, media filters, and advanced query operators to find specific content on X (Twitter).

Instructions

Search recent tweets by query. Supports keywords, hashtags, from:user, to:user, is:reply, has:media, etc. Uses the recent search endpoint (last 7 days).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (e.g. 'from:elonmusk', '#ai', 'machine learning')
max_resultsNoNumber of results (10-100, default 10)
next_tokenNoPagination token from previous response
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the temporal constraint ('last 7 days'), endpoint type ('recent search endpoint'), and query syntax support. However, it doesn't mention rate limits, authentication requirements, or what the response format looks like, leaving gaps for a search tool.

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?

The description is extremely concise with just two sentences that each earn their place: the first states purpose and capabilities, the second adds critical behavioral context about temporal scope. No wasted words, perfectly front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate purpose and behavioral context but lacks information about response format, error conditions, or authentication requirements that would be helpful for an AI agent.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already fully documents all 3 parameters. The description adds minimal value beyond the schema by mentioning query syntax examples ('keywords, hashtags, from:user, etc.') but doesn't provide additional semantic context about parameters beyond what's in the schema descriptions.

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 description clearly states the specific action ('Search recent tweets by query') and resource ('tweets'), distinguishing it from siblings like get_timeline or get_mentions by specifying it's a search operation. It explicitly mentions the 'recent search endpoint' which further clarifies scope.

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?

The description provides clear context about when to use this tool ('Search recent tweets by query') and mentions the temporal limitation ('last 7 days'), but doesn't explicitly state when NOT to use it or name specific alternatives among siblings like get_timeline or get_mentions for different use cases.

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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