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DanielTomaro13

sportsdata-mcp

twitter_search_recent

Search posts from the last 7 days on X using query operators to find relevant discussions or mentions. Returns tweet data including metrics and author info.

Instructions

Search posts from the last 7 days with X's query operators (e.g. '"Lakers" lang:en -is:retweet').

Returns: {data:[{id, text, created_at, author_id, lang, public_metrics:{retweet_count, reply_count, like_count, impression_count}}], includes:{users:[…]}, meta:{result_count, newest_id, next_token}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
end_timeNo
since_idNo
expansionsNoauthor_id
next_tokenNo
sort_orderNo
start_timeNo
max_resultsNo
user_fieldsNousername,name,verified,public_metrics
tweet_fieldsNocreated_at,author_id,public_metrics,lang
Behavior3/5

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

The description specifies the time range and return format, but does not disclose authentication requirements, rate limits, or pagination behavior (though next_token appears in the return structure). Without annotations, this is an acceptable but not thorough disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief, with two sentences and a clear return structure. It front-loads the purpose and example without unnecessary repetition.

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

Completeness2/5

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

Given 10 parameters and no output schema, the description covers only the purpose and return format, omitting parameter semantics and usage context. An agent would struggle to invoke the tool correctly without additional information.

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

Parameters2/5

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

With 0% schema description coverage and 10 parameters, the description only explains the 'query' parameter via an example. All other parameters (end_time, since_id, max_results, etc.) lack any explanation, leaving the agent without guidance on how to use them.

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 verb 'Search', the resource 'posts from the last 7 days', and provides a specific example query. It distinguishes from sibling tools like twitter_tweet or twitter_user_tweets by specifying the time constraint and query operators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for searching recent tweets with complex queries, but does not explicitly state when not to use it or mention alternatives such as twitter_tweets for known IDs or twitter_user_tweets for user-specific searches.

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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