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DanielTomaro13

sportsdata-mcp

twitter_user_tweets

Read-onlyIdempotent

Retrieve recent posts from a specific Twitter account using its numeric user ID. For sports clubs and insiders, use this to access announcements and news for cross-provider analysis.

Instructions

An account's recent posts (club announcements, insider news). Resolve the id with twitter_user_by_username first.

Returns: {data:[{id, text, created_at, public_metrics}], meta:{result_count, newest_id, next_token}}

Auth: needs your own key in X_BEARER_TOKEN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesNumeric user id. Required — part of the URL path.
excludeNoCSV of retweets and/or replies to drop.
end_timeNoISO 8601 upper bound.
since_idNoOnly posts newer than this id.
start_timeNoISO 8601 lower bound.
max_resultsNoResults per page (5-100).
tweet_fieldsNoPost fields (CSV).created_at,public_metrics,lang
pagination_tokenNoPagination token.
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the bar for additional transparency is lower. The description adds value by specifying auth requirements ('needs your own key in X_BEARER_TOKEN') and the exact return shape, including data fields and meta. No contradiction with annotations.

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: three short sentences covering purpose, dependency, return format, and auth. Every sentence carries useful information, and no wasted words. The return example and auth note are clearly separated.

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

Completeness4/5

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

For an 8-parameter read-only tool with no output schema, the description provides the essential return structure, auth prerequisite, and a clear dependency flow. It does not mention rate limits or pagination behavior beyond the next_token field, but the schema covers parameter details, making this reasonably complete.

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 coverage is 100%, so the schema already documents all parameters. The description adds no additional parameter details beyond hinting that the id must be resolved first, which the schema already indicates as 'Numeric user id.' With full schema coverage, the baseline of 3 is appropriate.

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?

Description states it returns 'An account's recent posts' with specific examples (club announcements, insider news). It uses a clear verb (get/list) and resource (account's recent posts), and the note to 'Resolve the id with twitter_user_by_username first' distinguishes it from other Twitter tools that might accept usernames or tweet IDs.

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 gives a clear prerequisite ('Resolve the id with twitter_user_by_username first') which implies a usage flow. However, it does not explicitly contrast with sibling tools (e.g., twitter_tweets, twitter_search_recent) or say when NOT to use this tool. The context is implied rather than explicit.

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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