Fact-checking server that retrieves information from Wikipedia and Twitter to verify claims using NLP models
14 tools with basic verb-noun naming (search_*, get_*) and present descriptions, but significant gaps in parameter documentation and output schema clarity. All tools are READ_ONLY, reducing risk. However, 11 of 14 tools lack explicit parameter descriptions in visible code (e.g., summarize_article_for_query has 'title', 'query', 'max_length' params but no inline descriptions visible). Output schemas are not documented, LLMs cannot infer what fields to expect. Descriptions are present but generic (avg ~50 chars), below the 194-char baseline for A+ tools. No error handling guidance visible. Tool composition is reasonable (single responsibility), but parameter validation and chaining support are absent.
Extract key facts from a Wikipedia article, optionally focused on a topic.
Get the full content of a Wikipedia article.
Get the links contained within a Wikipedia article.
Get topics related to a Wikipedia article based on links and categories.
Get the sections of a Wikipedia article.
Get a summary of a Wikipedia article.
Get trending topics for a specific location.
Output schemas not documented. Tools return Dict[str, Any] with no field specification. LLMs cannot infer what fields to extract or how to chain results to downstream tools.
Parameter descriptions missing or incomplete in visible code. E.g., summarize_article_for_query has 'title', 'query', 'max_length' but no inline descriptions visible in function signature. Rubric requires 100% of A+ tool params have descriptions.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 63 | <=2025-11-25 | v2 |
Get detailed information about a specific tweet.
Get detailed information about a Twitter user.
Get recent tweets from a specific user.
Search Twitter for tweets matching a query.
Search Wikipedia for articles matching a query.
Get a summary of a Wikipedia article tailored to a specific query.
Get a summary of a specific section of a Wikipedia article.
No error handling guidance. Tools return Dict[str, Any] with no documented error cases, recovery steps, or actionable error messages. LLMs cannot self-correct on failures.
Tool descriptions are generic and brief (~50 chars avg). Rubric baseline is 194 chars. Descriptions lack WHEN to use, prerequisites, or distinction from similar tools (e.g., get_summary vs summarize_article_for_query).
No pagination or result limits documented. search_tweets and search_wikipedia accept 'limit' param but no guidance on max, default behavior, or next_cursor for large result sets.