Chinese AI intelligence MCP server — daily curated signals from 11 Chinese-language sources, translated and scored for enterprise AI/tech readers
Lex Intel provides 4 read-only tools for Chinese AI intelligence discovery. All tools have clear, descriptive docstrings and well-structured input schemas. However, there are significant gaps in parameter descriptions, output schema documentation, and error handling guidance. The schema quality varies: lex_search_articles has excellent parameter descriptions and constraints (enum for category, min/max for limit/relevance); lex_get_briefing has minimal parameter descriptions; lex_get_signals lacks output schema documentation; lex_list_sources has no parameters but no explicit output schema. All tools are read-only (safe), but lack actionable error messages and recovery guidance. The codebase shows thoughtful design (e.g., semantic search, time-windowed queries, filtering) but falls short of production-grade tool definition standards expected for agent composition. Parameter descriptions average ~60 chars (below the 72-char baseline for A+ tools); tool descriptions average ~180 chars (within range but some lack usage context). Output schemas are inferred from code but not formally documented in tool metadata.
Get the latest Chinese AI morning briefing (Bloomberg-style). Use this when you need a summary of what happened in Chinese AI/tech today or on a specific date. The briefing has five sections: LEAD (biggest story), PATTERNS (cross-source themes), SIGNALS (emerging trends), WATCHLIST (developing stories), DATA (key numbers).
Get emerging signals and high-relevance developments from recent days. Use this to understand what trends are building in Chinese AI/tech. Returns articles scored 4+ (significant) or 5 (critical) from the last N days, grouped by category.
List all Chinese AI/tech sources Lex monitors and their health. Use this to understand what data sources feed the intelligence pipeline and how recently each was successfully scraped.
Search the Chinese AI article corpus by semantic similarity. Use this when looking for articles about a specific topic, company, technology, or event in the Chinese AI/tech ecosystem.
Output schemas not formally documented in tool metadata. Descriptions state 'Returns dict with...' but JSON Schema for outputs is missing from tool definitions. Agents must infer structure from prose descriptions.
Error handling lacks actionable recovery guidance. Tool implementations (lib.vectors.search, lib.db._get_client) are not visible, so error cases are unknown. No error-recovery examples in descriptions (e.g., 'If search fails, try a shorter query' or 'If no briefing exists, call get_signals instead').
Parameter 'date' in lex_get_briefing lacks format specification. Description says 'ISO date string (YYYY-MM-DD)' but no validation guidance for invalid formats (e.g., '2024-13-01'). No constraint in schema preventing future dates.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 61 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 0 | - | v1 |
lex_get_signals 'days' and 'min_relevance' parameters have constraints (1-30, 1-5) stated in descriptions but not in schema. Schema shows type but no minimum/maximum properties. LLMs cannot enforce these bounds without reading prose.
lex_list_sources has empty input schema ({}), no description of what sources are returned, no fields listed. Output structure is undocumented, agent cannot determine what 'health' data includes (last_scrape_time? error_count? uptime?).
lex_get_briefing 'date' parameter is optional with default=null, but description and implementation imply absence means 'most recent'. No explicit guidance: 'Omit to fetch the most recent briefing, or provide YYYY-MM-DD to fetch a specific day.'