A
licenseB
qualityC
maintenanceEnables AI agents to interact with Google BigQuery databases through natural language queries and schema exploration.
3
10
MIT
karte-datahub-mcp
by YusukeYajima
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TDQS
A3.8/5.0
Scored across 4 tools
Disambiguation4/5
The tools are mostly distinct: count_karte_events counts events, describe_karte_events_schema provides metadata, execute_karte_sql runs custom SQL, and query_karte_events retrieves event data. However, query_karte_events and execute_karte_sql could be confused as both retrieve data, though execute_karte_sql is more flexible with custom SQL while query_karte_events has structured parameters. The descriptions help clarify their boundaries.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case: count_karte_events, describe_karte_events_schema, execute_karte_sql, and query_karte_events. The naming is predictable and readable throughout the set.
Tool Count4/5
With 4 tools, the count is reasonable for a data query server focused on KARTE events. It covers core operations like counting, describing schema, querying, and custom SQL execution. It might benefit from additional tools for more advanced analytics or data manipulation, but the scope is well-defined and manageable.
Completeness3/5
The toolset covers basic read operations for KARTE event data, including counting, schema description, structured querying, and custom SQL. However, there are notable gaps: no tools for writing or updating data (e.g., inserting or modifying events), and limited to event data without broader data management capabilities. Agents can work around this for query tasks but may fail for data manipulation needs.
Maintenance
ActivityInactive
ResponsivenessNo issues