analyze_query_results
Execute database queries and analyze results using AI to extract insights, summarize findings, and identify patterns from data.
Instructions
Execute a query and analyze its results using AI
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| results_limit | No | ||
| analysis_type | No | summary |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/main.py:310-334 (handler)The MCP tool handler for 'analyze_query_results'. Executes the SQL query via Snowflake client, then delegates AI analysis to the OpenAI client.
@mcp.tool() async def analyze_query_results(query: str, results_limit: int = 100, analysis_type: str = "summary", ctx: Context = None) -> str: """Execute a query and analyze its results using AI""" await ctx.info(f"Analyzing query results for: {query[:100]}...") try: # Execute the query first snowflake = await get_snowflake_client() result = await snowflake.execute_query(query, results_limit) if not result.success: await ctx.error(f"Query failed: {result.error}") return f"Query execution failed: {result.error}" # Analyze the results openai = await get_openai_client() analysis = await openai.analyze_query_results(query, result.data, analysis_type) await ctx.info("Successfully analyzed query results") return analysis except Exception as e: logger.error(f"Error analyzing query results: {str(e)}") await ctx.error(f"Failed to analyze query results: {str(e)}") raise - src/openai_client.py:75-127 (helper)Supporting helper in OpenAIClient class that performs the AI-powered analysis of query results using OpenAI chat completions.
async def analyze_query_results( self, query: str, results: List[Dict[str, Any]], analysis_type: str = "summary" ) -> str: """Analyze query results using AI""" # Limit data for analysis to avoid token limits sample_data = results[:10] if len(results) > 10 else results system_prompt = f""" You are a data analyst. Analyze the provided query results and provide insights. Analysis type: {analysis_type} Provide a clear, concise analysis with: - Key findings and patterns - Statistical insights if applicable - Recommendations or next steps - Any anomalies or interesting observations Format your response in a clear, professional manner. """ user_prompt = f""" SQL Query: {query} Results ({len(results)} rows total, showing sample): {json.dumps(sample_data, indent=2, default=str)} Please analyze these results and provide insights. """ try: response = await self.client.chat.completions.create( model=self.model, messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.3, max_tokens=1000 ) analysis = response.choices[0].message.content.strip() logger.info("Generated data analysis") return analysis except Exception as e: logger.error(f"Error analyzing data: {str(e)}") raise Exception(f"Failed to analyze data: {str(e)}")