FreeOnePlus commented on issue #19:
URL:
https://github.com/apache/doris-mcp-server/issues/19#issuecomment-3055827444
Thank you for the suggestion to add ADBC (Arrow Database Connectivity) tools
for large-scale data retrieval and computation. After careful analysis of the
technical capabilities and use case scenarios, here's my conclusion:
## 🚫 **Decision: We will NOT implement ADBC tools at this time**
### **Primary Reasoning:**
#### 1. **MCP Design Philosophy Conflict**
- **MCP tools are designed for LLM consumption**, not human data processing
- **Large datasets (millions of rows) would overwhelm LLMs**, causing:
- Token limit exceeded errors
- Extremely high API costs
- Processing timeouts
- Degraded response quality
#### 2. **Limited Practical Use Cases in MCP Context**
While ADBC offers 10-100x performance improvements for large data transfers,
the scenarios where this matters in MCP are very limited:
**❌ Invalid scenarios:**
```python
# This makes no sense in MCP context
get_large_dataset(sql="SELECT * FROM orders", max_rows=1000000)
# → Returns 1M rows to LLM → LLM crashes/expensive/useless
```
**✅ Potentially valid scenarios:**
```python
# Data export for human use (but limited value)
export_data_to_file(sql="...", output_path="...")
# → Returns only file metadata to LLM, not data itself
```
#### 3. **Existing Tools Already Sufficient**
Our current MySQL-based tools effectively handle MCP use cases:
- **Small to medium queries** (< 10K rows): Performance difference negligible
- **Large data analysis**: Can be done via SQL aggregation functions
- **Data sampling**: Achievable with `LIMIT` and statistical queries
- **Summary statistics**: Computable using SQL `GROUP BY` and aggregate
functions
#### 4. **Better Alternatives Available**
Instead of ADBC, we can enhance existing tools:
```python
# Enhanced data analysis without ADBC
def analyze_large_dataset(sql: str) -> Dict:
"""
1. Execute COUNT(*) to get total rows
2. Execute statistical queries for summaries
3. Sample representative data with LIMIT
4. Return concise analysis to LLM
"""
```
#### 5. **Implementation Complexity vs. Value**
- **High complexity**: Additional dependencies, configuration, error handling
- **Limited ROI**: Most MCP interactions involve small datasets
- **Maintenance burden**: ADBC requires specialized knowledge and debugging
### **Recommended Alternative Approach:**
Rather than implementing ADBC, we should focus on:
1. **Enhanced Data Sampling Tools**
- Intelligent sampling strategies
- Statistical summary generation
- Data quality assessment
2. **Improved Query Optimization**
- Query performance suggestions
- Automatic query limiting for safety
- Smart aggregation recommendations
3. **Better Data Visualization**
- Chart generation for trends
- Distribution analysis
- Anomaly detection
### **Conclusion:**
While ADBC is excellent for data engineering and analytics workflows, **it
doesn't align with MCP's design patterns and LLM interaction models**. The
performance benefits are overshadowed by the fundamental mismatch with how LLMs
consume and process data.
We should instead focus on making our existing tools smarter and more
efficient for the actual use cases that MCP serves.
---
**Feel free to share this analysis with the user. The key message is that
ADBC solves a different problem than what MCP is designed for.**
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