Design an intelligent, modular, data-agnostic agent capable of processing diverse traffic-related data using a dynamic suite of up to 100 MCP (Model Context Protocol) tools. The system supports high-throughput workloads, tool chaining, visualization, and secure API-based integration into larger architectures.
Tech: Python (FastAPI/Flask), Docker
Purpose: Exposes REST endpoints (e.g., /analyze) to accept input data and context
Responsibilities:
- Parse & validate request payloads
- Forward to Router for tool assignment
- Manage response formatting, chaining, and error handling
- Emit job/run events for visualization dashboards
Modes:
- Rule-Based Routing: Based on input types, keywords, metadata
- ML-Based Routing: Intent classification using ML/LLM
- Hybrid Routing: Fast rules + fallback to model-driven dispatch
Responsibilities:
- Decide best-fit MCP tool or tool chain
- Send routing metadata to Dispatcher
- Generate trace identifiers and step metadata for visualization
Function: Orchestrates tool invocations
Protocols Supported:
- REST (default)
- gRPC (for low-latency/high-throughput)
- Kafka/RabbitMQ (for async workloads)
Responsibilities:
- Handles retries, timeouts
- Resolves tool endpoint from Tool Registry
- Loads authentication headers + payloads
- Publishes step progress and results for visualization and tracing
Purpose: Executes tool pipelines
Approach:
- DAG-based chaining (e.g., Anomaly Detection -> Clustering)
- Agent-guided dynamic chaining
Responsibilities:
- Manage data hand-off between tools
- Track intermediate results and state
- Report stage transitions to the visualization subsystem
Storage: Local JSON file or database
Fields:
- Tool name, task type, supported data types
- Endpoint URL & communication protocol
- Version, metadata, health status
- Visualization metadata: category, icon, color code, owner
Standardized Schema:
{
"input": { ... },
"context": { ... }
}Returns:
{
"status": "success",
"output": { ... },
"meta": { ... }
}This architecture enables scalable, flexible, and intelligent data analysis for diverse and demanding workloads.
Purpose: Provide full transparency into running processes and tool interactions.
Components:
- Run Service / Jobs API: Tracks all runs, jobs, steps, and statuses; exposes REST + WebSocket endpoints (e.g.,
/v1/runs,/ws/runs/{id}) - Dashboard UI: Shows tool catalog, live runs, DAG visualizer (tool chaining), job progress, and metrics
- Tracing: OpenTelemetry + Jaeger/Tempo for distributed traces
- Logs: ELK/OpenSearch for structured logs (linked to runs)
- Metrics: Prometheus + Grafana for latency, throughput, error rate
- Lineage: OpenLineage/Marquez integration for dataset–tool–output provenance
User View:
- Tool catalog with capabilities, schema, and status
- Real-time run status, progress bars, ETA, per-step logs, and trace links
- DAG view showing current pipeline execution flow
Options:
- REST (development, small-scale)
- gRPC (binary RPCs, high throughput)
- Kafka (buffered, async tasks)
Concurrency:
- Python asyncio / Celery for parallel calls
- K8s for container auto-scaling
- KEDA for queue-based scaling
Auth:
- JWT-based access control
- HMAC signing for internal tool calls
Transport:
- TLS encryption for REST/gRPC
- Kafka: TLS + SASL
Audit Logging:
- Request, tool, user, timestamp, result status
- Integrated into visualization UI for admin access
Tracing: OpenTelemetry spans per request and tool Metrics: Prometheus collectors for latency, throughput, queue lag Logging: Structured, tenant-aware JSON logs SLOs & Alerts: Alertmanager for anomalies and health checks
Governance:
- Versioning, tool lifecycle tracking
- Canary releases and shadow runs
- UI displays deprecation notices and tool change logs
Input Types:
- Tabular (CSV, Excel, SQL result)
- Text (incident reports, logs)
- JSON/XML (API or IoT device input)
- Images (traffic cams)
- Geo (GeoJSON, GPS points)
Supported Tasks:
- Anomaly Detection
- Incident Detection
- Time-Series Forecasting
- Descriptive Stats & Comparison
- Classification / Regression
- Clustering & Feature Engineering
- Geospatial Mapping & Analysis
- Containerized with Docker for each tool and core module
- Use Docker Compose or Kubernetes for orchestration
- Includes visualization stack (Grafana, Jaeger, ELK) and UI dashboard
- Designed to plug into larger systems as a callable API service
- Future-proofed for more advanced ML planning agents (e.g., LLM planner)
This project includes a React-based frontend for uploading datasets, running anomaly detection, and visualizing results.
- Open a terminal and navigate to the
frontendfolder:cd frontend - Install dependencies:
npm install - Start the development server:
npm start - Open your browser and go to:
http://localhost:3000
Note: Make sure the MCP agent backend is running at http://localhost:8080 before using the frontend.
- Upload CSV dataset
- Run anomaly detection
- View detected anomalies in a table and chart
- See summary statistics
You can extend the UI for more tools, real-time updates, and advanced visualizations as needed.
- Scaffold base API + agent logic
- Implement router (rule-based first)
- Add 3–5 MCP tools with REST endpoints
- Integrate Run Service + WebSocket for real-time progress
- Add UI layer for visualization (DAG, runs, logs)
- Package with Docker Compose for local testing
- Extend with gRPC + Kafka for async cases
python -m venv t_venv
t_venv\Scripts\activate
python -m pip install --upgrade pip
pip install -r requirements.txt