[MISSING] Observability & Monitoring Integration [Size: M, Priority: S... #14
Description
# 🚀 [Enhancement] Observability & Monitoring Integration for solana-mcp-server
---
### 🔥 Priority: High
### 📏 Estimated Size: Medium
### 🏷️ Labels: enhancement, missing-feature, monitoring, observability
### 🎯 Milestone: AI Development Plan Milestone #1
---
## 🧐 Problem Statement
The **solana-mcp-server** currently lacks integrated observability and monitoring capabilities. Without real-time metrics and logging, it is challenging to:
- Detect performance degradations or anomalies proactively
- Understand system health during high load or failure states
- Drill down into issues occurring in production environments
- Provide actionable insights to developers and DevOps
This gap increases the risk of unnoticed failures and complicates troubleshooting in live deployments.
---
## 🔍 Technical Context
`solana-mcp-server` is a Rust-based Model Context Protocol (MCP) server exposing Solana RPC functionality. It supports multiple deployment environments including local, Docker, and Kubernetes. While the codebase has foundational testing and documentation for RPC methods, it currently lacks:
- Metrics instrumentation (e.g., request latencies, error rates)
- Integration with popular monitoring stacks (Prometheus for metrics scraping, Grafana for dashboards)
- Structured logging with context for troubleshooting
Rust ecosystem offers mature crates for instrumentation and metrics export (e.g., `prometheus`, `tracing`, `tokio-metrics`), which can be leveraged for seamless integration.
---
## 🎯 Goals & Deliverables
- Integrate Prometheus metrics endpoint to expose key server metrics
- Instrument critical code paths to collect metrics such as:
- RPC request counts and latencies per method
- Error rates and types
- Resource utilization (optional: memory, CPU if feasible)
- Implement structured logging with `tracing` crate or equivalent, augment logs with request context and error details
- Provide Grafana dashboards templates or configuration snippets to visualize server health and load
- Ensure metrics and logs are compatible with containerized and Kubernetes environments for easy integration with existing monitoring stacks
- Fully document the observability setup, usage, and troubleshooting guidance
---
## 🛠️ Implementation Steps
1. **Research & Design**
- Review best practices for observability in Rust microservices
- Evaluate crates for Prometheus instrumentation (`prometheus`, `metrics-exporter-prometheus`) and logging (`tracing`, `tracing-subscriber`)
- Design metrics schema (histograms, counters, gauges) tailored to RPC server use cases
- Define endpoint to expose `/metrics` in Prometheus format
2. **Instrumentation**
- Add instrumentation hooks at RPC handler boundaries to measure:
- Total request counts (per RPC method)
- Request latency histograms
- Error counters (by error type)
- Integrate structured logging capturing:
- Request IDs or correlation IDs
- Method names
- Error messages and stack traces where applicable
- Ensure minimal overhead and thread safety in instrumentation code
3. **Metrics Endpoint**
- Expose `/metrics` HTTP endpoint accessible by Prometheus server
- Secure endpoint as needed (e.g., via network policies or authentication if applicable)
4. **Dashboards**
- Create Grafana dashboard JSON or YAML files visualizing:
- RPC request rate trends
- Latency percentiles (p50, p95, p99)
- Error counts over time
- Resource usage if available
5. **Testing**
- Write unit and integration tests verifying:
- Metrics are correctly recorded and exposed
- Logging outputs expected structured data
- Perform load tests simulating realistic RPC traffic to validate metrics accuracy and performance impact
6. **Documentation**
- Update README and docs/observability.md with:
- Setup instructions for Prometheus and Grafana integration
- Explanation of metrics and logs semantics
- Examples of queries and dashboard usage
- Troubleshooting tips
7. **Code Review & Merge**
- Follow Rust best practices and repository style guidelines
- Ensure clear commit messages and PR description referencing this issue
- Solicit feedback from maintainers and iterate
---
## ✅ Acceptance Criteria
- [ ] Prometheus metrics endpoint `/metrics` is exposed and serving valid metrics
- [ ] RPC methods are instrumented with counters and histograms for request counts and latencies
- [ ] Structured logging with request context is integrated and configurable via environment variables
- [ ] Grafana dashboard files provided and documented
- [ ] Unit and integration tests cover metrics and logging features, passing in CI
- [ ] Documentation is complete, clear, and includes setup and usage instructions
- [ ] Code merged following successful review and no regressions introduced
---
## 🧪 Testing Requirements
- Automated tests simulating RPC calls verifying metrics increment and latency measurement
- Validation of `/metrics` endpoint returning Prometheus-formatted data using tools like `curl` or `promtool`
- End-to-end test deploying server with Prometheus + Grafana stack to verify dashboard visualization
- Performance benchmark to ensure instrumentation overhead is within acceptable limits (<5% latency increase)
---
## 📚 Documentation Updates
- Add `docs/observability.md` detailing:
- Metrics schema and Prometheus integration
- How to enable, configure, and secure logging and metrics
- Grafana dashboard setup and usage guide
- Update main README to reference observability features and link to detailed docs
- Add example configuration snippets for Kubernetes and Docker environments demonstrating monitoring setup
---
## ⚠️ Potential Challenges
- Balancing instrumentation detail with performance and resource overhead
- Securing the metrics endpoint in multi-tenant or public deployments
- Correlating logs and metrics for complex async RPC flows
- Ensuring compatibility with existing deployment scripts and environments
- Coordinating with DevOps for monitoring stack integration and alerting rules
---
## 🔗 Resources & References
- [Prometheus Rust Client Crate](https://crates.io/crates/prometheus)
- [tracing - Application-level tracing for Rust](https://crates.io/crates/tracing)
- [Metrics Exporter Prometheus](https://crates.io/crates/metrics-exporter-prometheus)
- [Grafana Dashboards examples](https://grafana.com/grafana/dashboards)
- [Rust Observability Best Practices](https://rust-lang.github.io/wg-async-foundations/vision/observability.html)
- [Prometheus Instrumentation Best Practices](https://prometheus.io/docs/practices/instrumentation/)
- [Example Rust Microservice with Prometheus](https://github.com/tikv/rust-prometheus-example)
---
# Let’s turn our solana-mcp-server into a powerhouse of insight and reliability!
Every metric collected, every log structured, brings us closer to zero-downtime and rock-solid production readiness.
**Grab your favorite IDE, summon your inner observability wizard 🧙♂️, and let’s make monitoring magical!**