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Spanly

Spanly monitors MCP servers with real-time traces, logs, metrics, error tracking, and protocol-level observability for AI agents and developer tools.

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Spanly is an AI infrastructure observability platform built specifically for Model Context Protocol (MCP) servers. It helps engineering teams monitor, debug, and analyze every interaction between AI agents, clients, and MCP servers through protocol-level visibility. Rather than replacing traditional application performance monitoring (APM) tools, Spanly complements them by capturing MCP-specific requests, responses, tool calls, prompts, resources, sessions, and errors.

The platform provides real-time tracing, performance analytics, error tracking, payload inspection, and monitoring dashboards without requiring application code changes. It supports multiple programming languages through SDKs, CLI tools, and Docker deployments, making it suitable for teams building production AI agents and MCP-based applications.

Features

MCP Protocol Observability

Monitor every MCP request, including tool calls, prompts, resource access, and session activity through protocol-aware dashboards.

Real-Time Tracing

Capture detailed traces showing request timing, execution flow, latency, payloads, and server responses for every MCP interaction.

Error Tracking

Automatically detect, group, and analyze recurring MCP errors with detailed diagnostics to simplify debugging.

Performance Analytics

Measure request volume, latency, P50, P95, P99 response times, throughput, and overall server performance across production deployments.

Full Payload Inspection

Inspect complete JSON-RPC requests and responses, including arguments, outputs, timings, and error messages, instead of only application traces.

Flexible Deployment

Instrument MCP servers using SDKs for TypeScript and Python, a command-line wrapper, or Docker sidecars without modifying application code.

Data Residency

Choose US or EU regions for telemetry storage while maintaining regional data residency for compliance requirements.

Open-Source SDK

Spanly provides open-source SDKs and CLI tools under the Apache 2.0 license, allowing developers to integrate observability into existing AI infrastructure.

How It Works

Sign up for a Spanly account.

Install the SDK, CLI, or Docker integration.

Connect your MCP server.

Allow Spanly to automatically collect MCP protocol traffic.

View traces, logs, errors, metrics, and performance dashboards.

Use the collected insights to identify bottlenecks and improve AI application reliability.

Use Cases

  • Engineering teams building MCP servers.
  • AI agent developers.
  • LLM application developers.
  • DevOps and Site Reliability Engineering teams.
  • Organizations deploying production AI infrastructure.
  • Teams monitoring Claude, Cursor, Codex CLI, Copilot, Windsurf, and similar AI clients.
  • Companies needing protocol-level AI observability.

Pricing

Spanly offers a Free tier with no credit card required. The official website also provides open-source SDKs and CLI tools. Detailed paid pricing plans are not clearly listed on the official website.

Strengths

  • Purpose-built for MCP observability.
  • No application code changes required.
  • Protocol-level visibility beyond traditional APM tools.
  • Real-time tracing and detailed payload inspection.
  • Open-source SDKs and CLI.
  • Supports multiple deployment methods.
  • Regional data residency options for compliance.

Drawbacks

  • Focused specifically on MCP-based AI systems.
  • Less relevant for applications that do not use MCP.
  • Advanced enterprise features may require a commercial plan.
  • Teams unfamiliar with MCP may need time to understand protocol-specific metrics.

Comparison with Other Platforms

Unlike traditional observability platforms such as Datadog, New Relic, or Sentry, Spanly focuses specifically on Model Context Protocol traffic. It captures complete MCP requests, responses, tool calls, prompts, resources, and protocol payloads, giving engineering teams much deeper visibility into AI agent interactions while complementing existing APM solutions rather than replacing them.

Customer Reviews and Testimonials

Customer reviews and testimonials are not clearly available on the official website.

Conclusion

Spanly is a specialized observability platform for organizations building AI applications with the Model Context Protocol. By providing protocol-aware monitoring, real-time traces, error tracking, payload inspection, and performance analytics without requiring code modifications, it enables engineering teams to operate AI agents with greater reliability and confidence. It is particularly valuable for developers and enterprises deploying MCP-based AI infrastructure in production.

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