VictoriaLogs MCP Server

Overview

The VictoriaLogs MCP Server bridges the power of VictoriaLogs—a high‑performance, cloud‑native log storage and analytics platform—with AI assistants that understand the Model Context Protocol (MCP). By exposing VictoriaLogs’ read‑only APIs as MCP tools, the server lets conversational agents query logs, explore metadata, and retrieve diagnostic statistics directly from an existing VictoriaLogs deployment. This integration removes the need for manual API calls or custom code, enabling developers to embed log analysis and observability into natural‑language workflows.

What Problem It Solves

Modern software stacks generate terabytes of log data, and troubleshooting often requires repetitive API queries or manual UI navigation. Engineers typically spend significant time formulating complex query strings, interpreting results, and correlating logs with metrics. The MCP server automates these interactions by translating user intent into precise API requests, returning structured results that can be processed or displayed by the assistant. This reduces cognitive load and accelerates debugging, monitoring, and incident response.

Core Functionality

• Full Read‑Only API Coverage: The server supports all VictoriaLogs read APIs, mirroring the functionality available in the Web UI. Users can query logs, list streams and fields, inspect field values, and retrieve instance parameters.
• Embedded Documentation Search: It ships with up‑to‑date VictoriaLogs documentation, enabling the assistant to answer queries about query syntax, field semantics, or configuration options without external network access.
• Metrics‑Style Log Statistics: Beyond raw log data, the server can return aggregated statistics—such as counts, rates, or error distributions—that are useful for monitoring dashboards and alerting.

Real‑World Use Cases

• Incident Investigation: A security analyst asks the assistant to “show me all authentication failures in the last hour for service X.” The MCP server executes the appropriate query and returns a concise list, which can be further filtered or visualized.
• Observability Automation: Ops teams embed the server into chatops tools, allowing a single command like “list all streams with high error rates” to surface actionable insights.
• Developer Onboarding: New engineers can learn about log schema and query patterns by asking the assistant to “explain the fields in stream ‘api‑logs’,” receiving documentation excerpts and example queries.

Integration with AI Workflows

Because the server follows MCP, any client that understands the protocol—Claude Desktop, Claude Web, or custom agents—can consume its capabilities. The assistant can combine log queries with other MCP servers (e.g., metric stores, documentation search) to construct composite answers. For example, a prompt might request “compare the error rate of service A in production versus staging,” and the assistant will orchestrate calls to VictoriaLogs for both environments, merge results, and present a side‑by‑side comparison.

Unique Advantages

• Zero Code Interaction: Users interact purely through natural language; no need to write HTTP requests or parse JSON manually.
• Consistent API Surface: The server’s tools mirror the official Web UI, ensuring that any feature available in the UI is equally accessible via MCP.
• Offline Documentation: The embedded docs search allows agents to answer questions about query syntax or configuration even when the network is restricted, enhancing reliability in secure environments.
• Scalable Deployment: The server can run on a single node or a cluster, matching VictoriaLogs’ deployment model and supporting high‑availability setups.

In summary, the VictoriaLogs MCP Server transforms log analytics into an AI‑friendly service, enabling developers and operators to harness the full power of VictoriaLogs through conversational interfaces and automated workflows.