Datalust Seq MCP Server

Overview

The mcp-datalust-seq-mcp-dotnet server provides a Model Context Protocol (MCP) wrapper around the Datalust Seq logging platform. By exposing Seq’s RESTful API through MCP, developers can let AI assistants query, ingest, and manipulate log data directly from the assistant’s context. This bridges the gap between structured log analytics and conversational AI, enabling richer debugging, monitoring, and observability workflows without leaving the chat interface.

The server translates MCP calls into Seq API requests. When an AI client sends a prompt that references log queries or ingestion commands, the MCP server forwards those calls to Seq, receives JSON responses, and returns them as structured resources. This allows assistants to perform actions such as searching for error patterns, aggregating metrics, or even pushing new log events in real time. The integration is valuable for teams that already rely on Seq for centralized logging but want to harness AI‑driven insights, anomaly detection, or automated remediation.

Key capabilities include:

• Resource discovery: The MCP server advertises Seq endpoints (e.g., , ) as resources that clients can enumerate and invoke.
• Tool execution: It exposes tools for querying logs, creating events, or retrieving index information, making these operations first‑class citizens in the assistant’s toolbox.
• Prompt templates: Predefined prompts guide users to construct effective log queries, reducing the learning curve for non‑technical stakeholders.
• Sampling and pagination: The server handles large result sets by streaming samples or paginating responses, ensuring that assistant interactions remain responsive.

Typical use cases involve:

• Debugging: A developer asks the AI to fetch recent crash logs or filter by a specific exception, and the assistant returns concise summaries directly from Seq.
• Operational monitoring: Ops teams can query trend data or alert thresholds via conversational commands, receiving actionable insights without switching to a log viewer.
• Incident response: Automated playbooks can trigger the MCP server to ingest corrective logs or flag affected services, integrating seamlessly with incident management workflows.

Because MCP is language‑agnostic, the Seq wrapper can be deployed in any environment that supports .NET Core or Docker. Once running, it becomes a drop‑in component of an AI workflow: the assistant simply references the MCP endpoint, and all log interactions are handled transparently. This eliminates manual API plumbing, centralizes logging logic, and unlocks a new dimension of AI‑assisted observability.