developer-toolsanalytics-data

SolarWinds Observability Logs

jakenuts

by jakenuts

Integrate with SolarWinds Observability Logs for advanced log searching, visualization, and analysis—ideal for DevOps an

Integrates with SolarWinds Observability logs, providing tools for searching, visualizing, and analyzing log data with advanced filtering options and customizable time ranges for DevOps and IT operations teams.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Works with existing SolarWinds infrastructureIncomplete - missing structured data search

best for

  • / DevOps teams monitoring application logs
  • / IT operations troubleshooting system issues
  • / Analyzing log patterns and trends over time

capabilities

  • / Search SolarWinds Observability logs with filters
  • / Generate histogram visualizations of log events
  • / Filter logs by time range, group, and entity
  • / View log entries with timestamps and hostnames

what it does

Connects to SolarWinds Observability to search and analyze log data with filtering and time-based visualization capabilities. Currently has limitations with structured data search.

about

SolarWinds Observability Logs is a community-built MCP server published by jakenuts that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with SolarWinds Observability Logs for advanced log searching, visualization, and analysis—ideal for DevOps an It is categorized under developer tools, analytics data.

how to install

You can install SolarWinds Observability Logs in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

SolarWinds Observability Logs is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

SolarWinds Logs MCP Server

A Model Context Protocol (MCP) server for accessing and visualizing SolarWinds Observability logs.

Note -

This server is currently incomplete as it does not support structured data search (a limitation of the REST API?). I'm uncertain if it also needs to accept a data center to use in the api endpoint calls. Will address both when time allows (needed it for a real work problem, have to fix that first)

Tools

search_logs

Search SolarWinds Observability logs with optional filtering

  • Takes search parameters including filter, time range, and pagination options
  • Returns formatted log entries with timestamps, hostnames, and messages
  • Supports advanced filtering by group, entity, and more
  • Default search range is the last 24 hours

visualize_logs

Generate a histogram json response for of log events

  • Formatted for Claude and canvas representations
  • Configurable time intervals (minute, hour, day)
  • Supports UTC or local time zones
  • Customizable query filters and time ranges
  • Default visualization range is the last 24 hours

Resources

SolarWinds Log Search

  • URI Template: solarwinds://{query}/search
  • Returns log entries matching the specified query
  • Example: solarwinds://error/search

Installation

Optionally install from npm:

npm install -g mcp-solarwinds

Or clone and build from source:

git clone https://github.com/@jakenuts/mcp-solarwinds.git
cd mcp-solarwinds
npm install
npm run build

Or just use npx in your configurations

For Cline VSCode Extension

Add to %APPDATA%/Code - Insiders/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json:

{
  "mcpServers": {
    "solarwinds": {
      "command": "npx",
      "args": ["-y", "mcp-solarwinds"],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      },
      "autoApprove": ["search_logs", "visualize_logs"]
    }
  }
}

For Claude Desktop

Add to the appropriate config file:

Windows: %APPDATA%/Claude/claude_desktop_config.json MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "solarwinds": {
      "command": "npx",
      "args": ["-y", "mcp-solarwinds"],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      }
    }
  }
}

Special Windows Configuration

If you encounter the ENOENT spawn npx issue on Windows, use this alternative configuration that specifies the full paths:

{
  "mcpServers": {
    "solarwinds": {
      "command": "C:\Users\[username]\AppData\Roaming\
vm\[node-version]\
ode.exe",
      "args": [
        "C:\Users\[username]\AppData\Roaming\
pm\
ode_modules\
pm\bin\
px-cli.js",
        "-y",
        "mcp-solarwinds"
      ],
      "env": {
        "SOLARWINDS_API_TOKEN": "your-api-token"
      }
    }
  }
}

Configuration

The SolarWinds Observability MCP server requires an API token to authenticate with the SolarWinds Observability API.

Configuration Methods

There are multiple ways to provide the API token:

  1. MCP Settings Configuration (Recommended): Configure the token in your MCP settings file
  2. Environment Variable: Set the SOLARWINDS_API_TOKEN environment variable
  3. Local .env File (For Testing): Create a .env file in the project root with SOLARWINDS_API_TOKEN=your-token

For local testing, you can:

  1. Copy .env.example to .env and add your token
  2. Run the example script: node examples/local-test.js

Tool Usage Examples

search_logs

Basic search:

{
  "filter": "error"
}

Advanced search with time range and pagination:

{
  "filter": "error",
  "entityId": "web-server",
  "startTime": "2025-03-01T00:00:00Z",
  "endTime": "2025-03-05T23:59:59Z",
  "pageSize": 100,
  "direction": "backward"
}

visualize_logs

Basic histogram (ASCII chart):

{
  "filter": "error",
  "interval": "hour"
}

Advanced visualization (ASCII chart):

{
  "filter": "error",
  "entityId": "web-server",
  "startTime": "2025-03-01T00:00:00Z",
  "endTime": "2025-03-05T23:59:59Z",
  "interval": "day",
  "use_utc": true
}

Claude visualization (JSON format):

{
  "filter": "error",
  "interval": "hour",
  "format": "json"
}

The JSON format returns data that Claude can visualize as a chart:

{
  "timeRanges": ["12:02", "12:03", "12:04", "12:05", "12:06", "12:07", "12:08", "12:09"],
  "counts": [261, 47, 48, 48, 31, 262, 270, 33],
  "total": 1000,
  "queryParams": {
    "query": "error",
    "startTime": "2025-03-05T00:00:00.000Z",
    "endTime": "2025-03-05T23:59:59.000Z"
  }
}

Development

Install dependencies:

npm install

Build the server:

npm run build

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. The MCP Inspector provides helpful debugging tools:

npm run debug:inspector

This will provide a URL to access the inspector in your browser, where you can:

  • View all MCP messages
  • Inspect request/response payloads
  • Test tools interactively
  • Monitor server state

For local testing without the MCP framework:

# Create a .env file with your token
cp .env.example .env
# Edit .env to add your token
# Run the example script
node examples/local-test.js

Technical Details

  • Built with TypeScript and the MCP SDK
  • Uses axios for API communication
  • Supports ISO 8601 date formats for time ranges
  • Generates ASCII histograms for log visualization
  • Default search range: last 24 hours
  • Default page size: 50 logs
  • Supports multiple authentication methods

FAQ

What is the SolarWinds Observability Logs MCP server?
SolarWinds Observability Logs is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for SolarWinds Observability Logs?
This profile displays 65 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.565 reviews
  • Pratham Ware· Dec 28, 2024

    SolarWinds Observability Logs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Advait Sharma· Dec 28, 2024

    SolarWinds Observability Logs reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Michael Mensah· Dec 20, 2024

    We wired SolarWinds Observability Logs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Aditi Rahman· Dec 16, 2024

    We evaluated SolarWinds Observability Logs against two servers with overlapping tools; this profile had the clearer scope statement.

  • Advait Wang· Dec 8, 2024

    SolarWinds Observability Logs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Tariq Park· Nov 27, 2024

    We wired SolarWinds Observability Logs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Sakshi Patil· Nov 19, 2024

    We evaluated SolarWinds Observability Logs against two servers with overlapping tools; this profile had the clearer scope statement.

  • Advait Kapoor· Nov 19, 2024

    Useful MCP listing: SolarWinds Observability Logs is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Kofi Jain· Nov 11, 2024

    SolarWinds Observability Logs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Zara Chawla· Nov 7, 2024

    SolarWinds Observability Logs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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