developer-toolsanalytics-data

Cribl Stream

pebbletek

by pebbletek

Streamline DevOps workflows by managing configurations, pipelines, and metrics with Cribl Stream for optimized data proc

Integrates with Cribl Stream deployments to enable management of configurations, pipelines, sources, and system metrics for DevOps teams and data engineers monitoring data processing workflows.

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

Direct Cribl API integrationStandardized MCP tooling

best for

  • / DevOps teams managing Cribl deployments
  • / Data engineers monitoring processing workflows
  • / Organizations with complex data routing needs
  • / Teams automating Cribl administration tasks

capabilities

  • / Manage Cribl Stream configurations
  • / Monitor data processing pipelines
  • / Query system metrics and performance data
  • / Configure data sources and destinations
  • / View pipeline health and status
  • / Modify data processing workflows

what it does

Connects AI clients to Cribl Stream deployments for managing data processing configurations, pipelines, and monitoring system metrics through a standardized API interface.

about

Cribl Stream is a community-built MCP server published by pebbletek that provides AI assistants with tools and capabilities via the Model Context Protocol. Streamline DevOps workflows by managing configurations, pipelines, and metrics with Cribl Stream for optimized data proc It is categorized under developer tools, analytics data.

how to install

You can install Cribl Stream 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

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

readme

Cribl MCP Server

npm version License

A Model Context Protocol (MCP) server that enables AI interactions with the Cribl API.

This server allows AI clients to discover and invoke data operations from a Cribl deployment, using standardised MCP tooling.

For detailed documentation, please see the docs/ directory. Here's a breakdown of the documentation files:

  • README.md (this file): Provides the highest-level introduction, quick start instructions, very basic examples, and links to the docs directory.
  • docs/configuration.md: Focuses specifically on the environment variables needed for configuration and explains the authentication modes. It's purely about setup.
  • docs/usage.md: Focuses on how to run the server (via npx or local development commands) and how to configure an MCP client to connect to it. It provides conceptual examples of invoking the tools.
  • docs/overview.md: Sits between the README and the specific config/usage docs. It details what the server offers in terms of authentication and, most importantly, provides a detailed reference guide to the available tools and their specific parameters/outputs, and future tools.

Getting Started

You can either install and run locally, or execute instantly via npx. npx saves you installing Git, dependencies, or any build tools. Both scenarios require you to have node later than version 16 installed:

Prerequisites

To run this server ensure the following:

  • Node.js is installed (version 16 or later recommended)
  • Internet access is available (to fetch the MCP server package from npm)

You can install Node.js from https://nodejs.org.

To verify your setup:

node -v
npx -v

Quick Start with npx

CRIBL_BASE_URL=https://your.cribl.cloud.instance CRIBL_AUTH_TYPE=cloud CRIBL_CLIENT_ID=abc123 CRIBL_CLIENT_SECRET=secret npx @pebbletek/cribl-mcp

Note: Running npx requires essential environment variables (like CRIBL_BASE_URL and authentication details) to be set either in your environment or passed directly on the command line for the server to connect successfully.

See docs/usage.md for advanced npx usage configuration, including relevant JSON for your MCP client, and required variables.

Local Development

  1. Browse to a high-level folder where you'll store the code base (cloning will create the containing folder). Clone the repo:
    git clone https://github.com/pebbletek/cribl-mcp.git
    cd cribl-mcp
    
  2. Install dependencies:
    npm install
    
  3. Copy the .env.example file and populate the required values:
    cp .env.example .env
    
  4. See usage.md > Local Development for running, building and starting the mcp server

See docs/configuration.md for details on environment variables and authentication, and docs/usage.md for running the server.

Available Tools

This server provides tools to interact with Cribl, including:

  • Listing Worker Groups/Fleets
  • Getting/Setting Pipeline configurations
  • Getting Source configurations
  • Restarting Worker Groups
  • Querying System Metrics (with filtering)

For a detailed list and usage instructions, see docs/usage.md.

Example Usage (Prompts)

Once connected via an MCP client (like Claude Desktop), you can interact with your Cribl instance using natural language prompts that leverage the available tools. Here are some examples:

  • Prompt: what sources are we ingesting?
    (you will need to allow the MCP client access to the tool)

    • Response will be something like:
      "To answer your question about ingested sources, let me check the available data sources in your Cribl Stream environment.
      <View result from cribl_getSources from Cribl (local)>

      Let me also check what pipelines are configured in your Cribl Stream environment:
      <View result from cribl_getPipelines from Cribl (local)>

      Based on the information I've retrieved from your Cribl Stream environment, here are the sources you are ingesting:
      Active Data Sources
      Your Cribl Stream instance has several configured data sources, with the following notable ones:

      1. HTTP/HTTPS Sources:
        HTTP source (port 10080)
        Cribl HTTP (port 10200)
        Splunk HEC (port 8088)...
      2. <etc.>

      Pipeline Configurations
      Your environment also has several pipelines configured for processing specific data types:

      1. Cisco ASA events - Filters and samples Cisco ASA security logs
      2. Cisco eStreamer data - Processes Cisco eStreamer data
      3. Palo Alto traffic events - Filters and samples Palo Alto traffic logs
      4. Windows Event Logs - Processes Windows security event logs

      These pipelines suggest you're ingesting network security logs from Cisco ASA firewalls, Cisco eStreamer (likely from Firepower/FTD devices), Palo Alto Networks firewalls, and Windows security events.
      Would you like more detailed information about any specific source or pipeline configuration?"

  • Prompt: what functions are processing the palo alto traffic?

    • Response:
      "Let me examine the functions that are processing the Palo Alto traffic in more detail:
      <View result from cribl_getPipelineConfig from Cribl (local)>

      Here's a detailed breakdown of how the Palo Alto traffic data is being processed in your Cribl Stream environment:
      Palo Alto Traffic Pipeline Functions
      1. Initial Filter (Eval):

      • Filter: sourcetype!='pan:traffic'
      • Description: Short-circuits (skips) all events that are NOT of sourcetype 'pan'
      • This is set as a final function, meaning if the condition is true, processing stops for that event

      2. Field Extraction (Regex Extract):

      • Extracts several key fields from the raw event using regex pattern matching:
        • __log_subtype: Identifies if the log is a start/end record
        • __src_zone and __dest_zone: Source and destination security zones
        • __transport: Transport protocol
        • __action: Action taken (allow, deny, etc.)
        • __bytes_in: Bytes received

      3. Filtering Events (Drop):

      • Filter: __log_subtype=='start'
      • Description: Drops all events with log subtype of 'start'
      • Rationale (from comment): "Drop events of log_subtype=start as they often have incorrect app assignment. Log_subtype=end contain better information."
      • etc."
  • Prompt: please update the pipeline to include start events as well as end events.

    • Response:
      "I'll update the Palo Alto traffic pipeline to include both "start" and "end" events by modifying the configuration. Let me make this change for you:
      <View result from cribl_getPipelineConfig from Cribl (local)>

      Now I'll update the pipeline configuration to keep both "start" and "end" events by disabling the drop function and updating the comments:
      <View result from cribl_setPipelineConfig from Cribl (local)>

      Now let's restart the worker group to apply these changes: <View result from cribl_restartWorkerGroup from Cribl (local)>

      The Palo Alto traffic pipeline has been successfully updated to include both "start" and "end" events. Here's what I modified:

      1. Disabled the Drop Function:

        • The function that was filtering out __log_subtype=='start' events has been disabled
        • This means both "start" and "end" session events will now be processed
      2. Updated the Comment:

        • Changed the comment from explaining why "start" events were being dropped to noting that both event types are now being kept for complete traffic visibility
      3. Restarted the Worker Group:

        • Applied the changes by restarting the Cribl Stream worker group

      The updated pipeline will now process both "start" and "end" events from your Palo Alto firewalls, giving you complete visibility into traffic sessions from initiation through completion. All other processing functions remain in place, including:

      • Field extraction for key traffic attributes
      • Sampling for zero-byte traffic and trusted zone traffic
      • Removal of unreliable date fields

      Your Palo Alto traffic data will now include the complete lifecycle of network connections while still maintaining the efficiency benefits of the sampling rules."

License

MIT

FAQ

What is the Cribl Stream MCP server?
Cribl Stream 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 Cribl Stream?
This profile displays 73 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 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

4.773 reviews
  • Kiara Shah· Dec 28, 2024

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

  • Ganesh Mohane· Dec 16, 2024

    Cribl Stream is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Valentina Khanna· Dec 16, 2024

    Strong directory entry: Cribl Stream surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Kabir Diallo· Dec 16, 2024

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

  • Kiara Sharma· Dec 16, 2024

    I recommend Cribl Stream for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Diya Thomas· Dec 16, 2024

    Cribl Stream reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Diya Mensah· Dec 12, 2024

    I recommend Cribl Stream for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Ishan Abebe· Dec 12, 2024

    We evaluated Cribl Stream against two servers with overlapping tools; this profile had the clearer scope statement.

  • Michael Okafor· Dec 8, 2024

    According to our notes, Cribl Stream benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Zaid Tandon· Nov 19, 2024

    We evaluated Cribl Stream against two servers with overlapping tools; this profile had the clearer scope statement.

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