Axiom▌
by thetabird
Integrate with Axiom to run APL queries, analyze logs, detect anomalies, and make data-driven decisions easily.
Integrates with Axiom for executing APL queries and listing datasets, enabling log analysis, anomaly detection, and data-driven decision making.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / DevOps teams analyzing application logs
- / Data analysts exploring observability data
- / Teams performing real-time monitoring
- / Incident response and troubleshooting
capabilities
- / Execute APL queries on Axiom datasets
- / List available datasets
- / Perform log analysis and filtering
- / Detect anomalies in data
- / Query time-series data
- / Generate data-driven insights
what it does
Connects to Axiom to execute APL queries and manage datasets. Enables AI agents to perform log analysis and anomaly detection on your Axiom data.
about
Axiom is a community-built MCP server published by thetabird that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Axiom to run APL queries, analyze logs, detect anomalies, and make data-driven decisions easily. It is categorized under analytics data.
how to install
You can install Axiom 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
Axiom is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
MCP Server for Axiom
A JavaScript port of the official Axiom MCP server that enables AI agents to query data using Axiom Processing Language (APL).
<a href="https://glama.ai/mcp/servers/8hxxw8uenu"> <img width="380" height="200" src="https://glama.ai/mcp/servers/8hxxw8uenu/badge" /> </a>This implementation provides the same functionality as the original Go version but packaged as an npm module for easier integration with Node.js environments.
Installation & Usage
MCP Configuration
You can run this MCP server directly using npx. Add the following configuration to your MCP configuration file:
{
"axiom": {
"command": "npx",
"args": ["-y", "mcp-server-axiom"],
"env": {
"AXIOM_TOKEN": "<AXIOM_TOKEN_HERE>",
"AXIOM_URL": "https://api.axiom.co",
"AXIOM_ORG_ID": "<AXIOM_ORG_ID_HERE>"
}
}
}
Local Development & Testing
Installation
npm install -g mcp-server-axiom
Environment Variables
The server can be configured using environment variables:
AXIOM_TOKEN(required): Your Axiom API tokenAXIOM_ORG_ID(required): Your Axiom organization IDAXIOM_URL(optional): Custom Axiom API URL (defaults to https://api.axiom.co)AXIOM_QUERY_RATE(optional): Queries per second limit (default: 1)AXIOM_QUERY_BURST(optional): Query burst capacity (default: 1)AXIOM_DATASETS_RATE(optional): Dataset list operations per second (default: 1)AXIOM_DATASETS_BURST(optional): Dataset list burst capacity (default: 1)PORT(optional): Server port (default: 3000)
Running the Server Locally
- Using environment variables:
export AXIOM_TOKEN=your_token
mcp-server-axiom
- Using a config file:
mcp-server-axiom config.json
Example config.json:
{
"token": "your_token",
"url": "https://custom.axiom.co",
"orgId": "your_org_id",
"queryRate": 2,
"queryBurst": 5,
"datasetsRate": 1,
"datasetsBurst": 2
}
API Endpoints
GET /: Get server implementation infoGET /tools: List available toolsPOST /tools/:name/call: Call a specific tool- Available tools:
queryApl: Execute APL querieslistDatasets: List available datasets
- Available tools:
Example Tool Calls
- Query APL:
curl -X POST http://localhost:3000/tools/queryApl/call \
-H "Content-Type: application/json" \
-d '{
"arguments": {
"query": "['logs'] | where ['severity'] == "error" | limit 10"
}
}'
- List Datasets:
curl -X POST http://localhost:3000/tools/listDatasets/call \
-H "Content-Type: application/json" \
-d '{
"arguments": {}
}'
License
MIT
FAQ
- What is the Axiom MCP server?
- Axiom 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 Axiom?
- This profile displays 35 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 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.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 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.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.8★★★★★35 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
Useful MCP listing: Axiom is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Aarav Flores· Dec 20, 2024
We evaluated Axiom against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Hana Garcia· Dec 20, 2024
Axiom is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sakura Huang· Dec 8, 2024
Axiom has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Sofia Jackson· Nov 27, 2024
Strong directory entry: Axiom surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Ren Khan· Nov 23, 2024
According to our notes, Axiom benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Oshnikdeep· Nov 15, 2024
Axiom reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Soo Brown· Nov 11, 2024
Axiom is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Soo Sethi· Nov 11, 2024
I recommend Axiom for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Min Garcia· Oct 18, 2024
Axiom is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
showing 1-10 of 35