Axiom
MCP ServerFree** - Query and analyze your Axiom logs, traces, and all other event data in natural language
Capabilities7 decomposed
natural language apl query execution with mcp protocol translation
Medium confidenceTranslates natural language queries from AI agents into Axiom Processing Language (APL) queries and executes them against the Axiom data platform via REST API. The server implements MCP protocol handlers that receive query requests, convert them to APL syntax, submit them to Axiom's query API, and return structured results back through the MCP protocol. This enables AI agents like Claude Desktop to perform complex log and trace analysis without requiring users to learn APL syntax directly.
Implements MCP protocol as a protocol translator layer that bridges AI agents directly to Axiom's APL query engine, with built-in rate limiting per tool invocation rather than per-request, enabling safe multi-step query workflows from agents without explicit throttling logic in the agent itself.
Provides direct MCP integration to Axiom's native APL engine rather than requiring custom API wrappers, enabling AI agents to leverage Axiom's full query capabilities while maintaining protocol-level rate limiting and error handling.
dataset discovery and metadata retrieval via mcp tools
Medium confidenceExposes Axiom dataset metadata through MCP tool calls that retrieve available datasets, their schemas, field types, and retention policies without requiring direct API knowledge. The implementation calls Axiom's dataset management API endpoints and structures the response as tool output that AI agents can parse and use for query planning. This enables agents to understand what data is available before constructing queries.
Implements dataset discovery as a first-class MCP tool that returns structured schema information, enabling AI agents to perform schema-aware query planning without requiring separate documentation lookups or manual schema specification.
Provides schema discovery as a callable MCP tool rather than requiring agents to maintain hardcoded dataset knowledge, enabling dynamic adaptation to schema changes and multi-dataset environments.
saved query management and execution through mcp interface
Medium confidenceProvides MCP tools to list, retrieve, and execute pre-saved APL queries stored in Axiom without requiring agents to know query syntax. The implementation calls Axiom's saved query API to fetch query definitions and parameters, then executes them with agent-provided parameter values. This enables reuse of complex queries and standardized analysis patterns through a simple tool interface.
Exposes saved queries as MCP tools with parameter binding, allowing agents to execute complex pre-built queries through simple tool calls while maintaining query governance through Axiom's access control layer.
Enables query reuse and governance through Axiom's native saved query system rather than requiring agents to reconstruct queries, reducing query complexity and enabling non-technical users to leverage standardized analysis patterns.
monitor creation and alert rule configuration via mcp tools
Medium confidenceProvides MCP tools to create monitors and configure alert rules in Axiom that trigger based on APL query conditions. The implementation accepts monitor definitions (query, threshold, notification channels) through tool parameters, translates them to Axiom's monitor API format, and creates persistent monitoring rules. This enables AI agents to set up automated alerting without requiring manual Axiom UI interaction.
Implements monitor creation as an MCP tool that accepts APL query conditions and notification configuration, enabling agents to autonomously set up persistent monitoring rules without requiring manual Axiom UI interaction or external monitoring system integration.
Provides direct monitor creation through MCP rather than requiring agents to call separate monitoring APIs, enabling integrated alerting workflows where query analysis and monitor setup happen in the same agent conversation.
rate-limited mcp tool invocation with per-tool quota enforcement
Medium confidenceImplements rate limiting at the MCP tool level using a quota system that tracks API calls per tool and enforces limits to prevent Axiom API abuse. The implementation uses the ff library for configuration and maintains per-tool rate limit counters that are checked before each API call. If a tool exceeds its quota, the MCP server returns an error response without making the API call, protecting the Axiom backend from overload.
Implements rate limiting at the MCP tool level with per-tool quota enforcement, preventing individual tools from consuming all available API quota and enabling fine-grained control over which operations are rate-limited.
Provides tool-level rate limiting rather than global API throttling, enabling different rate limits for different operations (e.g., expensive queries vs. metadata lookups) and preventing a single tool from blocking others.
multi-source configuration management with precedence ordering
Medium confidenceImplements a three-tier configuration system using the ff library that reads settings from command-line flags (highest priority), environment variables (medium priority), and configuration files (lowest priority). The setupConfig() function in main.go parses all sources and merges them with proper precedence, enabling flexible deployment across different environments (local development, Docker, Kubernetes) without code changes. Configuration includes API token, server settings, and rate limit parameters.
Uses the ff library to implement three-tier configuration with explicit precedence ordering, enabling environment-specific overrides without requiring separate configuration files or code changes for different deployment targets.
Provides explicit precedence ordering (flags > env vars > files) rather than requiring manual precedence logic, making configuration behavior predictable and enabling standard DevOps patterns like environment variable overrides in containerized deployments.
mcp protocol server initialization and lifecycle management
Medium confidenceImplements the MCP server lifecycle using the mcp.NewServer() API, handling server initialization with metadata (name 'axiom-mcp', version), tool registration, and protocol message routing. The main.go entry point creates the server instance, registers all six MCP tools through the createTools() function, and manages the server's connection to AI agents. This provides the foundational protocol handling that enables all other capabilities.
Implements MCP server initialization with explicit tool registration through createTools(), providing a clean separation between protocol handling and tool implementation that enables modular tool addition.
Uses the standard mcp.NewServer() API rather than custom protocol implementation, ensuring compatibility with MCP-compliant agents and reducing maintenance burden for protocol updates.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓DevOps teams using Axiom for observability who want AI-assisted query generation
- ✓SREs investigating incidents through natural language conversation
- ✓Developers integrating Axiom data analysis into AI agent workflows
- ✓AI agents that need to dynamically discover available data sources
- ✓Multi-tenant Axiom deployments where dataset inventory changes frequently
- ✓Automated query generation systems that need schema awareness
- ✓Organizations with standardized observability queries they want to expose to AI agents
- ✓Teams that want to enforce query governance through pre-approved saved queries
Known Limitations
- ⚠APL query complexity is limited by what the AI agent can generate — no validation that generated APL is semantically correct before execution
- ⚠Rate limiting applies per API token — high-volume query patterns may hit throttling limits
- ⚠Query results are bounded by Axiom API response limits — very large result sets may be truncated
- ⚠No local query caching — every natural language query triggers a new API call to Axiom
- ⚠Metadata is retrieved at tool invocation time — no caching means repeated calls incur API latency
- ⚠Field-level schema details may be limited to basic types (string, number, timestamp) without semantic annotations
Requirements
Input / Output
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** - Query and analyze your Axiom logs, traces, and all other event data in natural language
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