Equip your agents with the right telemetry

Give them clean, contexutal data to make faster, smarter decisions.

Stop debugging your AI

AI agents need clean, contextual data to operate effectively. Without it, they make costly mistakes, miss critical patterns, and require constant human intervention. Mezmo's Active Telemetry Platform and its Model Context Protocol (MCP) Server deliver the real-time context and intelligent interface agents need to act intelligently and autonomously.

Mezmo's MCP Server intelligently gathers relevant context and condenses for LLMs.

From reactive to proactive intelligence

Transform your agents into proactive decision-makers with agent-ready telemetry
Accelerate decisions

Structured, noise-free data helps agents identify patters and act faster

Enrich with context

Add business, user, and system data to improve agent reasoning and insights.

Expand utility

Telemetry pipelines format, enrich, and route data directly into your AI/ML systems.

Smarter data, better agents

Unlike competitors who simply stream raw observability data to LLMs, flooding them with noise and increasing costs, Mezmo's MCP Server acts as an intelligent interface layer between AI development tools and your telemetry data.
Active Engagement

Works directly in your development environment without context switching

Active Routing

Streamline pipelines by deduplicating and grouping logs before LLM processing to delivery relevant context

Active Analysis

Deduplication, correlation, and intelligent root cause analysis and detection

Key capabilities for AI-powered observability

Capabilities focus on handling a wide range of data, even when it's messy or inconsistent. They are also addressing any issues with data accuracy and its structure.
Root cause analysis

Automatically analyze log patterns and system behavior to identify incident causes.

Filter, sample & reduce

Remove low-value logs (DEBUG/INFO) to reduce noise and manage costs effectively. Learn more about supported processors.

Retain & route

Decide what stays hot, what moves to cold storage (S3, Azure Blob, GCP), or gets dropped. Learn more about supported destinations.

Data compliance

Redact, encrypt, or mask sensitive/PII data before reaching consumers.

Live tail + replay

Stream telemetry data in real-time and replay buffered events for instant incident investigation without waiting for indexing or storage delays.

Data profiling

Continuously analyze telemetry patterns to identify high-volume, low-value data streams and provide actionable recommendations for cost optimization. Learn how to use data profiling.

Responsive pipelines

Automatically adapt pipeline behavior based on real-time conditions, scaling processing capacity and adjusting sampling rates to maintain performance during traffic spikes.

Data enrichment

Enhance telemetry data with contextual metadata from external sources, standardize formats, and add business context to improve observability and enable better analysis.

Cardinality management

Monitor and control metric cardinality in real-time to prevent exponential cost increases from high-cardinality tags while preserving essential dimensional data.

Real-world use cases from real teams

> 80%
Faster processing and response times

"Mezmo’s MCP Server analyzed over 160,000 logs and immediately identified our payment service token validation failures affecting all gold-tier customers. What would have taken hours of manual investigation was resolved in minutes with natural language queries."

— DevOps Engineering Lead

> 95%
Cost reduction on AI analysis

"Mezmo's MCP Server analyzed 164,122 raw logs for $0.66, while competitor approaches cost $0.50+ for smaller datasets."

— Engineering Manager

> 99%
Root cause identification accuracy

"Successfully identified payment failures, cache leaks, and database connectivity issues accross mutliple services. "

— Site Reliability Engineer

Explore more

Browse resources to learn more about how it works
Learn
Agentic AI: MCP & its impact on observability automation
Blog
Empowering an MCP server with a telemetry pipeline
Guide
MCP implementation guide on GitHub
Blog
The debugging bottleneck: A manual log-sifting expedition

Stop drowning your agents in noise
Start delivering intelligence

Give your AI agents contextual, high fidelity telemetry and the intelligence to interact act naturally with Active Telemetry.