Axiom MCP. Analyze logs and metrics directly from chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Axiom. Connect your AI client to Axiom to manage logs and observability data. Ingest JSON, NDJSON, or CSV data and run complex Axiom Processing Language (APL) queries to analyze logs in real-time.
You can manage datasets, create monitors, and track system errors directly through natural conversation with your agent.
What your AI agents can do
Create annotation
Adds a specific note or marker to a dashboard for context.
Create dashboard
Builds a new visualization dashboard to track multiple metrics.
Create dataset
Creates a new container to hold and organize raw log or metric data.
Pass raw JSON, NDJSON, or CSV data to create a dataset, then run APL queries against it to find specific error counts or log patterns.
Create, read, update, and delete monitors to track system performance thresholds and generate alerts.
Create and modify datasets to structure large amounts of telemetry data, ensuring clean, reliable data sources for analysis.
Create and retrieve annotations on dashboards, allowing you to mark specific system events or important data points for future reference.
Create and update notifiers to ensure you get immediate, actionable alerts when system metrics deviate from normal ranges.
Retrieve user profiles, organization details, and API tokens to verify who has access and what permissions they hold.
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Supported MCP Clients
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Axiom MCP Server: 31 Tools for Observability
These tools let your agent manage the entire data lifecycle: from ingesting raw logs to running complex queries, setting up alerts, and visualizing trends.
019e3868create annotation
Adds a specific note or marker to a dashboard for context.
019e3868create dashboard
Builds a new visualization dashboard to track multiple metrics.
019e3868create dataset
Creates a new container to hold and organize raw log or metric data.
019e3868create monitor
Sets up a new automated check that alerts you if a metric crosses a set threshold.
019e3868create notifier
Configures a new channel to receive system alerts when something breaks.
019e3868delete annotation
Removes an existing annotation from a dashboard.
019e3868delete dashboard
Permanently deletes a visualization dashboard.
019e3868delete dataset
Wipes a dataset, removing all the logs and metrics it contained.
019e3868delete monitor
Turns off and removes an existing system performance monitor.
019e3868delete notifier
Deactivates and removes a configured system alert channel.
019e3868get annotation
Retrieves the details of a specific annotation using its unique ID.
019e3868get dashboard
Fetches the details and layout of a specific dashboard.
019e3868get dataset
Retrieves the metadata and contents of a specific dataset.
019e3868get monitor
Gets the current status and configuration of a specific monitor.
019e3868get notifier
Retrieves the details of a specific alert channel.
019e3868get org
Retrieves information about the organization associated with the account.
019e3868get user
Fetches the profile details for a specific user.
019e3868ingest data
Loads external JSON, NDJSON, or CSV data into a specified Axiom dataset.
019e3868list annotations
Lists all existing annotations across all your dashboards.
019e3868list dashboards
Shows a list of all visualization dashboards you've created.
019e3868list datasets
Lists all available datasets containing your logs and metrics.
019e3868list monitors
Shows a list of all active performance monitors.
019e3868list notifiers
Lists all configured system alert channels.
019e3868list tokens
Retrieves a list of all active API tokens for security review.
019e3868list users
Lists all user accounts associated with the organization.
019e3868run query
Executes a complex APL query against the stored Axiom data.
019e3868update annotation
Modifies the content or visibility of an existing annotation.
019e3868update dashboard
Changes the layout or widgets on an existing dashboard.
019e3868update dataset
Modifies the settings or scope of an existing dataset.
019e3868update monitor
Adjusts the threshold or schedule of an existing performance monitor.
019e3868update notifier
Changes the settings or destination of an existing alert channel.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Axiom, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Yo, you connect your AI client to Axiom to wrangle logs and observability data. You're gonna manage everything through natural talk with your agent. You can load external JSON, NDJSON, or CSV files into a new dataset using ingest_data and then run complex Axiom Processing Language (APL) queries against it with run_query to find specific error counts or log patterns.
Managing Datasets and Insights
You can organize your telemetry data by using create_dataset to make a new container, and you'll use get_dataset to check its metadata and contents. If you need to tweak the data scope or settings, just use update_dataset. You can list all existing datasets with list_datasets, and if you mess up, you can wipe a dataset clean using delete_dataset.
You'll also find update_annotation and get_annotation let you modify notes or markers on a dashboard, and create_annotation lets you drop a specific note using list_annotations to see what's already there. You'll use delete_annotation to take a marker off a dashboard when you're done.
Setting Up Alerts and Dashboards
Want to keep an eye on system performance? You can build a new visualization dashboard with create_dashboard, and you'll fetch the layout of an existing one using get_dashboard or see all of them with list_dashboards. If you need to change the widgets or layout, use update_dashboard; and if you never need it again, delete_dashboard takes it out.
To keep tabs on metrics, you can set up an automated check using create_monitor, and you'll get the current status and setup using get_monitor or see all active ones with list_monitors. If the threshold changes, you use update_monitor; and if you shut it down, delete_monitor takes care of it. You can also configure a new alert channel with create_notifier, and you'll pull the details of an alert using get_notifier or see all configured channels with list_notifiers.
If the destination changes, update_notifier adjusts it, and you can deactivate it with delete_notifier.
Auditing Access and Users
If you gotta check who's logged in or what permissions they got, you can pull the organization details with get_org or see the whole org info with get_org. To check user profiles, you use get_user or list all users with list_users. You can also get information about all API tokens with list_tokens for a security review.
You've got the tools to manage every aspect of your observability stack.
How Axiom MCP Works
- 1 1. Subscribe to the Axiom server and provide your Axiom API Token and Organization ID.
- 2 2. Your AI client sends a natural language request (e.g., 'Count errors in production logs from yesterday').
- 3 3. The server translates the request into the necessary tool calls (e.g.,
run_query) and returns the analysis results directly to your client.
The bottom line is: you analyze complex infrastructure data by talking to your agent, not by writing boilerplate API calls.
Who Is Axiom MCP For?
DevOps engineers who spend too much time clicking through monitoring dashboards to find a single root cause. SREs who need to check monitor statuses or run ad-hoc queries without leaving their terminal. Data analysts who need to ingest massive, messy log files and run complex queries like APL to make sense of them.
Checks monitor statuses, runs APL queries to find service degradation, and manages alerts without switching tools or tabs.
Ingests new log data, creates datasets for staging environments, and updates annotations on dashboards to document deployment changes.
Ingests large CSV or JSON datasets and runs complex APL queries to identify trends or outliers in historical usage data.
What Changes When You Connect
- See real-time data flow by running
run_query. You can execute complex APL queries on your data without leaving your chat window, getting error counts and trends instantly. - Build structured data sources with
create_datasetandupdate_dataset. Instead of managing raw files, you get versioned, clean datasets ready for deep analysis. - Track system health with
create_monitor. You define a threshold (e.g., 500ms latency), and the system automatically creates a monitor that alerts you when it breaks. - Manage all your alerts with
create_notifier. This tool ensures that whenrun_queryfinds an error, the alert goes to the right channel (Slack, PagerDuty, etc.). - Keep track of changes with
create_annotation. When a deployment causes a dip in performance, you can immediately create an annotation on the dashboard to link the incident to the code change. - Audit system access using
list_tokensandlist_users. You get a clear inventory of who has access and what credentials are active, making compliance checks fast.
Real-World Use Cases
Pinpointing the source of a spike in 5xx errors
The ops engineer notices high 5xx errors but doesn't know which microservice is failing. They ask their agent to 'Run an APL query to count 5xx errors in the last hour for all services.' The agent uses run_query against the relevant dataset, filtering by error code and time, and immediately identifies the failing service and the associated trace IDs. Problem solved in minutes, not hours of log diving.
Documenting a critical system change
A software engineer pushes a new feature that slightly degrades performance. Before merging, they ask their agent to 'Create an annotation on the dashboard for the performance change on 2024-05-18.' The agent uses create_annotation, permanently linking the incident to the dashboard, ensuring future teams know exactly when and why the performance dipped.
Setting up proactive failure detection
The SRE needs to know if the API response time exceeds 500ms regularly. They instruct their agent to 'Create a monitor named Latency Alert that checks for response times over 500ms every 5 minutes.' The agent uses create_monitor, and the system automatically manages the check and triggers alerts via create_notifier if the threshold is crossed.
Preparing data for a quarterly compliance audit
The data analyst receives a massive dump of raw access logs (JSON). Instead of dumping it into a separate system, they ask their agent to 'Ingest this JSON data into a new dataset for audit.' The agent uses ingest_data and then uses list_datasets to confirm the data is ready for querying using run_query for compliance reporting.
The Tradeoffs
Manual log correlation
The junior engineer manually opens the log dashboard, scrolls through hundreds of entries, and tries to correlate a user ID with a specific transaction ID across multiple tabs. This takes 30 minutes of wasted time.
→
Tell your agent: 'Run a query to correlate user ID 123 with transactions that failed yesterday.' The agent uses run_query to execute the complex APL query, delivering the correlated results instantly.
Ignoring data lineage
A team member makes a fix, but forgets to document the change. The next engineer sees the dashboard, but has no idea why the metric is behaving strangely, leading to confusion and wasted debugging time.
→
After making a change, use create_annotation on the dashboard and include a summary of the change and the reason. This builds institutional knowledge right where the data lives.
Using raw file dumps for analysis
The analyst gets a massive CSV dump from a vendor and just uploads it to a local tool, making it hard to query or share. The data is isolated and unindexed.
→
Use ingest_data to load the raw CSV into a structured dataset. Then, use run_query to apply Axiom's powerful language against it, making the data instantly queryable by everyone.
When It Fits, When It Doesn't
Use this if you need to treat observability data as a structured, queryable resource. You need to do more than just view logs; you need to analyze them—running complex queries, setting up automated checks, or versioning the underlying datasets. This server is for the full data lifecycle: from raw ingest_data to run_query to create_monitor. Don't use it if you just need to list available dashboards (list_dashboards) or retrieve basic user metadata (get_user). For simple listing tasks, the dedicated list_ tools are fine, but if the goal involves computation, monitoring, or structural change, this is the right place.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Axiom. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 31 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging production logs shouldn't feel like sifting through a dumpster.
Before this, debugging meant jumping between the log viewing tool, the metrics dashboard, and the ticketing system. You’d copy an error ID, switch tabs, search, and paste. If the logs were too big, you were stuck reading plain text dumps, trying to find the needle in the haystack manually.
Axiom MCP Server: Manage Data Lifecycle Tools
You no longer have to manually create a monitor, wait for it to fail, and then manually create an alert. You tell your agent to 'Set up a monitor for high latency,' and it runs `create_monitor`, then `create_notifier`, tying the whole process together. The system manages the complexity for you.
Common Questions About Axiom MCP
How do I run a complex query using the run_query tool? +
You simply tell your agent what you want to find. The agent handles the APL syntax and calls run_query for you, returning the filtered data directly. You don't need to write the APL code yourself.
Does the ingest_data tool handle all file types? +
It handles JSON, NDJSON, and CSV formats. You just need to pass the data to the agent, and it executes the ingest_data tool to make the data available for querying.
What is the difference between create_dataset and list_datasets? +
list_datasets shows you what datasets already exist. create_dataset builds a new, empty container—the dataset—where you can then use ingest_data to put logs into it.
Can I view the status of a monitor using get_monitor? +
Yes, get_monitor retrieves the current status of a specific monitor. You can check if it's active, what its threshold is, and when it last ran.
How do I delete old data using the delete_dataset tool? +
You use delete_dataset to wipe the dataset clean. Be careful with this; it permanently removes all logs and metrics contained within that dataset ID.
Can I update an existing dashboard using update_dashboard? +
Yes, update_dashboard lets you change the layout or widgets of a dashboard without rebuilding it from scratch. You just tell the agent what needs changing.
What is the purpose of the get_user tool, and how do I check user details? +
The get_user tool retrieves specific user records by ID. You use it when your agent needs to verify a user's permissions or pull account details for auditing purposes.
How do I manage API tokens using the list_tokens tool? +
The list_tokens tool shows all existing API tokens associated with your account. This helps you audit which tokens are active or revoke access for old credentials.
Can I run complex log analysis using Axiom Processing Language (APL)? +
Yes! Use the run_query tool to execute any APL string. You can specify start_time and end_time to filter your data and get precise analytical results directly in the chat.
How do I send new log data to my Axiom datasets? +
You can use the ingest_data tool. Simply provide the dataset_name, the data payload, and the content_type (JSON, NDJSON, or CSV) to stream data into your Axiom account.
Is it possible to manage system monitors and alerts through this integration? +
Absolutely. You have access to a full suite of tools including list_monitors, create_monitor, and update_monitor to configure threshold or anomaly detection alerts based on your APL queries.
Multi-server workflows that include Axiom MCP
MCP Recipe for Code Review Time Analytics
Review bottlenecks detected, unreviewed PRs surfaced, reviewer workload balanced, team velocity measured , fix your code review process with data
MCP Recipe for Instant Incident War Rooms
PagerDuty wakes you up at 2am with 'high error rate' but the Axiom dashboard shows 47 different error types , your agent already ran the query, found the root cause, and posted the diagnosis before you opened your laptop
Track Database Performance Issues Using MCP
Query performance profiled, slow queries caught, branch costs tracked, optimization reports generated , DBA-level visibility without a DBA
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