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Kibana MCP. Manage your entire observability stack via natural language.

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Works with every AI agent you already use

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Just plug in your AI agents and start using Vinkius.

Kibana. Manage your entire observability stack directly from your AI agent. This server lets you list, create, and update spaces, dashboards, and index patterns across your Elastic environment.

You can copy objects between spaces, get full object metadata, or automate the lifecycle of your monitoring setup using natural language.

What your AI agents can do

Add case comment

Adds a comment to an existing support case.

Bulk create saved objects

Creates multiple saved objects (like dashboards) in a single batch operation.

Bulk get saved objects

Retrieves details for multiple saved objects at once.

+ 52 more capabilities included
Manage Kibana Spaces

Create, delete, update, or list entire Kibana spaces to segment dashboards and visualizations for different teams.

Control Saved Objects

Create, read, update, delete, or copy dashboards, index patterns, and visualizations across your instance.

Automate Alerting Rules

Create, find, enable, disable, or update alerting rules to manage your system monitoring alerts.

Manage Data Views

Get or update data views, and create runtime fields to process and structure raw log data.

Audit and Inspect Roles

List and retrieve details for Kibana roles, allowing you to audit permissions and access controls.

Run Connectors

Execute specific connector actions to interact with external data sources.

Supported MCP Clients

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients
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AI Agent

Kibana MCP Server: 55 Tools for Data Observability

These tools let you programmatically control every aspect of your Kibana instance, from creating spaces to managing complex alerting rules.

add019e5d2a

add case comment

Adds a comment to an existing support case.

bulk019e5d2a

bulk create saved objects

Creates multiple saved objects (like dashboards) in a single batch operation.

bulk019e5d2a

bulk get saved objects

Retrieves details for multiple saved objects at once.

bulk019e5d2a

bulk update saved objects

Updates several saved objects simultaneously.

copy019e5d2a

copy saved objects

Copies saved objects, such as dashboards, from one Kibana space to another.

create019e5d2a

create agent policy

Creates a new policy for Elastic Agents.

create019e5d2a

create case

Creates a new support case record.

create019e5d2a

create connector

Creates a new data connector for Kibana.

create019e5d2a

create data view

Creates a new data view or updates an existing one.

create019e5d2a

create enrollment key

Generates a new enrollment key for an Elastic Agent.

create019e5d2a

create or update role

Creates or updates a specific Kibana user role and its permissions.

create019e5d2a

create rule

Creates a new alerting rule based on defined criteria.

create019e5d2a

create runtime field

Adds or modifies a calculated field within a data view.

create019e5d2a

create saved object

Creates a specific saved object, like a dashboard or visualization.

create019e5d2a

create short url

Generates a short, shareable URL for a resource.

create019e5d2a

create space

Creates a new, isolated Kibana space for a specific team or project.

delete019e5d2a

delete cases

Deletes multiple support cases records.

delete019e5d2a

delete connector

Deletes a specified data connector.

delete019e5d2a

delete data view

Deletes an entire data view from the system.

delete019e5d2a

delete role

Removes a Kibana user role and all associated permissions.

delete019e5d2a

delete rule

Deletes an alerting rule that was previously created.

delete019e5d2a

delete saved object

Deletes a specific saved object like a dashboard or visualization.

delete019e5d2a

delete short url

Removes a generated short URL.

delete019e5d2a

delete space

Deletes an entire Kibana space.

disable019e5d2a

disable rule

Temporarily disables an alerting rule without deleting it.

enable019e5d2a

enable rule

Activates a previously disabled alerting rule.

execute019e5d2a

execute connector

Runs a configured data connector action to pull current data.

export019e5d2a

export saved objects

Exports defined sets of saved objects into a usable file format.

find019e5d2a

find rules

Searches and lists all existing alerting rules.

find019e5d2a

find saved objects

Searches the entire instance for saved objects matching specific criteria.

get019e5d2a

get agent

Retrieves detailed information about a specific Elastic Agent.

get019e5d2a

get case

Retrieves the full details of a specific support case.

get019e5d2a

get connector

Fetches the configuration details for a specified data connector.

get019e5d2a

get data view

Retrieves the full configuration and metadata for a data view.

get019e5d2a

get role

Fetches the details of a specific Kibana user role.

get019e5d2a

get rule

Retrieves all details for a specific alerting rule.

get019e5d2a

get saved object

Retrieves the full metadata for a specific saved object.

get019e5d2a

get short url

Retrieves the details and status of a short URL.

get019e5d2a

get space

Retrieves the full details of a specific Kibana space.

import019e5d2a

import saved objects

Imports a set of saved objects from a specified file upload.

list019e5d2a

list agent policies

Lists all configured policies for Elastic Agents.

list019e5d2a

list agents

Retrieves a list of all currently enrolled Elastic Agents.

list019e5d2a

list connectors

Lists every available data connector configured in the system.

list019e5d2a

list data views

Retrieves a list of all existing data views.

list019e5d2a

list enrollment keys

Lists all available enrollment keys for agents.

list019e5d2a

list roles

Lists all user roles defined in Kibana.

list019e5d2a

list spaces

Retrieves a list of all available Kibana spaces.

resolve019e5d2a

resolve import errors

Checks and resolves errors encountered during the bulk import of saved objects.

search019e5d2a

search cases

Searches the support case database for specific records.

unenroll019e5d2a

unenroll agent

Removes a specific Elastic Agent from the system.

update019e5d2a

update cases

Modifies the details of existing support cases.

update019e5d2a

update data view

Updates the configuration or settings of an existing data view.

update019e5d2a

update rule

Modifies the parameters or logic of an existing alerting rule.

update019e5d2a

update saved object

Updates the metadata and settings of a specific saved object.

update019e5d2a

update space

Modifies the settings of an existing Kibana space.

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
Start building

Make Your AI Do More

Start with Kibana, 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

Kibana MCP Server - Manage Dashboards & Observability

Your AI agent lets you control your entire observability stack right from the prompt. This server gives you the tools to manage everything in your Elastic environment—from spaces to dashboards and rules. You'll use your agent to list, create, update, and delete resources, letting you manage your data lifecycle using plain language.

Managing Kibana Spaces and Roles

You can segment your dashboards and visualizations by creating, deleting, or updating entire Kibana spaces using create_space, delete_space, and update_space. You can also manage user access by listing and getting details on roles with list_roles and get_role, or by creating or updating specific permissions with create_or_update_role and delete_role.

Handling Saved Objects

Need to manage dashboards or visualizations? You can create a new saved object, like a dashboard, with create_saved_object. You can also bulk-create or bulk-update multiple saved objects at once using bulk_create_saved_objects and bulk_update_saved_objects. To keep your environments consistent, copy saved objects from one Kibana space to another using copy_saved_objects. You can search for existing objects using find_saved_objects, or get the full details of any object with get_saved_object.

You can also delete specific objects with delete_saved_object, or export groups of objects into a file with export_saved_objects.

Alerting and Monitoring Rules

To keep tabs on your system, you can create a new alerting rule using create_rule, or update an existing one with update_rule. You can find all existing rules with find_rules, and you can temporarily disable a rule with disable_rule or activate it with enable_rule. If a rule is garbage, you can delete it entirely using delete_rule.

Data Views and Connectors

When it comes to data, you can create a new data view or modify an existing one using create_data_view or update_data_view. You can also enhance your data views by adding or changing a calculated field with create_runtime_field. You'll need to run external data sources using execute_connector after creating or fetching connector details with create_connector or get_connector.

You can list all available connectors with list_connectors and remove one with delete_connector.

Agents and Support

For your monitoring agents, you can list all enrolled agents with list_agents, or get details for a specific agent with get_agent. You can also generate new enrollment keys with create_enrollment_key and manage agent policies using list_agent_policies and create_agent_policy. If an agent is retired, you can remove it from the system with unenroll_agent.

For support issues, you can create a new case with create_case, search existing ones using search_cases, or get full details on a case with get_case. You can also update details on an existing case with update_cases or modify a case's comment with add_case_comment. Finally, you can delete multiple cases with delete_cases or modify a case's details with update_cases.

How Kibana MCP Works

  1. 1 Subscribe to the Kibana MCP Server and provide your Kibana URL and API Key.
  2. 2 Your AI agent calls a tool (e.g., create_space) using natural language.
  3. 3 The server executes the action against your Kibana instance and returns the resulting object or status.

The bottom line is: your AI agent performs complex Kibana actions without you having to navigate the UI.

Who Is Kibana MCP For?

This is for the DevOps Engineer who needs to move a dashboard from staging to production without manual clicks. It's for the Data Analyst who needs to find a specific index pattern across a massive environment. If you manage observability data or dashboards, you need this.

DevOps Engineer

Audits space configurations, moves dashboards between environments, and automates the provisioning of monitoring stack components.

Site Reliability Engineer (SRE)

Manages the lifecycle of monitoring rules and spaces, ensuring consistency between production and staging setups.

Data Analyst

Finds specific visualizations or index patterns using natural language search across a complex Kibana instance.

What Changes When You Connect

  • Move dashboards between environments instantly. Use copy_saved_objects to move an object from staging to production without manual steps.
  • Audit permissions and structure easily. List all roles with list_roles and get full details for a role using get_role to check who can see what.
  • Automate monitoring rules. Instead of manually clicking through rule editors, use create_rule or update_rule to set up alerts based on natural language input.
  • Maintain data consistency. Use create_data_view or update_data_view to standardize how raw logs are interpreted across multiple monitoring dashboards.
  • Find anything fast. If you don't know the exact ID, use find_saved_objects to search for dashboards or index patterns using plain text queries.
  • Control the data lifecycle. Use delete_space or delete_saved_object to clean up old, unused monitoring components and reduce clutter.

Real-World Use Cases

01

Staging-to-Production Dashboard Promotion

A DevOps engineer needs to promote a newly built dashboard. Instead of manually exporting and re-importing, they tell their agent: 'Copy the dashboard 'Global Network View' from the 'Staging' space to the 'Production' space.' The agent uses copy_saved_objects and confirms the promotion.

02

Standardizing Data Collection

A data analyst joins a new team and needs to see metrics from a legacy system. They ask their agent to 'Create a data view for the legacy logs and set the runtime field for latency.' The agent runs create_data_view and create_runtime_field, ensuring the new data is structured correctly immediately.

03

Mass Cleanup of Old Assets

An SRE is tasked with cleaning up old monitoring environments. They prompt the agent: 'List all spaces that haven't been used in 90 days, and then delete the saved objects inside them.' The agent runs list_spaces and then systematically uses delete_saved_object across the identified spaces.

04

Initial Setup and Permissions Check

A platform engineer needs to provision a new team workspace. They ask the agent to 'Create a new Kibana space called 'Project Phoenix' and grant the 'Developer' role read-only access.' The agent executes create_space and create_or_update_role.

The Tradeoffs

Searching for specific objects

Typing 'Find me the index pattern for logs' and hoping the AI understands the exact API call needed. This fails because the AI needs specific identifiers.

Use find_saved_objects. This tool lets you search the entire instance for saved objects using natural language queries, giving you the exact ID needed for subsequent commands like get_saved_object.

Manual configuration updates

Going into the UI to update a dashboard's time field or change a role's permission, wasting 15 minutes on clicks.

Use update_saved_object or update_role. Tell your agent exactly what needs changing. For example: 'Update the 'User Dashboard' saved object to use the time field '@timestamp'.'

Guessing object IDs

Trying to run delete_saved_object with an ID you remember but which is slightly wrong, resulting in a 404 error and broken workflow.

First, run get_saved_object to confirm the object's existence and metadata. If you need to search for it first, use find_saved_objects.

When It Fits, When It Doesn't

Use this server if your job involves managing the full lifecycle of monitoring data—creating, moving, modifying, or deleting spaces, dashboards, and rules. You need this if your workflow requires consistency across development, staging, and production environments. Don't use it if you only need to view data (that's a simple query). Don't use it if you only need to manage user authentication (use a dedicated identity management tool instead). This server is for structural changes to the observability stack itself.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kibana. 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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How we secure it →

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 55 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

add_case_comment bulk_create_saved_objects bulk_get_saved_objects bulk_update_saved_objects copy_saved_objects create_agent_policy create_case create_connector create_data_view create_enrollment_key create_or_update_role create_rule create_runtime_field create_saved_object create_short_url create_space delete_cases delete_connector delete_data_view delete_role delete_rule delete_saved_object delete_short_url delete_space disable_rule enable_rule execute_connector export_saved_objects find_rules find_saved_objects get_agent get_case get_connector get_data_view get_role get_rule get_saved_object get_short_url get_space import_saved_objects list_agent_policies list_agents list_connectors list_data_views list_enrollment_keys list_roles list_spaces resolve_import_errors search_cases unenroll_agent update_cases update_data_view update_rule update_saved_object update_space

Navigating a sprawling Kibana UI is a time sink.

Right now, moving a dashboard requires multiple clicks: you find the dashboard, you select 'Save As,' you choose a new space, you confirm the overwrite, and you repeat this process for every single visualization you want to promote. It's tedious, and you're always worried about missing a step or losing the correct version.

With this MCP server, you just tell your agent: 'Copy the 'API Latency Overview' dashboard from 'Staging' to 'Production'.' It uses `copy_saved_objects` and handles the entire transfer, giving you the final, correct dashboard in seconds.

Kibana MCP Server: Manage Data Views & Dashboards

Manual data preparation involves checking multiple tabs to see which fields are available, then manually defining the data type and transformation logic for every new metric. You have to confirm that the field is available before you can use it in a dashboard.

Now, you tell your agent to 'Create a data view for the logs and add a runtime field to calculate the request latency.' The agent runs `create_data_view` and `create_runtime_field`, setting up the field and the calculation in one go.

Common Questions About Kibana MCP

How do I use the Kibana MCP Server to copy a dashboard? +

You use the copy_saved_objects tool. Simply ask your agent to copy the object, specifying the source and destination spaces. You don't need to know the underlying API IDs; just give the names.

Can I use Kibana MCP Server to list all available spaces? +

Yes, run the list_spaces tool. It returns a full list of every space in your instance, allowing you to see exactly where your dashboards live.

What is the best tool for finding dashboards in Kibana MCP Server? +

Use find_saved_objects. This tool lets you search the entire instance for saved objects using plain text queries, making it much faster than navigating the UI.

How do I manage user permissions with the Kibana MCP Server? +

You use the create_or_update_role or list_roles tools. These let you create new roles or update existing ones, managing who can see what data.

How do I delete a space using the Kibana MCP Server? +

Use the delete_space tool. This permanently removes the entire space and all its contained objects. Be sure you know what you're deleting before running it.

How do I use the `get_saved_object` tool to check the metadata of a specific index pattern? +

The get_saved_object tool retrieves full metadata for any specific saved object. You just need to provide the object ID and type to see its configuration, last updated time, and field mappings.

What should I do if I run into errors when I try to `bulk_update_saved_objects`? +

If bulk updates fail, check the object IDs and ensure the data structure matches the target saved object type. The API response will detail which records failed and why.

Does the Kibana MCP Server support listing all Kibana roles using the `list_roles` tool? +

Yes, the list_roles tool lists every Kibana role in your instance. This lets your agent review all available roles to understand user permissions and access levels.

Can I search for a specific dashboard across my entire Kibana instance? +

Yes. Use the find_saved_objects tool and specify the type as 'dashboard'. You can also provide a search string to filter the results by name or description.

Is it possible to move dashboards from a development space to a production space? +

Absolutely. The copy_saved_objects tool allows you to select objects from a source space and replicate them into one or more target spaces, including their references.

Can I create new Kibana spaces using this integration? +

Yes, you can use the create_space action. You just need to provide the space configuration JSON, including the ID and name you want for the new environment.

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Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients

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