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mshegolev/kibana-mcp

kibana_list_indices

Read-onlyIdempotent

Discover available Elasticsearch indices along with health, status, document count, and storage size. Use this to find index names before searching or aggregating logs.

Instructions

List available Elasticsearch indices.

Calls GET {ES_URL}/_cat/indices?format=json and returns a structured list of indices with health, status, document count, and storage size. Use this first to discover which index names / patterns exist before calling kibana_search_logs or kibana_aggregate_logs.

Examples: - Use when: "What log indices are available in Elasticsearch?" → default params, pattern='*'. - Use when: The user mentions a service name but not the index. Try pattern='logs-myservice-*' to narrow down. - Use when: "How many documents in the access-log index?" → pattern='access-log*', check docs_count. - Don't use when: You already know the index name — pass it directly to kibana_search_logs (saves one round trip). - Don't use when: You need to search log content — that's kibana_search_logs.

Returns: dict with keys indices_count / pattern / include_system / indices (list of {index, health, status, docs_count, store_size_bytes, size_human}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternNoIndex name or pattern to filter results (e.g. 'logs-*', 'filebeat-*'). Supports Elasticsearch wildcard syntax. Default '*' lists all non-system indices.*
include_systemNoWhether to include system/internal indices. Hidden by default: any prefix in {'.', 'kibana', 'ilm-history', 'shrink-'} — covers Kibana internals, ILM history, and shrunk index leftovers.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
indices_countYes
patternYes
include_systemYes
indicesYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant value beyond annotations by detailing the HTTP method (GET), endpoint, and the exact structure of the return value. Annotations already indicate read-only and idempotent, but the description enriches with concrete behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bullet points and examples, but it is somewhat lengthy and repeats some return format details. It is front-loaded with the main action and every sentence adds value, though could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description appropriately summarizes the return keys. It covers the tool's purpose, parameters, usage context, and behavioral details, making it fully complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with detailed parameter descriptions. The description adds value through examples and usage context, such as default pattern and how to narrow down, going beyond the schema. Baseline 3 increased to 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists available Elasticsearch indices, specifies the exact API call, and distinguishes it from sibling tools like kibana_search_logs and kibana_aggregate_logs. It uses specific verbs and resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use and when-not-to-use scenarios, including examples of when to use with specific patterns and when to skip and directly use kibana_search_logs. This provides clear guidance on tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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