Graylog MCP Server

cluster_log_messages

Clusters similar log messages into structural templates, revealing recurring patterns and reducing log noise.

Instructions

Cluster similar log messages into Drain3-style templates. Fetches messages with the same args as fetch_graylog_messages, then groups them by structural similarity. Templates are persisted per connection and reused across calls.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "cluster_log_messages"
  }
}

Input Schema

Name	Required	Description
`to`	No	Absolute end time (ISO)
`from`	No	Absolute start time (ISO)
`field`	No	Field to cluster on. Default 'message'.
`query`	No	Query string (same as fetch_graylog_messages)
`filters`	No	Field filters
`readOnly`	No	If true, do not update template library. Default false.
`algorithm`	No	Clustering algorithm. Default 'drain3'.
`streamIds`	No	Optional stream IDs
`timeRange`	No	Time range (e.g. '1h', '30m')
`exactMatch`	No	Wrap query in quotes (default true)
`sampleSize`	No	Max messages to fetch & cluster. Default 1000, max 10000.
`maxChildren`	No	Max templates per length bucket (LRU evict beyond this). Default 100.
`includeSamples`	No	Sample messages per cluster (first/middle/last by time). Default 3.
`minClusterSize`	No	Singletons collapsed under '_misc' cluster. Default 2.
`similarityThreshold`	No	Drain3 similarity threshold 0-1. Default 0.6. Lower = more aggressive merging.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.0Aug 10, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden of disclosing side effects. It states 'Templates are persisted per connection and reused across calls,' revealing statefulness. It does not mention performance cost or partial updates, but the core side-effect is disclosed.

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

Conciseness5/5

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

The description is two sentences, front-loaded with the purpose, and follows up with a useful behavior note. No unnecessary words or repetition.

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

Completeness2/5

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

This is a complex tool with 15 parameters and no output schema. The description omits the return format, performance implications, and relationship to sibling template-management tools (e.g., list_log_templates, delete_log_template). It only mentions persistence and reuse, which is insufficient for a tool of this complexity.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description references fetch_graylog_messages to explain shared query parameters but adds no unique syntax or behavior beyond what the schema already provides. No compensation is needed.

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 uses a specific verb 'Cluster' with a clear resource ('log messages') and specifies the output ('Drain3-style templates'). It also distinguishes itself from sibling fetch_graylog_messages by stating it fetches using the same args but groups them by structural similarity.

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

Usage Guidelines4/5

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

It explicitly references fetch_graylog_messages for the same query arguments, implying this tool is for clustering instead of raw retrieval. It also notes that templates are persisted and reused across calls, indicating when consistency matters. No explicit exclusions are given, but the context is sufficient for most decisions.

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