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Graylog MCP Server

by jperelliversion 1.3.0i★ –iRuns on your computer
Registry nameio.github.jperelli/graylog-mcp

Install

Overview

Description from the maker (original English)

Query logs from Graylog: get streams, search messages, aggregate, and get full data.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: GRAYLOG_BASE_URL_INSTANCE_1, GRAYLOG_API_TOKEN_INSTANCE_1

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: GRAYLOG_BASE_URL_INSTANCE_1, GRAYLOG_API_TOKEN_INSTANCE_1, GRAYLOG_LABEL_INSTANCE_1

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "graylog": {
      "command": "npx",
      "args": [
        "-y",
        "@jperelli/graylog-mcp"
      ],
      "env": {
        "GRAYLOG_BASE_URL_INSTANCE_1": "your-value",
        "GRAYLOG_API_TOKEN_INSTANCE_1": "your-value",
        "GRAYLOG_LABEL_INSTANCE_1": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add graylog --env GRAYLOG_BASE_URL_INSTANCE_1=your-value --env GRAYLOG_API_TOKEN_INSTANCE_1=your-value --env GRAYLOG_LABEL_INSTANCE_1=your-value -- npx -y @jperelli/graylog-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "graylog": {
      "command": "npx",
      "args": [
        "-y",
        "@jperelli/graylog-mcp"
      ],
      "env": {
        "GRAYLOG_BASE_URL_INSTANCE_1": "your-value",
        "GRAYLOG_API_TOKEN_INSTANCE_1": "your-value",
        "GRAYLOG_LABEL_INSTANCE_1": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "graylog": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@jperelli/graylog-mcp"
      ],
      "env": {
        "GRAYLOG_BASE_URL_INSTANCE_1": "your-value",
        "GRAYLOG_API_TOKEN_INSTANCE_1": "your-value",
        "GRAYLOG_LABEL_INSTANCE_1": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • GRAYLOG_BASE_URL_INSTANCE_1 required env
    Base URL of the first Graylog instance, e.g. http://graylog.example.com:9000
  • GRAYLOG_API_TOKEN_INSTANCE_1 secret required env
    API token for the first Graylog instance (used as the HTTP Basic username).
  • GRAYLOG_LABEL_INSTANCE_1 env
    Optional human-readable label for the first instance (default: instance_1).

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account jperelli, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7258 servers in this category
  • Published to the registry more recently than 23% of the other 7258 servers in this category
  • Basic checks equal to or better than 100% of the other 7258 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Graylog MCP Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: GRAYLOG_BASE_URL_INSTANCE_1, GRAYLOG_API_TOKEN_INSTANCE_1

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.3.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Graylog MCP Server

A minimal MCP (Model Context Protocol) server in JavaScript that integrates with Graylog.

Features

  • JavaScript MCP server
  • Tools: search (read matching log lines across multiple streams), analyze (aggregate matches by a field, with an optional time histogram), list_fields (which fields exist), list_streams (discover readable streams), and get_message (fetch one full document)
  • Empty results explain themselves. A zero-match search reports whether the query is wrong, the window is quiet, or Graylog simply hasn't *indexed* the logs yet — three causes that otherwise look identical and send an agent in circles
  • Discovery over guessing. list_fields and analyze's valueContains let an agent look up real field names and values instead of inventing them, since a wrong guess returns 0 hits and reads as "no logs exist"
  • Token-efficient by design, search returns a concise projection of high-signal fields by default; opt into full documents with verbose
  • A server-level "instructions" manual teaches the client the query syntax, stream-scoping rules, and severity quirks up front
  • Multi-instance support, query multiple Graylog servers from a single MCP server

Requirements

  • Node.js 18+

Configuration

Configure one or more Graylog instances using numbered env vars:

VariableRequiredDescription
GRAYLOG_BASE_URL_INSTANCE_NyesGraylog base URL for instance N
GRAYLOG_API_TOKEN_INSTANCE_NyesAPI token for instance N
GRAYLOG_LABEL_INSTANCE_NnoHuman-readable label (default: instance_N)

Replace N with 1, 2, 3, … to register as many instances as needed. Only instances with both BASE_URL and API_TOKEN set will be active.

Use with an MCP client

No installation needed, npx downloads and runs the server automatically.

Claude Code
claude mcp add graylog-mcp \

-e GRAYLOG_BASE_URL_INSTANCE_1=http://your-graylog-production.example.com:9000 \ -e GRAYLOG_API_TOKEN_INSTANCE_1=your_production_token \ -e GRAYLOG_LABEL_INSTANCE_1=production \ -e GRAYLOG_BASE_URL_INSTANCE_2=http://your-graylog-staging.example.com:9000 \ -e GRAYLOG_API_TOKEN_INSTANCE_2=your_staging_token \ -e GRAYLOG_LABEL_INSTANCE_2=staging \ -- npx -y @jperelli/graylog-mcp@latest

Or add it manually to ~/.claude.json:

{

"mcpServers": { "graylog-mcp": { "command": "npx", "args": ["-y", "@jperelli/graylog-mcp@latest"], "env": { "GRAYLOG_BASE_URL_INSTANCE_1": "http://your-graylog-production.example.com:9000", "GRAYLOG_API_TOKEN_INSTANCE_1": "your_production_token", "GRAYLOG_LABEL_INSTANCE_1": "production",

"GRAYLOG_BASE_URL_INSTANCE_2": "http://your-graylog-staging.example.com:9000", "GRAYLOG_API_TOKEN_INSTANCE_2": "your_staging_token", "GRAYLOG_LABEL_INSTANCE_2": "staging" } } } }

Cursor

Add to ~/.cursor/mcp.json:

{

"mcpServers": { "graylog-mcp": { "command": "npx", "args": ["-y", "@jperelli/graylog-mcp@latest"], "env": { "GRAYLOG_BASE_URL_INSTANCE_1": "http://your-graylog-production.example.com:9000", "GRAYLOG_API_TOKEN_INSTANCE_1": "your_production_token", "GRAYLOG_LABEL_INSTANCE_1": "production",

"GRAYLOG_BASE_URL_INSTANCE_2": "http://your-graylog-staging.example.com:9000", "GRAYLOG_API_TOKEN_INSTANCE_2": "your_staging_token", "GRAYLOG_LABEL_INSTANCE_2": "staging" } } } }

Claude Desktop

Config file locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Linux: ~/.config/claude-desktop/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Use the same JSON structure shown above for Cursor.


Use

Once configured, the tools become available and are called automatically when needed. The usual flow is to search the Default Stream (000000000000000000000001), which covers everything the token can read, then analyze to spot patterns, search to read individual lines, and get_message to inspect one hit in full. Reach for list_streams only to scope to a specific named stream. Example prompts:

Search Graylog for errors in the payments namespace in the last 15 minutes.

Query the "staging" instance.

Which containers produced the most errors in the last hour?

Which namespaces have "payments" in the name?

Don't let the agent guess field names or values. A query on a field or value that doesn't exist matches nothing, which looks exactly like "there are no logs". list_fields answers which fields exist, and analyze with valueContains answers which values a field actually has.

Available tools

list_streams

List the streams the configured API token can read (id + title). Only readable ones are returned.

You usually don't need this: pass the Default Stream id 000000000000000000000001 to search/analyze to cover everything the token can read. A cluster can hold thousands of streams (the author's has 1,205), so output is capped — use titleContains when you want one specific named stream.

Parameters:

  • instance (string, optional): Label of the Graylog instance to query. Defaults to the first configured instance.
  • titleContains (string, optional): Case-insensitive substring filter on the stream title (e.g. catalogue).
  • limit (number, optional): Max streams to return. Default: 50. The Default Stream is always included and never counts against the cap.
list_fields

List the message fields that actually exist in the index. Use it before searching on a field you haven't seen in a result, so you never guess a field name.

A cluster indexes thousands of fields (the author's: 3,269), and near-duplicates are common — namespace_name, Pod_namespace, pod_namespace and Namespace may all exist while only one is populated by your shipper. Pass contains to narrow.

Parameters:

  • contains (string, optional): Case-insensitive substring filter on the field name, e.g. namespace, pod, level.
  • limit (number, optional): Max field names to return. Default: 100.
  • instance (string, optional): Instance label. Defaults to the first configured instance.
search

Read matching log lines across one or more streams, merged newest-first. By default it returns a concise projection of high-signal fields (timestamp, source, level, container_name, pod_name, namespace_name, application_name, service, logger_name, name, msg, err, stack, message) plus each hit's _id/_index, with the raw message body truncated to 500 chars, this keeps the agent's context small. Set verbose: true (or pass explicit fields) to get every populated field, untruncated. Stream IDs are required, an all-streams search is not performed implicitly, because a limited-permission token would be rejected with 403 Not authorized.

name/msg/err/stack are there because a shipper that parses a JSON log line (pino, bunyan, structlog) extracts its keys into real fields. Those fields *are* the summary of the event, and they're cheap — the raw message body that contains them is neither, which is why it's truncated hard by default.

Reach for `analyze` before `search`. Raw lines are the most expensive thing this server returns. A hundred repetitions of one error cost a hundred times as much via search as one aggregated row via analyze, and tell you less. Use search once you know which line you want.

Tip: to search everything the token can see (including messages not routed to a named stream), pass the Default Stream id 000000000000000000000001. list_streams also surfaces it.

Parameters:

  • query (string, required): Search query, using Graylog/Elasticsearch syntax. Examples: msg:Error, namespace_name:app-payments-qa AND error, source:api-*, *.
  • streams (string, required): Comma-separated stream IDs to search. Get them from list_streams.
  • instance (string, optional): Label of the Graylog instance to query. Defaults to the first configured instance.
  • searchTimeRangeInSeconds (number, optional): Relative time range in seconds. Default: 900 (15 minutes).
  • from / to (string, optional): Absolute window in ISO-8601 UTC (e.g. 2026-07-11 14:00:00). When both are set they override the relative range, use them to investigate a known incident window.
  • searchCountLimit (number, optional): Max number of messages. Default: 50.
  • messageChars (number, optional): Max characters of the raw message body per hit. Default: 500. Raise it only when the detail you need lives in the raw body rather than the parsed fields.
  • verbose (boolean, optional): Return every populated field, untruncated, instead of the concise projection. Default: false.
  • fields (string, optional): Comma-separated explicit field list to return. Overrides the concise projection.

The response is { returned, total_matched, streams, messages, note?, projection?, why_no_results? }, where total_matched is the total number of hits across the streams (may exceed returned, which is capped by searchCountLimit); when it does, note explains how to see more.

When a search matches nothing, it tells you why. A bare total_matched: 0 is ambiguous, and the three causes need opposite responses, so why_no_results names the one that applies:

  • *The window has messages, but none match.* The streams and time range are fine, so the query is wrong — usually a guessed field name or value. Confirm with list_fields / analyze.
  • *The window is empty, and indexing is current.* These streams are genuinely quiet.
  • *The window is empty, and the newest indexed message is hours old.* Graylog is still indexing. The logs exist and have been received; they just aren't searchable yet. The response reports how far behind indexing is and the journal backlog. Don't conclude the logs are missing — widen the range or retry.

That last case is easy to misread as "this service produced no logs", and it's the reason this project exists in its current shape: the pipeline can lag ingestion by hours under load.

Note on log levels — severity must be discovered, not guessed. Some services emit a top-level Graylog level (syslog: 3=error, 4=warn). Others log JSON, and the shipper extracts its keys into their *own* fields: pino's {"level":50,"msg":"Error","name":"SvcX"} typically becomes fields msg and name, while its numeric level is lost — it collides with the container's own level (often 7), so level:50 and level:ERROR both match nothing even though the errors are plainly there. Guessing a disjunction like level:ERROR OR level:50 OR error OR exception OR fatal is how agents waste turns. Instead run list_fields (contains: "level", "msg", "err"), then analyze on msg to see the actual values. Free-text error works as a fallback; don't assume exception or fatal exist. And avoid "level":50 as a query, a quoted string before : is invalid Lucene.

analyze

Aggregate matching messages by the top values of a field instead of returning raw lines, e.g. which source, container_name, or level dominates the errors in a window. Optionally add a time histogram of total

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