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Mcp Server Logs Sieve

mcp-oluwatunmise-olat-mcp-server-logs-sieve · by Oluwatunmise-olat

A Model Context Protocol (MCP) server that enables AI assistants to efficiently filter and analyze Cloud Platform logs by specific prefixes and patterns.

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Install

$ agentstack add mcp-oluwatunmise-olat-mcp-server-logs-sieve

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

mcp-server-logs-sieve

[](https://www.npmjs.com/package/mcp-server-logs-sieve) [](https://www.npmjs.com/package/mcp-server-logs-sieve) [](https://github.com/Oluwatunmise-olat/mcp-server-logs-sieve/actions/workflows/ci.yml) [](https://www.npmjs.com/package/mcp-server-logs-sieve) [](https://glama.ai/mcp/servers/Oluwatunmise-olat/mcp-server-logs-sieve) [](https://glama.ai/mcp/servers/Oluwatunmise-olat/mcp-server-logs-sieve)

mcp-server-logs-sieve is an MCP server that lets AI assistants query logs directly from your observability backend.

Ask debugging questions in plain language, and let Logs Sieve pull the exact logs and context for you.

Why this exists

During incidents, a lot of time goes into repetitive steps:

  • finding the right log source
  • narrowing the time window
  • spotting recurring failure patterns
  • following one trace across services

Logs Sieve packages those workflows into four tools: query_logs, summarize_logs, trace_request, and list_log_sources.

Supported providers

  • Google Cloud Logging (gcp)
  • AWS CloudWatch Logs (aws)
  • Azure Monitor Logs (azure)
  • Grafana Loki (loki)
  • Elasticsearch (elasticsearch)

Prerequisites

> ⚠️ Node.js 20 or above is required. > > This package uses ES modules and dependencies that need Node.js 20+. If you see ERR_MODULE_NOT_FOUND errors, check your Node version with node -v and upgrade if needed. > > This applies whether you run via npx, install globally, or use from source.

Quick MCP config

Example .mcp.json:

{
  "mcpServers": {
    "logs-sieve": {
      "command": "npx",
      "args": ["-y", "mcp-server-logs-sieve@latest", "--provider", "gcp"]
    }
  }
}

If you are running from this repo source directly, you can still use node ./bin/mcp-server-logs-sieve.js --provider gcp.

First-time setup: On first use, npx needs to download and install the package (~40s). Claude Code may time out waiting for this. Run the following once in your terminal to prime the cache before adding it to your MCP config:

npx mcp-server-logs-sieve@latest --provider gcp
# (Ctrl+C once it starts — you just need the install to complete)

After that, Claude Code will start the server instantly from the npx cache.

After updating config, restart your MCP client and confirm with /mcp.

Provider auth and env vars

See provider-specific setup and auth docs:

For Elasticsearch API compatibility headers:

export ELASTICSEARCH_COMPAT_VERSION=8

CLI usage

The package also includes a CLI for direct terminal usage:

mcp-server-logs-sieve query --provider gcp --scope my-project --last 1h --filter "payment failed"
mcp-server-logs-sieve summarize --provider gcp --scope my-project --last 24h
mcp-server-logs-sieve trace --provider gcp --scope my-project --trace 
mcp-server-logs-sieve sources --provider gcp --scope my-project

Documentation

Contributing

Contributions are welcome.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.