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Es Error Lens

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Enables LLM agents to search and analyze Elasticsearch logs for errors, detect recurring patterns, analyze error-rate trends, and retrieve full trace context th

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Enables LLM agents to search and analyze Elasticsearch logs for errors, detect recurring patterns, analyze error-rate trends, and retrieve full trace context through MCP tools.

README

CI License: MIT Python 3.10+ Offline tests

An MCP server that gives LLM agents eyes on your Elasticsearch logs. Point it at a cluster holding ECS-format logs and any MCP client (Claude Desktop, Claude Code, or your own agent) can search errors, detect recurring patterns, analyze error-rate trends, and pull full trace context — the queries an engineer runs by hand during triage, exposed as tools.

Tool What it answers
search_errors "What's failing right now?" — filtered by window, service, level
get_error_patterns "Is this systemic or a one-off?" — message aggregation with occurrence counts
analyze_error_trend "Is it getting worse?" — time-series histogram, peak detection, trend verdict
get_error_context "What led up to this?" — every log sharing the error's trace ID, in order
compare_errors "Are these two failures related?" — attribute + fuzzy-message comparison
health_check "Can I even reach the cluster?"

Elasticsearch is spoken to over its plain REST API via httpx — no Elasticsearch client dependency, and the HTTP layer is transport-injectable, so the entire server runs offline against the bundled FakeElasticsearch for tests and the demo.

Demo (offline, no cluster, no API keys)

git clone https://github.com/TerraCo89/es-error-lens && cd es-error-lens
python -m venv .venv && source .venv/bin/activate   # .venv\Scripts\activate on Windows
pip install -e ".[dev]"

pytest              # 22 tests, all offline, < 1 second
es-error-lens-demo  # synthetic incident walk-through

The demo mounts FakeElasticsearch with a deterministic 60-document ECS corpus simulating a small incident — a steady payment-provider failure, an escalating search-timeout problem, and background noise — then triages it:

[get_error_patterns] 2 recurring patterns in 57 errors:
   42x  [TimeoutError] Search query timed out after 30s  (search-api)
   13x  [UpstreamHTTPError] Payment provider returned 502 Bad Gateway  (checkout-api)
  error types: TimeoutError=42, UpstreamHTTPError=13, TemplateError=1, ConnectionResetError=1

[analyze_error_trend] search-api: 42 errors, trend=INCREASING
  peak: 8 errors at 2026-07-10T06:00:00Z

[get_error_context] trace-checkout-7f3a -> [UpstreamHTTPError] Payment provider returned 502 Bad Gateway
  3 related logs on the same trace:
    info  POST /checkout started
    info  Cart validated: 3 items
    warn  Payment provider latency 4100ms exceeds SLO

Demo result: OK

Using it against a real cluster

export ELASTICSEARCH_URL=http://localhost:9200   # default
export ES_INDEX_PATTERN=logs-*                   # default

es-error-lens                                    # stdio, for Claude Desktop / Claude Code
es-error-lens --transport streamable-http --port 8080   # HTTP clients

Claude Desktop / Claude Code config:

{
  "mcpServers": {
    "es-error-lens": {
      "command": "es-error-lens",
      "env": { "ELASTICSEARCH_URL": "http://localhost:9200" }
    }
  }
}

Expected document shape — standard ECS fields, the ones Filebeat and the ECS logging libraries emit by default: @timestamp, log.level, message, service.name, error.type, error.stack_trace, trace.id, host.name. Anything missing degrades gracefully (fields come back null).

Testing without a cluster

FakeElasticsearch (also exported) implements just enough of the _search API for this server: bool queries with term/range clauses, sorting, terms aggregations with top_hits sub-aggregations, and date_histogram with fixed intervals. Mount it in your own tests:

from es_error_lens import FakeElasticsearch, set_transport, search_errors

set_transport(FakeElasticsearch(my_ecs_docs).transport())
result = await search_errors(time_range="1h", service="checkout-api")

The test suite runs entirely through it — deterministic, offline, fast — and a hygiene test enforces that all bundled demo data stays synthetic (hosts under .example, no real domains or addresses).

Provenance

Extracted from a personal observability platform where it fronts the Elasticsearch instance that aggregates structured logs from a fleet of side-project services, letting coding agents triage production errors during development sessions. All demo and test data in this repository is synthetic.

License

MIT © 2026 Kris Cernjavic

from github.com/TerraCo89/es-error-lens

Install Es Error Lens in Claude Desktop, Claude Code & Cursor

Recommended · one command, every IDE
unyly install es-error-lens

Installs into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.

First time? Get the CLI: curl -fsSL https://unyly.org/install | sh

Or configure manually

Run in your terminal:

claude mcp add es-error-lens -- uvx --from git+https://github.com/TerraCo89/es-error-lens es-error-lens

FAQ

Is Es Error Lens MCP free?

Yes, Es Error Lens MCP is free — one-click install via Unyly at no cost.

Does Es Error Lens need an API key?

No, Es Error Lens runs without API keys or environment variables.

Is Es Error Lens hosted or self-hosted?

A hosted option is available: Unyly runs the server in the cloud, no local setup required.

How do I install Es Error Lens in Claude Desktop, Claude Code or Cursor?

Open Es Error Lens on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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