Log10x MCP Server

Observability memory for your logs, exposed to AI assistants. Ask Claude (or any MCP-compatible AI) "why did our log costs spike this week?", "triage these 3000 events", "what's causing the payments-svc error spike", or "pull all payment_retry events for acme-corp from Jan 15 through Apr 15" — and get structured answers backed by stable per-pattern identity, not best-effort clustering.

What it does

Log10x fingerprints every log line into a stable templateHash — a structural identity that stays constant across deploys, restarts, pod names, timestamps, and request IDs. That identity is the key to a per-pattern Prometheus time series (volume + cost) and, optionally, a Bloom-indexed S3 archive of the raw events. This MCP server exposes both surfaces to AI assistants as a set of tools:

Cost attribution and daily-habit tools

Tool	Answers	Tier
log10x_cost_drivers	"Why did our log costs spike?" — dollar-ranked patterns with before→after deltas, keyed by stable templateHash (Datadog Log Patterns re-cluster per query and can't do this honestly)	Reporter
log10x_event_lookup	"What is this single log line?" — cost breakdown + AI classification	Reporter
log10x_pattern_trend	"When did this pattern start spiking?" — time series + sparkline	Reporter
log10x_top_patterns	"What's expensive right now?" — loudest patterns by current cost	Reporter
log10x_list_by_label	"Cost by namespace / severity / tenant?" — group-by ranking	Reporter
log10x_services	"What services are we monitoring?" — volume + cost by service	Reporter
log10x_discover_labels	"What labels can I filter on?" — label universe for the session	Reporter
log10x_savings	"How much are we saving?" — per-app savings with annual projection	Reporter
log10x_dependency_check	"Anything depending on this before I drop it?" — SIEM dependency scan	None
log10x_exclusion_filter	"How do I drop this in Datadog?" — config snippets for 14 vendors	None

Investigation, triage, and archive tools (v1.3)

Tool	Answers	Tier
log10x_investigate	"Why is this spiking?" — single-call root-cause: anchor resolution, trajectory shape detection (acute-spike vs drift), cross-pattern lag correlation, causal chain with stat/lag/chain confidence sub-scores, drift cohort analysis, two-stage Retriever fallback, verification commands. Surfaces log-only signals (pool saturation, cache evictions, retry amplification) that APM structurally cannot see.	Reporter
log10x_resolve_batch	"Triage these events" — paste a file / array / text dump of raw log lines and get per-pattern frequency, severity, variable concentration, and next-action suggestions. Runs via the Log10x paste endpoint; works at any tier including CLI-only.	None
log10x_retriever_query	"Get me the actual events" — direct retrieval from the Retriever archive by templateHash with JS filter expressions over event payloads. Queries the customer's own S3 via pre-computed Bloom filters. Answers forensic, audit, and out-of-retention retrieval.	Retriever
log10x_backfill_metric	"Create a new Datadog metric backfilled with 90 days of history" — pulls historical events from the Retriever, aggregates into a bucketed time series, emits to the destination TSDB with historical timestamps preserved. Datadog + Prometheus remote_write supported today.	Retriever

All tools query prometheus.log10x.com (for Reporter-tier tools) over HTTPS, with the same X-10X-Auth header used by the rest of the Log10x stack. No log scanning; sub-second at any scale.

ROI examples — three real flows

These are real round-trips against the Log10x demo environment, captured during development. Every tool call below is verbatim what the model would produce; outputs are abbreviated for the README.

1. "Why is checkout-svc cost up?" (log10x_cost_drivers)

Prompt: "Why did checkout cost spike this week?"

Tool call: log10x_cost_drivers({ service: "cart", timeRange: "7d" })

Output (abbreviated):

cart — $137 → $38K/wk (4 cost drivers)

#1  cart cartstore ValkeyCartStore     $51 → $13K/wk   INFO  13.3B events
#2  shipping service Post shipping...  $34 → $12K/wk   CRIT  1.6B events
#3  GetCartAsync called with userId    $34 → $8.7K/wk        8.7B events
#4  AddItemAsync called with...        $18 → $4.6K/wk        4.2B events

4 drivers = 49% of increase · 2442 other patterns

**Next actions**:
  - call `log10x_investigate({ starting_point: 'cart_cartstore_ValkeyCartStore' })` to trace the cause of the $13K delta on this pattern.
  - call `log10x_dependency_check({ pattern: '...' })` before muting or dropping — blast-radius safety.

The next-action hints in the output literally tell the model what to do next. No prompt engineering required.

2. "What's broken in payments-svc?" (log10x_investigate)

Prompt: "Investigate kafka — there's an alert firing."

Tool call: log10x_investigate({ starting_point: "kafka", window: "1h" })

Output (abbreviated 8-link causal chain):

## Investigation: kafka, last 1h

**Anchor**: cluster_metadata_Wrote_producer_snapshot... (resolved from service_name)
**Service**: kafka
**Inflection**: 2026-04-14T00:19:52Z UTC
**Shape**: acute spike
**Reporter tier**: edge

### Most likely root cause

Pattern: cluster_metadata_dir_tmp_kafka_logs_Rolled_new_segment...
Confidence: 43% (stat:1.00 lag:0.43 chain:1.00)
Why: peaked 300s before the anchor, magnitude 1.4× baseline.

### Causal chain

1. cluster_metadata_dir_tmp_kafka_logs_Rolled... — peaked T-300s
2. Successfully_wrote_snapshot_org_apache_kafka...  — peaked T-300s
3. opentelemetry_javaagent_shaded_instrumentation... — peaked T-300s
... (8 links total, each with stat × lag × chain confidence sub-scores)

### Suggested verification commands

gh api /repos/<owner>/kafka/commits?since=...&until=...
kubectl get events -n kafka --since=Xm
dog metric query "avg:trace.kafka.requests{*} by {resource_name}" --from ...

The full causal chain comes back in one tool call. The model doesn't need to compose. The verification commands are pre-substituted with the inflection timestamp so the user can paste them directly.

3. "Triage this Slack paste" (log10x_resolve_batch)

Prompt: "My teammate dumped these 12 lines from order-processing-svc into Slack — what's happening?"

Tool call: log10x_resolve_batch({ source: "text", text: "..." })

Output (abbreviated):

## Batch Triage

12 events, resolved into 3 distinct patterns. Templater wall time: 6.4s. Execution: Log10x paste endpoint.

**Severity mix**: INFO: 7 · ERROR: 4 · WARN: 1

### Top 3 patterns by interestingness

**#1  checkout_svc_tenant_acme_corp_order_status_failed_reason_payment_gateway**  · 4 events (33% of batch) · interestingness 0.47
severity: ERROR

Variable concentration (top values within this batch):
  - timestamp · 4 distinct · `1776067923000` 25%, `1776067925000` 25%, `1776067928000` 25%
  - order · 4 distinct · `12347` 25%, `12349` 25%, `12352` 25%

**Next actions**:
  - call `log10x_investigate({ starting_point: '...' })` for historical correlation (requires Reporter tier).
  - call `log10x_retriever_query({ pattern: '...', filters: ["event.order === \"12347\""] })` to retrieve all historical events concentrated on order=12347 (requires Retriever tier).
  - native Datadog follow-up: `dog log search '@order:"12347"' --from now-24h` — filters to the dominant variable concentration directly in the SIEM.

Every pattern is ranked by an interestingness score (severity-weighted); the dominant variable is identified; ready-to-paste next-action commands are pre-constructed for both Log10x tools and the customer's SIEM. The model just needs to relay the output and ask which path the user wants to take.

Install

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "log10x": {
      "command": "npx",
      "args": ["-y", "log10x-mcp"],
      "env": {
        "LOG10X_API_KEY": "your-api-key"
      }
    }
  }
}

Restart Claude Desktop. The server autodiscovers every env your account can reach (default + any shared with you) — pass environment: "<nickname>" on any tool call to switch between them.

If LOG10X_API_KEY is omitted, the MCP boots in demo mode against the public Log10x demo env (read-only). Run log10x_signin and the LLM will walk you through the GitHub Device Flow — it mints a real key, writes it to ~/.log10x/credentials, and hot-reloads without an MCP-host restart.

Claude Code

claude mcp add --transport stdio \
  --env LOG10X_API_KEY=your-api-key \
  log10x -- npx -y log10x-mcp

Verify with /mcp.

Cursor / Windsurf / other MCP clients

Same pattern — add an mcpServers entry with "command": "npx", "args": ["-y", "log10x-mcp"], and LOG10X_API_KEY.

Help your AI find the tools

Even with the MCP installed, agents sometimes miss it for natural-language questions ("am I logged in?" → which log10x shell probe instead of log10x_login_status). Project rules nudge the agent toward MCP-first tool selection. Drop a snippet that says "the log10x MCP IS the surface; call its tools before shelling out" plus a few trigger-phrase mappings into:

Client	Rule file
Cursor	.cursor/rules/log10x.mdc (with alwaysApply: true frontmatter)
Claude Code	CLAUDE.md (workspace root)
Windsurf	.windsurfrules (workspace root)
GitHub Copilot Chat	.github/copilot-instructions.md
Claude Desktop	(no project-rule mechanism — relies on manifest descriptions)

Full canonical text and explanation in doc.log10x.com/install/mcp-server/#help-your-ai-find-the-tools.

Get your credentials

Two options:

1. Sign in via GitHub: launch the MCP without LOG10X_API_KEY set, then ask the LLM to "sign in to Log10x". log10x_signin runs the GitHub Device Flow, mints an API key, and saves it to ~/.log10x/credentials — works across every MCP host on the same machine.
2. Console: log into console.log10x.com → Profile → API Settings → copy your API Key. (Optional: set your Analyzer Cost ($/GB for your SIEM) on that page — the server reads it automatically.)

Multi-environment access

Two patterns, depending on what you need:

Same account, multiple envs (e.g., your account owns prod, staging, dev). Nothing special to configure — the MCP autodiscovers all envs your account can reach via GET /api/v1/user. Pass environment: "<nickname>" on any tool call, or just say "check staging costs" and the LLM routes there. The chosen env sticks for follow-up calls until you switch again.

Multiple accounts (e.g., a consultant accessing customer envs). The Log10x backend supports per-env permission sharing (OWNER / WRITE / READ). Have the env owner grant your account READ on their env from the console — it then shows up in your /api/v1/user response automatically, no client-side multi-credential setup needed. If you genuinely need parallel access from distinct API keys, register one MCP server per account in your host config with distinct server names.

Usage

You never call tools directly. Just ask your AI assistant a question in plain English:

You say	The AI calls
"Why did our log costs spike this week?"	log10x_cost_drivers
"What is this Payment Gateway Timeout pattern?"	log10x_event_lookup
"How much are we saving with the pipeline?"	log10x_savings
"When did the checkout service start spiking?"	log10x_pattern_trend
"What services are we monitoring?"	log10x_services
"How do I drop this in Datadog?"	log10x_exclusion_filter
"Anything depending on this before I drop it?"	log10x_dependency_check

Cost driver algorithm

When you ask about cost spikes, the server runs the same algorithm as the Log10x Slack bot:

1. Query current window — bytes per pattern for the selected timeframe
2. Query baseline — average of the 3 prior windows of the same size
3. Compute delta — cost_this_period - cost_baseline per pattern
4. Apply gates — a pattern is a cost driver when it passes both:
   • Dollar floor: delta exceeds $500/period
   • Contribution floor: delta is at least 5% of the total service increase
5. Sort by delta descending

Example output:

cart — $103 → $13K/wk (3 cost drivers)

#1  cart cartstore ValkeyCartStore      $51 → $6.4K/wk   INFO  6.6B events
#2  GetCartAsync called with userId     $34 → $4.3K/wk         4.2B events
#3  AddItemAsync called with userId     $18 → $2.2K/wk         2.1B events

3 drivers = 98% of increase · 11 other patterns

Environment variables

Reporter-tier (required for cost, trend, and investigate tools)

Variable	Required	Description
LOG10X_API_KEY	No	Your Log10x API key. Omit to boot in demo mode + use log10x_signin to mint one via GitHub. The env list is autodiscovered from GET /api/v1/user — no env-id pinning needed.
LOG10X_API_BASE	No	API base URL (default: https://prometheus.log10x.com)

Pasted-batch triage (log10x_resolve_batch)

Variable	Required	Description
LOG10X_PASTE_URL	No	Override the Log10x paste endpoint (default: https://meljpepqpd.execute-api.us-east-1.amazonaws.com/paste). Body limit 100 KB.

Retriever (log10x_retriever_query, log10x_backfill_metric)

Variable	Required	Description
__SAVE_LOG10X_RETRIEVER_URL__	Yes (Retriever tier)	Base URL of the customer's deployed Retriever query endpoint (e.g., https://retriever.<your-domain>). When unset, Retriever-dependent tools return a graceful "not configured" message.
LOG10X_RETRIEVER_AUTH_HEADER	No	Override the auth header name (default: X-10X-Auth, same as the Prometheus gateway).
LOG10X_RETRIEVER_AUTH_VALUE	No	Override the auth header value. Default is ${apiKey}/${envId} from the active environment.
__SAVE_LOG10X_RETRIEVER_TARGET__	No	Override the default target prefix under which retriever writes indexed objects (default: app).
LOG10X_RETRIEVER_INDEX_SUBPATH	No	Override the index subpath inside the bucket (default: indexing-results, matching the engine's indexContainer convention).
LOG10X_RETRIEVER_POLL_MS	No	Override the marker-stability poll interval (default: 1500 ms).
LOG10X_RETRIEVER_TIMEOUT_MS	No	Override the query timeout (default: 90000 ms).

Demo env retriever LB: the otel-demo cluster has a pre-provisioned retriever LoadBalancer at http://a2936089108bb492cb41d18cb5b75f8d-1298006809.us-east-1.elb.amazonaws.com. Set __SAVE_LOG10X_RETRIEVER_URL__ to that value for the demo. The demo bucket is tenx-demo-cloud-retriever-351939435334/indexing-results/.

Known engine-side issues (GAPS G12): log10x_retriever_query has two unresolved engine-side bugs that log10x_doctor flags as retriever_forensic_health warnings: (1) it may return 0 events on windows where log10x_pattern_trend proves events exist, (2) it may crash with MCP error -32000: Connection closed when passed a canonical slash-underscore pattern name. Workarounds: use short/free-text pattern names, cross-check any zero result against log10x_pattern_trend, prefer log10x_event_lookup + log10x_pattern_trend for incident reconstruction where approximate timing is acceptable. See docs/ENGINE_TICKETS.md for the full engine-team ticket.

Metric backfill destinations (log10x_backfill_metric)

Variable	Required	Description
DATADOG_API_KEY (or DD_API_KEY)	Yes (Datadog destination)	Datadog API key used to POST to /api/v2/series.
DATADOG_SITE	No	Datadog site (default: datadoghq.com, override for datadoghq.eu, us5.datadoghq.com, etc.)
PROMETHEUS_REMOTE_WRITE_URL	Yes (Prometheus destination)	URL of a Prometheus remote_write-compatible adapter. The MCP posts JSON; the adapter translates to the native protobuf/Snappy wire format.

The server fetches your analyzer cost ($/GB) from your Console profile at startup and refreshes it hourly. To change it, update the cost in your profile — the server picks up the new value within an hour.

Spawning sub-agents that use this MCP

If you script Claude Code sub-agents (Agent tool, OpenAI Agents SDK, custom orchestrators) to call log10x MCP tools, watch out for deferred-tool bootstrapping. The sub-agent's static tool list does not always include mcp__log10x__* — those tools are loaded on demand via ToolSearch (or the equivalent in your client). A sub-agent that reads its tool list and concludes "no MCP tools available" without first calling ToolSearch({query: "log10x"}) will refuse the task instead of using the tools.

In testing across 17 sub-agent runs, prompt framing was the deciding factor:

Framing	Bootstrap success
Action-oriented ("pull the events", "build the slide", "investigate this")	9/9
Honesty-oriented ("don't fabricate", "refuse if you can't verify")	0/5

The honesty disposition fires too early — the agent applies "be honest about my limits" before trying to discover deferred tools. The fix is one line at the top of every sub-agent prompt:

TOOL BOOTSTRAP: You have log10x MCP tools available, but they are
deferred-loaded. Before doing anything else, call
ToolSearch({query: "log10x", max_results: 20}) to load them.
After that you'll have mcp__log10x__log10x_* tools.

This raised bootstrap success from ~76% to 100% in our test runs and is harmless when MCP tools are already in scope.

Development

Build:

npm install
npm run build   # tsc → build/index.js

Operational gotcha — restart after rebuild. MCP clients (Claude Desktop, Claude Code, Cursor) typically launch the server as a long-running child process. Node caches loaded modules in memory, so the running process will not pick up your npm run build output until it is killed and respawned. After rebuilding, find and kill the stale processes:

pgrep -fl "log10x-mcp/build/index.js"   # show running servers + start times
ps -o pid=,lstart= -p $(pgrep -f log10x-mcp/build/index.js)
pkill -f "log10x-mcp/build/index.js"    # client will respawn on next tool call

If you ship a tool description change or a routing-hint update and the agent's behavior doesn't seem to reflect it, this is the most likely cause. Verify by grep-ing the raw tool output (most clients log it) for any string you added — if it's missing, the server is stale.

Security

• All API calls use your personal API key (never exposed in tool output)
• The server runs locally — no data leaves your machine except Prometheus queries
• Dependency check scripts run locally with your own SIEM credentials (read-only)
• No caching of log content — all data comes from pre-aggregated metrics

Documentation

Full documentation: doc.log10x.com/manage/mcp-server

License

This package is licensed under the Apache License 2.0.

Important: Log10x Product License Required

This package is the open-source MCP server for Log10x. While the MCP itself is open source, using Log10x requires a commercial license.

Component	License
This package (log10x-mcp)	Apache 2.0 (open source)
Log10x engine and runtime	Commercial license required

What this means:

• You can freely use, modify, and distribute this MCP server
• The Log10x backend services and tenx runtime that this MCP talks to require a paid subscription
• A valid Log10x API key is required to call the account-scoped tools (sign in via log10x_signin or set LOG10X_API_KEY)

Get Started:

• Log10x Pricing
• Documentation
• Contact Sales