Spoor

Name: Spoor
Author: asinha07

von asinha07Version 0.3.1i★ –iLäuft auf deinem Computer

Name in der Registryio.github.asinha07/spoor

Installieren

Überblick

Beschreibung vom Entwickler (englisches Original)

What changed across metrics, logs and traces, and where it started (Grafana, Victoria, New Relic).

Die drei Säulen von PopNx MCP

Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.

Stimmen der NutzeriNoch keine BewertungenBewertungen starten in einem späteren Schritt zusammen mit Benutzerkonten
Unsere Basischecksi1 von 1 bestandenAnhand öffentlicher Informationen geprüft; Code noch nicht gescannt
NutzungiNoch nicht gemessen★ –

Was du damit machen kannst

Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.

Verbindung: Läuft auf deinem Computer
Transport: stdio
Kategorie: Sonstiges
Das musst du eintragen: SPOOR_METRICS_TOKEN, SPOOR_LOGS_TOKEN, SPOOR_TRACES_TOKEN, NEW_RELIC_API_KEY

Installation

Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.

Bevor du installierst

Basischecks: 1 von 1 bestanden
Läuft auf deinem Computer mit den Rechten deines Benutzerkontos (Dateien, Netzwerk, Apps).
Das trägst du selbst ein: SPOOR_METRICS_TYPE, SPOOR_LOGS_TYPE, SPOOR_TRACES_TYPE, SPOOR_METRICS_URL, SPOOR_LOGS_URL, SPOOR_TRACES_URL, SPOOR_METRICS_TOKEN, SPOOR_LOGS_TOKEN, SPOOR_TRACES_TOKEN, NEW_RELIC_ACCOUNT_ID, NEW_RELIC_API_KEY

Als geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.

Du brauchst

DockerDocker installiert

Welche App nutzt du?

ClaudeClaude CodeCursorVS CodeChatGPT

Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.

Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein

{
  "mcpServers": {
    "spoor": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SPOOR_METRICS_TYPE",
        "-e",
        "SPOOR_LOGS_TYPE",
        "-e",
        "SPOOR_TRACES_TYPE",
        "-e",
        "SPOOR_METRICS_URL",
        "-e",
        "SPOOR_LOGS_URL",
        "-e",
        "SPOOR_TRACES_URL",
        "-e",
        "SPOOR_METRICS_TOKEN",
        "-e",
        "SPOOR_LOGS_TOKEN",
        "-e",
        "SPOOR_TRACES_TOKEN",
        "-e",
        "NEW_RELIC_ACCOUNT_ID",
        "-e",
        "NEW_RELIC_API_KEY",
        "ghcr.io/asinha07/spoor:0.3.1",
        "serve"
      ],
      "env": {
        "SPOOR_METRICS_TYPE": "your-value",
        "SPOOR_LOGS_TYPE": "your-value",
        "SPOOR_TRACES_TYPE": "your-value",
        "SPOOR_METRICS_URL": "your-value",
        "SPOOR_LOGS_URL": "your-value",
        "SPOOR_TRACES_URL": "your-value",
        "SPOOR_METRICS_TOKEN": "your-value",
        "SPOOR_LOGS_TOKEN": "your-value",
        "SPOOR_TRACES_TOKEN": "your-value",
        "NEW_RELIC_ACCOUNT_ID": "your-value",
        "NEW_RELIC_API_KEY": "your-value"
      }
    }
  }
}

Führe das in deinem Terminal aus

claude mcp add spoor --env SPOOR_METRICS_TYPE=your-value --env SPOOR_LOGS_TYPE=your-value --env SPOOR_TRACES_TYPE=your-value --env SPOOR_METRICS_URL=your-value --env SPOOR_LOGS_URL=your-value --env SPOOR_TRACES_URL=your-value --env SPOOR_METRICS_TOKEN=your-value --env SPOOR_LOGS_TOKEN=your-value --env SPOOR_TRACES_TOKEN=your-value --env NEW_RELIC_ACCOUNT_ID=your-value --env NEW_RELIC_API_KEY=your-value -- docker run -i --rm -e SPOOR_METRICS_TYPE -e SPOOR_LOGS_TYPE -e SPOOR_TRACES_TYPE -e SPOOR_METRICS_URL -e SPOOR_LOGS_URL -e SPOOR_TRACES_URL -e SPOOR_METRICS_TOKEN -e SPOOR_LOGS_TOKEN -e SPOOR_TRACES_TOKEN -e NEW_RELIC_ACCOUNT_ID -e NEW_RELIC_API_KEY ghcr.io/asinha07/spoor:0.3.1 serve

Trag die leeren Werte ein, bevor du ihn startest.

In Cursor öffnen

Oder kopier die Einstellungen selbst

In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen

{
  "mcpServers": {
    "spoor": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SPOOR_METRICS_TYPE",
        "-e",
        "SPOOR_LOGS_TYPE",
        "-e",
        "SPOOR_TRACES_TYPE",
        "-e",
        "SPOOR_METRICS_URL",
        "-e",
        "SPOOR_LOGS_URL",
        "-e",
        "SPOOR_TRACES_URL",
        "-e",
        "SPOOR_METRICS_TOKEN",
        "-e",
        "SPOOR_LOGS_TOKEN",
        "-e",
        "SPOOR_TRACES_TOKEN",
        "-e",
        "NEW_RELIC_ACCOUNT_ID",
        "-e",
        "NEW_RELIC_API_KEY",
        "ghcr.io/asinha07/spoor:0.3.1",
        "serve"
      ],
      "env": {
        "SPOOR_METRICS_TYPE": "your-value",
        "SPOOR_LOGS_TYPE": "your-value",
        "SPOOR_TRACES_TYPE": "your-value",
        "SPOOR_METRICS_URL": "your-value",
        "SPOOR_LOGS_URL": "your-value",
        "SPOOR_TRACES_URL": "your-value",
        "SPOOR_METRICS_TOKEN": "your-value",
        "SPOOR_LOGS_TOKEN": "your-value",
        "SPOOR_TRACES_TOKEN": "your-value",
        "NEW_RELIC_ACCOUNT_ID": "your-value",
        "NEW_RELIC_API_KEY": "your-value"
      }
    }
  }
}

Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.

In VS Code öffnen

Oder kopier die Einstellungen selbst

In .vscode/mcp.json in deinem Projekt einfügen

{
  "servers": {
    "spoor": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SPOOR_METRICS_TYPE",
        "-e",
        "SPOOR_LOGS_TYPE",
        "-e",
        "SPOOR_TRACES_TYPE",
        "-e",
        "SPOOR_METRICS_URL",
        "-e",
        "SPOOR_LOGS_URL",
        "-e",
        "SPOOR_TRACES_URL",
        "-e",
        "SPOOR_METRICS_TOKEN",
        "-e",
        "SPOOR_LOGS_TOKEN",
        "-e",
        "SPOOR_TRACES_TOKEN",
        "-e",
        "NEW_RELIC_ACCOUNT_ID",
        "-e",
        "NEW_RELIC_API_KEY",
        "ghcr.io/asinha07/spoor:0.3.1",
        "serve"
      ],
      "env": {
        "SPOOR_METRICS_TYPE": "your-value",
        "SPOOR_LOGS_TYPE": "your-value",
        "SPOOR_TRACES_TYPE": "your-value",
        "SPOOR_METRICS_URL": "your-value",
        "SPOOR_LOGS_URL": "your-value",
        "SPOOR_TRACES_URL": "your-value",
        "SPOOR_METRICS_TOKEN": "your-value",
        "SPOOR_LOGS_TOKEN": "your-value",
        "SPOOR_TRACES_TOKEN": "your-value",
        "NEW_RELIC_ACCOUNT_ID": "your-value",
        "NEW_RELIC_API_KEY": "your-value"
      }
    }
  }
}

Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.

Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.

Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.

Werte, die du selbst einträgst

SPOOR_METRICS_TYPE env
Backend for metrics: victoriametrics (default), prometheus or newrelic
SPOOR_LOGS_TYPE env
Backend for logs: victorialogs (default), loki or newrelic
SPOOR_TRACES_TYPE env
Backend for traces: victoriatraces (default), tempo or newrelic
SPOOR_METRICS_URL env
Metrics backend URL (VictoriaMetrics or Prometheus) as seen from the container, e.g. http://host.docker.internal:8428
SPOOR_LOGS_URL env
Logs backend URL (VictoriaLogs or Loki) as seen from the container, e.g. http://host.docker.internal:9428
SPOOR_TRACES_URL env
Traces backend URL (VictoriaTraces or Tempo) as seen from the container, e.g. http://host.docker.internal:10428
SPOOR_METRICS_TOKEN geheim env
Bearer token for the metrics backend, if it requires one
SPOOR_LOGS_TOKEN geheim env
Bearer token for the logs backend, if it requires one
SPOOR_TRACES_TOKEN geheim env
Bearer token for the traces backend, if it requires one
NEW_RELIC_ACCOUNT_ID env
New Relic account ID, for signals of type newrelic
NEW_RELIC_API_KEY geheim env
New Relic User API key (NRAK-...), for signals of type newrelic

Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.

Was er kann

Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.

Basischecksi

Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026

✓ Bestanden
Entwickler passt zum Repo
Der Registry-Name wurde mit dem GitHub-Konto asinha07 bestätigt, dem das Repo gehört
… Noch nicht geprüft
Wird noch gepflegt
Dieses Repo haben wir noch nicht von GitHub gelesen
… Noch nicht geprüft
Hat eine Lizenz
Dieses Repo haben wir noch nicht von GitHub gelesen
– Nicht zutreffend
Endpunkt antwortet
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
– Nicht zutreffend
Anmeldung mit OAuth
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
… Noch nicht geprüft
Keine versteckten Anweisungen in Tool-Beschreibungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen

Im Vergleich mit anderen in Sonstigesi

Mehr Sterne als 0 % der anderen 32655 Server in dieser Kategorie
Neuer in der Registry veröffentlicht als 72 % der anderen 32655 Server in dieser Kategorie
Basischecks gleich gut wie oder besser als bei 100 % der anderen 32655 Server in dieser Kategorie

Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.

Häufige Fragen

Ist Spoor sicher?

Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.

Welche Schlüssel oder Codes braucht er?

Das musst du eintragen: SPOOR_METRICS_TOKEN, SPOOR_LOGS_TOKEN, SPOOR_TRACES_TOKEN, NEW_RELIC_API_KEY

Läuft er auf meinem Computer oder in der Cloud?

Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).

Wird er noch gepflegt?

Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.1) kam vor 15 Tagen in die Registry.

Welche Apps können ihn nutzen?

Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Docker auf deinem Computer.

Was kann ich stattdessen nutzen?

In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, SuperCollider

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So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti

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README vom Entwickler

README öffnen

Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.

Spoor

Spoor tells an AI agent what changed in your services, and where it started, in a few hundred tokens.

Website: [telespoor.com](https://telespoor.com)

Spoor is an open-source MCP server and CLI for your observability backends: the VictoriaMetrics stack, Prometheus, Loki, Tempo and New Relic. When something breaks, an agent asks Spoor *"what changed in the last 30 minutes?"* and gets back a short, ranked answer across metrics, logs and traces, with the service where the failure started at the top and its echoes in other services labelled as such.

*Spoor* (n.): the track or trail left by an animal. Spoor follows the trail a failure leaves in your telemetry back

to where it started.

$ spoor what-changed --since 30m

"summary": "11 change(s) in 3 services. Strongest: payment — Failures start here, in \"POST /charge\" (100% of 100 failed traces). Also: payment — Span \"POST /charge\" errors 0.2% → 43.7% (229 spans). … Errors not significantly up (not new) in: product-catalog, recommendation, ad.", "findings": [ {"service": "payment", "signal": "traces", "kind": "error_origin", "score": 0.99, "title": "Failures start here, in \"POST /charge\" (100% of 100 failed traces)"}, {"service": "payment", "signal": "logs", "kind": "new_log_pattern", "score": 0.94, "title": "New error log pattern: \"charge failed: card processor returned for order \""}, {"service": "payment", "signal": "metrics", "kind": "error_rate_up", "score": 0.92, "title": "Error rate up: 0.2% → 40.5%", "onset": "2026-10-16T09:50:00Z"}, {"service": "checkout", "signal": "logs", "kind": "spiking_log_pattern", "score": 0.74, "title": "Error log pattern spiking: \"call to payment.Charge failed with status \" — originates in payment"} ], "coverage": {"metrics": "ok", "logs": "ok", "traces": "ok"} }

Findings carry evidence links to the query behind them (when the token budget allows), so a human can check the agent's work in one click.

Why use it

In a microservice failure, the broken service and every caller of it all light up. An agent with only raw query tools has to decide what to query, compare against "normal" itself, page through results and work out which service is the cause and which are echoes. It burns tool calls and tokens, and often blames the loudest symptom or an error that was always there.

Spoor does that work in plain, deterministic Go and hands the agent the conclusion's evidence:

Cross-signal: request rate, error rate, latency, CPU, memory and consumer lag; new and spiking errors and error volume from logs; per-span errors and latency, all for the same window against the same baseline.
Built for real logs: messages that differ only by IDs, values, stack traces or log prefixes count as one error; JSON errors are read by their message and code fields; services can use different level fields or put the level in the text; spoor doctor finds your fields and writes the config.
Cause vs. echo: trace trees show where failures start and where latency is added. Callers that merely suffer are demoted and labelled — originates in payment.
Steady errors called out: errors that were already there are listed as steady_errors, "not a new problem", so agents stop blaming them.
Bounded output: every tool takes budget_tokens (default 800).
Honest about gaps: coverage says when a backend failed or had no data, the baseline was empty, the error filter matched nothing, or lines lack a service or level field. "No change" never stands in for "can't see".
No LLM inside, read-only, redacted: same input, same output; only query endpoints are ever called; every text field is scrubbed of emails, tokens, IPs and generated IDs before it leaves.

Measured

The benchmark runs Claude Code headless on six incidents (five faults and a no-fault control), five times each, once with the official VictoriaMetrics MCP servers alone and once with Spoor added. On Claude Haiku, 60 isolated sessions:

	Official Victoria MCP servers	+ Spoor
Correct root cause, fault scenarios	19/25	25/25
Correct answer, all scenarios incl. "nothing is wrong"	23/30	30/30
Median tool calls per investigation	24	10
Median time	55 s	23 s
Total cost	$0.60	$0.19 (−68%)

Synthetic incidents and one small model, so treat it as a first data point.

On real production logs (about 25 services, mixed formats, read-only replay): the digest was 99.8% smaller than the raw evidence it replaces, with zero candidates against four from v0.1.1, and its baseline counts matched independent queries exactly. It was not faster than four hand-written queries (28 s vs 26 s), and accuracy on real incidents is not yet measured.

Method, per-scenario results, the production replay and caveats: docs/benchmark.md.

Quickstart

Requires Go 1.25+, Docker and jq. About five minutes.

git clone https://github.com/asinha07/spoor && cd spoor

make it-up # empty VictoriaMetrics, VictoriaLogs, VictoriaTraces in Docker make demo-data # six synthetic incidents; ground truth in scenarios/out/ make build

T=scenarios/out/payment-failure.truth.json ./bin/spoor what-changed --since $(jq -r .since $T) --until $(jq -r .until $T) --pretty

Connect it to Claude Code:

claude mcp add spoor \

-e SPOOR_METRICS_URL=http://localhost:8428 \ -e SPOOR_LOGS_URL=http://localhost:9428 \ -e SPOOR_TRACES_URL=http://localhost:10428 \ -- "$PWD/bin/spoor" serve

Run it from the spoor directory (or add -s user to register it everywhere), then ask Claude: *"Something is failing in the shop. What changed, and where did it start?"*

On your own backends, start with spoor doctor --since 15m: it checks what Spoor can see and suggests config. Other clients, Grafana-only access and the rest: [Getting started](docs/getting-started.md).

Backends

Each signal picks its backend with type:, and you can mix them:

	Metrics	Logs	Traces
VictoriaMetrics stack	VictoriaMetrics	VictoriaLogs	VictoriaTraces
Grafana stack	Prometheus, Mimir, Thanos	Loki	Tempo
New Relic	Transaction events (NRQL)	Log events	Span events

metrics: {type: prometheus, url: http://prometheus:9090}

logs: {type: loki, url: http://loki:3100} traces: {type: tempo, url: http://tempo:3200}

Setup and what differs per backend (Tempo statistics are sampled; New Relic span changes need the service's metrics to agree): [Backends](docs/backends.md).

Tools

Tool	Returns
`what_changed`	Ranked changes across all signals, with cause/echo attribution, for one service or all. Start here.
`new_errors`	Error log patterns that are new or spiking, with counts, first-seen time and a redacted example.
`trace_diff`	Span names that got slower or started failing, with an example trace ID.
`list_services`	Services in the window and which signals have data for each.

Inputs: service, since, until, baseline (previous, 1d, 7d), budget_tokens. Full reference: docs/tools.md.

How it works

flowchart LR

For a window and a baseline (by default, the six windows just before it), Spoor scores every metric with a robust z-score, compares error-log patterns with a Poisson test, compares span error rates and p95s, walks failed traces to the span where each failure started, boosts changes confirmed by more than one signal, and trims the result to the token budget. Details: architecture · scoring and attribution.

Run it for a team

spoor serve --http :8080 --auth-token … serves MCP over streamable HTTP with bearer-token auth, /healthz and Prometheus /metrics. Ready-made, hardened manifests:

Kubernetes (Kustomize): deploy/kubernetes/
Docker Compose: deploy/docker/
systemd: deploy/systemd/

Security, sizing, monitoring and upgrades: [Production guide](docs/production.md).

Verified on the OpenTelemetry Demo

On the OpenTelemetry Demo v3.1.0 (about 20 real services), a 50% payment failure came back as "payment: failures start here, in charge", with checkout and frontend errors labelled "originates in payment" (732 tokens); the adHighCpu fault came back as the single finding "ad: CPU up 0.07 → 3.97 cores" (192 tokens). Reproduce it: deploy/demo/.

Documentation


Getting started	Install, try, connect an agent
Production	Kubernetes, Docker, systemd, security, monitoring
Backends	VictoriaMetrics stack, Prometheus, Loki, Tempo, New Relic
Configuration	Every option and recipes for common setups
Tools	Inputs and response format
Architecture · How it works	Design and scoring
Benchmark · Comparison	Why use it, and what else exists
Troubleshooting · FAQ	When something looks wrong

Status and limitations

Early (pre-1.0). Tool output fields may still change between minor versions; see the changelog.

The scoring was developed and benchmarked on the VictoriaMetrics stack. Prometheus, Loki and Tempo pass the same scenario tests against real containers; New Relic was checked on one real account. Tempo statistics are sampled, and New Relic span changes are reported only when service metrics agree; see backends.
Cause/echo attribution needs traces; without them Spoor still ranks metric and log changes.
Attribution reads at most 50,000 span rows per read; on very busy systems a trace can be cut.
The benchmark uses synthetic incidents written by the author and one model; treat it as a first data point, and replay your own past incidents with bench/replay.py before relying on it.
Metrics and traces are queried per service; on large estates pass service when you know it.

Roadmap

Next: accuracy on labelled real incidents, "since the last deploy" windows, Datadog and Elasticsearch backends, exact Tempo statistics through TraceQL metrics. The full list, and what is deliberately out of scope: [docs/roadmap.md](docs/roadmap.md).

Contributing

Issues and pull requests are welcome. make test needs nothing but Go; see CONTRIBUTING.md. Security issues: SECURITY.md.

License

Apache-2.0. See LICENSE.

Spoor

Überblick

Die drei Säulen von PopNx MCP

Was du damit machen kannst

Installation

Bevor du installierst

Werte, die du selbst einträgst

Was er kann

Basischecksi

Entwickler passt zum Repo

Wird noch gepflegt

Hat eine Lizenz

Endpunkt antwortet

Anmeldung mit OAuth

Keine versteckten Anweisungen in Tool-Beschreibungen

Im Vergleich mit anderen in Sonstigesi

Häufige Fragen

Alternativen

Spec Driven Development

Remote Desktop Commander

SuperCollider

README vom Entwickler

Spoor

Why use it

Measured

Quickstart

Backends

Tools

How it works

Run it for a team

Verified on the OpenTelemetry Demo

Documentation

Status and limitations

Roadmap

Contributing

License