Spoor

Name: Spoor
Author: asinha07

by asinha07version 0.3.1i★ –iRuns on your computer

Registry nameio.github.asinha07/spoor

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: Developer tools
You need to fill in: SPOOR_METRICS_TOKEN, SPOOR_LOGS_TOKEN, SPOOR_TRACES_TOKEN, NEW_RELIC_API_KEY

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: 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

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

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "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"
      }
    }
  }
}

Run this in your terminal

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

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": {
    "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"
      }
    }
  }
}

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": {
    "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"
      }
    }
  }
}

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

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 secret env
Bearer token for the metrics backend, if it requires one
SPOOR_LOGS_TOKEN secret env
Bearer token for the logs backend, if it requires one
SPOOR_TRACES_TOKEN secret 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 secret env
New Relic User API key (NRAK-...), for signals of type newrelic

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 asinha07, 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 Developer toolsi

More stars than 0% of the other 4900 servers in this category
Published to the registry more recently than 72% of the other 4900 servers in this category
Basic checks equal to or better than 100% of the other 4900 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 Spoor 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: SPOOR_METRICS_TOKEN, SPOOR_LOGS_TOKEN, SPOOR_TRACES_TOKEN, NEW_RELIC_API_KEY

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 (0.3.1) reached the registry 15 days ago.

Which apps can use it?

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

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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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.

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

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

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