MCP Server
Read-only access to Spanlens LLM observability: request logs, costs, agent traces, anomalies, model savings and per-user analytics.
Installation
Les étapes d'installation sont dans la documentation ci-dessous. Documentation
Documentation
Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.
Sommaire
@spanlens/mcp-server
Query your LLM observability data from inside Cursor, Continue, or Claude Desktop. Powered by Spanlens.
Ask your IDE's agent things like "what's my OpenAI spend this week?", "any cost anomalies?", or "show me the most expensive trace today" — and get real answers from your Spanlens workspace through the Model Context Protocol.
30-second setup
1. Issue a public-scope Spanlens key
Public-scope keys (sl_live_pub_*) are read-only — they can query your dashboard data but cannot trigger LLM proxy spend. This server refuses to start with a full-access key because the credential will live in a plaintext config file in your IDE.
Create one at spanlens.io/projects → the Public Keys card at the top → + New public key. Copy the sl_live_pub_… value when it's shown.
2. Add the server to your IDE
Cursor — ~/.cursor/mcp.json (or workspace .cursor/mcp.json)
{
"mcpServers": {
"spanlens": {
"command": "npx",
"args": ["-y", "@spanlens/mcp-server"],
"env": { "SPANLENS_API_KEY": "sl_live_pub_..." }
}
}
}Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
{
"mcpServers": {
"spanlens": {
"command": "npx",
"args": ["-y", "@spanlens/mcp-server"],
"env": { "SPANLENS_API_KEY": "sl_live_pub_..." }
}
}
}Continue — ~/.continue/config.yaml
mcpServers:
- name: spanlens
command: npx
args: ['-y', '@spanlens/mcp-server']
env:
SPANLENS_API_KEY: sl_live_pub_...3. Reload your IDE and start asking
The agent will discover seven tools and use them automatically when relevant.
What you can ask
| Question | Tool called |
|---|---|
| "How much have we spent on LLMs this week?" | get_stats |
| "Break down cost by model for the last 30 days" | get_stats(groupBy=model) |
| "Which provider is costing us the most this week?" | get_stats(groupBy=provider) |
| "Any cost or latency anomalies?" | get_anomalies |
| "Show me the 10 most recent error calls on gpt-4o" | query_requests |
| "List my recent agent runs" | list_traces |
"Walk me through trace abc123…" | get_trace |
| "What models could we swap to save money?" | get_savings |
| "Which end-user is driving the most spend?" | get_user_analytics |
Available tools
| Tool | What it returns |
|---|---|
get_stats | Aggregate cost, request count, token usage, latency, error rate. Optional groupBy for a breakdown of cost, requests, latency and error rate: model returns one row per provider and model pair, and provider rolls those rows up into one row per provider, with latency and error rate weighted by request count. |
query_requests | Individual LLM requests with cost, latency, model, error message. Filter by model, provider, status, userId, since, limit. |
list_traces | Agent trace summaries (name, status, duration, span count, total tokens, total cost) for discovering trace IDs. Filter by limit, status, since, query. Pair with get_trace for the full span tree. |
get_trace | Full agent span tree for a trace ID — every LLM/tool/retrieval span with timing, tokens, cost. |
get_anomalies | Cost / latency / error-rate anomalies the platform has detected. Optional since (observation window) and sigma (sensitivity). |
get_savings | Model-swap recommendations with projected monthly savings and adoption status. |
get_user_analytics | Per-end-user usage breakdown — total cost, request count, models touched, recent calls. |
Safety
This server is designed for the IDE-config use case, where the credential sits in a plaintext file that's easy to leak (dotfile sync, screen shares, accidental commits). Two defenses:
- Public-scope keys only. The server boots, calls
/api/v1/me/key-infoagainst your workspace, and verifies the key hasscope=public. If you accidentally paste a full-accesssl_live_*key, the server refuses to start with a clear error pointing at the correct key type. - Read-only at the API layer. Even if a public key leaks, it cannot call
/proxy/*or/ingest/*— the Spanlens server enforces this with a 403 +PUBLIC_KEY_WRITE_FORBIDDENcode. The blast radius of a leak is "competitor sees my usage stats", not "competitor runs up my OpenAI bill".
Configuration
| Environment variable | Default | Notes |
|---|---|---|
SPANLENS_API_KEY | required | A sl_live_pub_* key from the Public Keys card on /projects. The server refuses to start without one, and refuses to start with a sl_live_* (full) key. |
SPANLENS_BASE_URL | https://api.spanlens.io | Override for self-hosted Spanlens. Trailing slashes are normalised. |
SPANLENS_TIMEOUT_MS | 30000 | How long each API call may take, in milliseconds, before it fails with a timeout error. Applies to the startup key check and to every tool call. Accepts a whole number from 1 to 600000. Any other value stops the server at startup with an error that names the variable. |
Self-hosted Spanlens
{
"mcpServers": {
"spanlens": {
"command": "npx",
"args": ["-y", "@spanlens/mcp-server"],
"env": {
"SPANLENS_API_KEY": "sl_live_pub_...",
"SPANLENS_BASE_URL": "https://spanlens.your-company.com"
}
}
}
}Links
License
MIT — see LICENSE in the repo root.
Questions fréquentes
Qu'est-ce que MCP Server ?
Read-only access to Spanlens LLM observability: request logs, costs, agent traces, anomalies, model savings and per-user analytics.
Comment installer MCP Server ?
Suivez les étapes d'installation de sa documentation, affichée sur cette page et dans son dépôt.
MCP Server est-il open source ?
Oui. Son code est sur GitHub sous licence MIT.
Qui maintient MCP Server ?
Il est publié par spanlens. AI.tn le référence sans le maintenir ni le recommander.
Issu des listes communautaires awesome-mcp-servers et du dépôt GitHub du projet. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.