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Agent tracing, cost tracking, anomaly detection for LLM agents
Agent tracing, cost tracking, anomaly detection for LLM agents
This is a well-designed observability MCP server with clean architecture, proper input validation, and no authentication requirements (appropriate for a local observability tool). The code is defensive, properly handles data types with Zod schemas, and maintains an in-memory data store without sensitive credential handling. Permissions are appropriate for the stated purpose—file system and network access are minimal, matching a developer tool focused on local telemetry. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
4 files analyzed · 9 issues found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mdfifty50-boop-agentic-observability": {
"args": [
"-y",
"agentic-observability-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
AI agent observability for MCP. Tracing, cost tracking, performance monitoring, anomaly detection, and audit trails — all via Model Context Protocol.
AI agents burn tokens, call tools, make decisions, and sometimes get stuck in loops. You need to know what they're doing, what it costs, and when something goes wrong. No existing MCP server provides unified agent observability. This one does.
Track every LLM call, every tool invocation, every decision — with automatic cost calculation and anomaly detection.
trace_agent_action — Log any agent action (tool calls, LLM requests, decisions, errors) with metadata and timestampstrack_token_usage — Track token usage per LLM call with automatic cost calculation from built-in pricing tables (Claude, GPT, Gemini, Mistral)get_cost_report — Aggregate cost breakdown across sessions, grouped by model, provider, tool, or sessionlog_tool_call — Log MCP tool calls with latency, success/failure, and error detailsget_session_summary — Full session report: cost, tokens, tool stats, error count, model breakdown, durationdetect_anomaly — Flag unusual patterns:
observability://pricing — Current LLM pricing table (per-token costs for all major models)observability://best-practices — Agent observability best practices guideAdd to your MCP configuration (~/.claude/settings.json or project .mcp.json):
{
"mcpServers": {
"agent-observability": {
"command": "npx",
"args": ["agentic-observability-mcp"]
}
}
}
Add to .cursor/mcp.json:
{
"mcpServers": {
"agent-observability": {
"command": "npx",
"args": ["agentic-observability-mcp"]
}
}
}
Same pattern — add the server to your MCP configuration file.
For agent framework developers:
Instrument your agent loop with track_token_usage and log_tool_call to get real-time cost and performance data without building your own telemetry.
For teams running agents in production:
Use detect_anomaly to catch stuck agents (loop detection), runaway costs (cost spike), and degraded tool performance (latency spike) before they become incidents.
For cost optimization:
Use get_cost_report grouped by model to identify which models are eating your budget. Switch expensive reasoning calls to cheaper models where quality allows.
For compliance and audit:
Every trace_agent_action with type "decision" creates an audit record. Include reasoning in the description for full traceability.
Agent: "Track that I just used 1,500 input tokens and 800 output tokens
with claude-sonnet-4 on Anthropic for session agent_run_001"
--> Returns:
{
"call_cost": 0.016500,
"running_session_total": 0.016500,
"model": "claude-sonnet-4",
"provider": "anthropic",
"pricing_used": { "input": 0.000003, "output": 0.000015 },
"model_breakdown": {
"claude-sonnet-4": {
"calls": 1,
"input_tokens": 1500,
"output_tokens": 800,
"cost": 0.016500
}
}
}
Agent: "Check session agent_run_001 for anomalies — cost spike and loop detection"
--> Returns:
{
"anomalies_found": 0,
"anomalies": [],
"checks_performed": ["cost_spike", "loop_detection"]
}
Automatically calculates costs for these models (override with custom pricing if needed):
| Provider | Models |
|---|---|
| Anthropic | Claude Opus 4, Sonnet 4, Haiku 4, 3.5 Sonnet, 3.5 Haiku, 3 Opus |
| OpenAI | GPT-4o, GPT-4o Mini, GPT-4 Turbo, o1, o1-mini, o3-mini |
| Gemini 2.5 Pro, 2.5 Flash, 2.0 Flash, 1.5 Pro | |
| Mistral | Large, Medium, Small, Codestral |
| Local | Zero cost (self-hosted models) |
| Tier | Price | Agents | Retention | Events/Month |
|---|---|---|---|---|
| Free | $0 | 1 | 7 days | 10,000 |
| Starter | $59/month | 5 | 30 days | 100,000 |
| Pro | $299/month | 25 | 90 days | 1,000,000 |
| Enterprise | $999/month | Unlimited | 1 year | Unlimited + SOC2 reporting |
v1 uses in-memory storage (Maps). Data is lost on server restart. The storage layer (src/storage.js) is structured for easy swap to Redis or Postgres in v2.
MIT
mcp, mcp-server, observability, agent-tracing, cost-tracking, token-usage, ai-agent, performance-monitoring, audit-trail, model-context-protocol
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