AgentTel MCP server

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AgentTel enriches OpenTelemetry telemetry with the structured context AI agents need to autonomously diagnose, reason about, and resolve production incidents — without human interpretation of dashboards. Works across the full stack: JVM backends (Java, Kotlin, Scala), Go backends, Node.js/TypeScript backends, Python backends (FastAPI, Django, Flask), and browser frontends (TypeScript/JavaScript).

Standard observability answers "What happened?" AgentTel adds "What does an AI agent need to know to act on this?"

The Problem

Modern observability tools generate massive volumes of telemetry — traces, metrics, logs — optimized for human consumption through dashboards and alert rules. AI agents tasked with autonomous incident response face critical gaps:

• No behavioral context — Spans lack baselines, so agents can't distinguish normal from anomalous
• No topology awareness — Agents don't know which services are critical, who owns them, or what depends on what
• No decision metadata — Is this operation retryable? Is there a fallback? What's the runbook?
• No actionable interface — Agents can read telemetry but can't query live system state or execute remediation

AgentTel closes these gaps at the instrumentation layer.

Design Philosophy

Core principle: telemetry should carry enough context for AI agents to reason and act autonomously.

AgentTel enriches telemetry at three levels — all configurable via YAML, no code changes required:

Level	Where	What	Example
Topology	OTel Resource (once per service)	Service identity, ownership, dependencies	team, tier, on-call channel
Baselines	Span attributes (per operation)	What "normal" looks like	P50/P99 latency, error rate
Decisions	Span attributes (per operation)	What an agent is allowed to do	retryable, runbook URL, escalation level

Topology is set once on the OTel Resource and automatically associated with all telemetry by the SDK. Baselines and decision metadata are attached per-operation on spans. This avoids redundant data on every span while ensuring agents always have the full context.

Quick Demo

Try AgentTel in one command — starts a demo payment service with OTel Collector and Jaeger:

cd examples/spring-boot-example
docker compose -f docker/docker-compose.yml up --build

Then open Jaeger to see enriched traces, Swagger UI for the API, and MCP Tool Docs for the agent interface.

What AgentTel Provides

Enriched Telemetry (agenttel-core)

Every span is automatically enriched with agent-actionable attributes:

Category	Attributes	Purpose
Topology	agenttel.topology.team, tier, domain, dependencies	Service identity and dependency graph
Baselines	agenttel.baseline.latency_p50_ms, error_rate, source	What "normal" looks like for each operation
Decisions	agenttel.decision.retryable, idempotent, runbook_url, escalation_level	What an agent is allowed to do
Anomalies	agenttel.anomaly.detected, pattern, score	Real-time deviation detection
SLOs	agenttel.slo.budget_remaining, burn_rate	Error budget consumption tracking

Agent Interface Layer (agenttel-agent)

A complete toolkit for AI agent interaction with production systems:

Component	Description
MCP Server	JSON-RPC server implementing the Model Context Protocol — exposes telemetry as tools AI agents can call
Health Aggregation	Real-time service health from span data with operation-level and dependency-level metrics
Incident Context	Structured incident packages: what's happening, what changed, what's affected, what to do
Remediation Framework	Registry of executable remediation actions with approval workflows
Action Tracking	Every agent decision and action recorded as OTel spans for full auditability
Context Formatters	Prompt-optimized output formats (compact, full, JSON) tuned for LLM context windows

Frontend Telemetry (agenttel-web)

Browser SDK for agent-ready frontend observability:

Feature	Description
Auto-Instrumentation	Page loads (Navigation Timing API), SPA navigation, fetch/XMLHttpRequest interception, click/submit interactions, JavaScript errors
Journey Tracking	Multi-step user funnel tracking with completion rates, abandonment detection, and duration baselines
Anomaly Detection	Client-side pattern detection — rage clicks, API failure cascades, slow page loads, error loops, funnel drop-offs
Cross-Stack Correlation	W3C Trace Context injection on all outgoing requests; backend trace ID extraction from responses
Route Baselines	Per-route configuration of expected page load times, API response times, error rates, and business criticality
Decision Metadata	Escalation levels, runbook URLs, retry policies, and fallback pages per route

Instrumentation Agent (agenttel-instrument)

IDE-integrated MCP server for automated instrumentation setup:

Tool	Description
analyze_codebase	Scans Java/Spring Boot source code — detects endpoints, dependencies, and framework
instrument_backend	Generates backend config — Gradle/Maven dependencies, annotations, agenttel.yml
instrument_frontend	Generates frontend config — React route detection, criticality inference, SDK initialization
validate_instrumentation	Validates agenttel.yml completeness against source code
suggest_improvements	Analyzes config and suggests fixes — missing baselines, uncovered endpoints, stale thresholds
apply_improvements	Auto-applies low-risk improvements using live health data; flags high-risk items for review

GenAI Instrumentation (agenttel-genai)

Full observability for AI/ML workloads on the JVM:

Framework	Approach	Coverage
Spring AI	SpanProcessor enrichment of existing Micrometer spans	Framework tag, cost calculation
LangChain4j	Decorator-based full instrumentation	Chat, embeddings, RAG retrieval
Anthropic SDK	Client wrapper	Messages API with token/cost tracking
OpenAI SDK	Client wrapper	Chat completions with token/cost tracking
AWS Bedrock	Client wrapper	Converse API with token/cost tracking

Agent Observability (agenttel-agentic)

Full lifecycle tracing for AI agents with 70+ semantic attributes:

Feature	Description
Invocation Lifecycle	Goal, status, step count, max steps for each agent execution
Reasoning Steps	Thought, action, observation, evaluation, revision tracking
Tool Calls	Tool name, success/error/timeout status per call
Task Decomposition	Nested task breakdown with depth and parent tracking
Orchestration Patterns	Sequential, parallel, evaluator-optimizer, handoff, ReAct, orchestrator-workers
Cost Aggregation	Automatic LLM cost rollup from GenAI spans to agent sessions
Guardrails	Block, warn, log, escalate actions with named guardrails
Human Checkpoints	Approval, feedback, correction gates with wait time tracking
Loop Detection	Detects stuck reasoning loops (identical tool calls)
Quality Signals	Goal achievement, human interventions, eval scores
RAG Pipeline	Retriever and reranker spans with relevance scoring
Error Classification	Source (LLM/tool/agent/guardrail/timeout/network), retryability

Three integration styles — programmatic, annotation, or YAML config:

Programmatic:

AgentTracer tracer = AgentTracer.create(openTelemetry)
    .agentName("incident-responder")
    .agentType(AgentType.SINGLE)
    .build();

try (AgentInvocation inv = tracer.invoke("Diagnose high latency")) {
    inv.step(StepType.THOUGHT, "Need to check service metrics");
    try (ToolCallScope tool = inv.toolCall("get_service_health")) {
        tool.success();
    }
    inv.complete(true);
}

@AgentMethod annotation (Spring Boot):

@AgentMethod(name = "incident-responder", type = "single", maxSteps = 100)
public IncidentReport diagnose(String incidentId) {
    // Automatically wrapped in AgentInvocation — no manual tracer calls
    return analyzeAndRespond(incidentId);
}

YAML config (Spring Boot):

agenttel:
  agentic:
    agents:
      incident-responder:
        type: single
        max-steps: 100
        loop-threshold: 5

Quick Start

AgentTel supports multiple integration paths — pick what fits your stack:

Path	Best For	Effort
Spring Boot Starter	Spring Boot applications	Add dependency + YAML config
Go SDK	Go services (net/http, Gin, gRPC)	go get + YAML config
Node.js SDK	Express / Fastify services	npm install + YAML config
Python SDK	FastAPI / Python services	pip install + YAML config
JavaAgent Extension	Any JVM app (no code changes)	JVM flag + YAML config
Web SDK	Browser/SPA applications	npm install + init call
Instrument Agent	IDE-assisted setup	Run MCP server in IDE

Backend: Spring Boot

1. Add Dependencies

Maven:

<dependencies>
    <!-- Core: span enrichment, baselines, anomaly detection, SLO tracking -->
    <dependency>
        <groupId>dev.agenttel</groupId>
        <artifactId>agenttel-spring-boot-starter</artifactId>
        <version>0.4.0-alpha</version>
    </dependency>

    <!-- Optional: GenAI instrumentation -->
    <dependency>
        <groupId>dev.agenttel</groupId>
        <artifactId>agenttel-genai</artifactId>
        <version>0.4.0-alpha</version>
    </dependency>

    <!-- Optional: Agent interface layer (MCP server, incident context, remediation) -->
    <dependency>
        <groupId>dev.agenttel</groupId>
        <artifactId>agenttel-agent</artifactId>
        <version>0.4.0-alpha</version>
    </dependency>
</dependencies>

Gradle:

// build.gradle.kts
dependencies {
    // Core: span enrichment, baselines, anomaly detection, SLO tracking
    implementation("dev.agenttel:agenttel-spring-boot-starter:0.4.0-alpha")

    // Optional: GenAI instrumentation
    implementation("dev.agenttel:agenttel-genai:0.4.0-alpha")

    // Optional: Agent interface layer (MCP server, incident context, remediation)
    implementation("dev.agenttel:agenttel-agent:0.4.0-alpha")
}

2. Configure Your Service

All enrichment is driven by YAML configuration -- no code changes needed:

# application.yml
agenttel:
  # Topology: set once on the OTel Resource, associated with all telemetry
  topology:
    team: payments-platform
    tier: critical
    domain: commerce
    on-call-channel: "#payments-oncall"
  dependencies:
    - name: postgres
      type: database
      criticality: required
      timeout-ms: 5000
      circuit-breaker: true
    - name: stripe-api
      type: rest_api
      criticality: required
      fallback: "Return cached pricing"

  # Reusable operational profiles — reduce repetition across operations
  profiles:
    critical-write:
      retryable: false
      escalation-level: page_oncall
      safe-to-restart: false
    read-only:
      retryable: true
      idempotent: true
      escalation-level: notify_team

  # Per-operation baselines and decision metadata
  # Use bracket notation [key] for operation names with special characters
  operations:
    "[POST /api/payments]":
      profile: critical-write
      expected-latency-p50: "45ms"
      expected-latency-p99: "200ms"
      expected-error-rate: 0.001
      retryable: true               # overrides profile default
      idempotent: true
      runbook-url: "https://wiki/runbooks/process-payment"
    "[GET /api/payments/{id}]":
      profile: read-only
      expected-latency-p50: "15ms"
      expected-latency-p99: "80ms"

  baselines:
    rolling-window-size: 1000
    rolling-min-samples: 10
  anomaly-detection:
    z-score-threshold: 3.0

3. Optional: Annotate for IDE Support

Annotations are optional -- YAML config above is sufficient. Use @AgentOperation when you want IDE autocomplete and compile-time validation. Reference profiles to avoid repeating values:

@AgentOperation(profile = "critical-write")
@PostMapping("/api/payments")
public ResponseEntity<PaymentResult> processPayment(@RequestBody PaymentRequest req) {
    // Your business logic — spans are enriched automatically
}

When both YAML config and annotations define the same operation, YAML config takes priority. Per-operation values override profile defaults.

4. Start the MCP Server (Optional)

// Expose telemetry to AI agents via MCP
McpServer mcp = new AgentTelMcpServerBuilder()
    .port(8081)
    .contextProvider(agentContextProvider)
    .remediationExecutor(remediationExecutor)
    .build();
mcp.start();

AI agents can now call tools like get_service_health, get_incident_context, list_remediation_actions, and execute_remediation over JSON-RPC.

5. What You Get

Resource attributes (set once per service, associated with all telemetry):

agenttel.topology.team         = "payments-platform"
agenttel.topology.tier         = "critical"
agenttel.topology.domain       = "commerce"
agenttel.topology.on_call_channel = "#payments-oncall"
agenttel.topology.dependencies = [{"name":"postgres","type":"database",...}]

Span attributes (per operation, only on operations with registered metadata):

agenttel.baseline.latency_p50_ms = 45.0
agenttel.baseline.latency_p99_ms = 200.0
agenttel.baseline.error_rate     = 0.001
agenttel.baseline.source         = "static"
agenttel.decision.retryable      = true
agenttel.decision.runbook_url    = "https://wiki/runbooks/process-payment"
agenttel.decision.escalation_level = "page_oncall"
agenttel.anomaly.detected        = false
agenttel.slo.budget_remaining    = 0.85

When an incident occurs, agents get structured context via MCP:

=== INCIDENT inc-a3f2b1c4 ===
SEVERITY: HIGH
SUMMARY: POST /api/payments experiencing elevated error rate (5.2%)

## WHAT IS HAPPENING
Error Rate: 5.2% (baseline: 0.1%)
Latency P50: 312ms (baseline: 45ms)
Patterns: ERROR_RATE_SPIKE

## WHAT CHANGED
Last Deploy: v2.1.0 at 2025-01-15T14:30:00Z

## WHAT IS AFFECTED
Scope: operation_specific
User-Facing: YES
Affected Deps: stripe-api

## SUGGESTED ACTIONS
  - [HIGH] rollback_deployment: Rollback to previous version (NEEDS APPROVAL)
  - [MEDIUM] enable_circuit_breakers: Circuit break stripe-api

Backend: Python (FastAPI)

1. Install

pip install agenttel[fastapi]

# Optional extras
pip install agenttel[openai]       # OpenAI instrumentation
pip install agenttel[anthropic]    # Anthropic instrumentation
pip install agenttel[langchain]    # LangChain instrumentation
pip install agenttel[all]          # Everything

2. Configure

# agenttel.yml
agenttel:
  topology:
    service-name: payment-service
    team: payments-platform
    tier: critical
    domain: commerce
    on-call-channel: "#payments-oncall"
  operations:
    "POST /api/payments":
      expected-latency-p50: 45ms
      expected-latency-p99: 200ms
      retryable: true
      runbook-url: "https://wiki/runbooks/process-payment"
  slo:
    availability:
      target: 0.999
      type: availability

3. Instrument

from fastapi import FastAPI
from agenttel.fastapi import instrument_fastapi

app = FastAPI()
instrument_fastapi(app)  # One-line integration

instrument_fastapi() attaches the AgentTel span processor to your existing OpenTelemetry TracerProvider (or creates one if none is set), instruments the app with opentelemetry-instrumentation-fastapi, and enriches each HTTP server span with topology, baselines and decision attributes for its METHOD /route — identical attributes to the JVM SDK.

Backend: Go

1. Install

go get go.agenttel.dev/agenttel-go@latest

2. Configure

# agenttel.yml — same format as JVM/Python SDKs
agenttel:
  topology:
    service-name: payment-service
    team: payments-platform
    tier: critical
  operations:
    "POST /api/payments":
      expected-latency-p50: 45ms
      expected-latency-p99: 200ms
      retryable: true

3. Instrument

import (
    "go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp"
    sdktrace "go.opentelemetry.io/otel/sdk/trace"

    agenttel "go.agenttel.dev/agenttel-go"
    agmw "go.agenttel.dev/agenttel-go/middleware/http"
)

engine, err := agenttel.New().WithConfigFile("agenttel.yml").Build()
if err != nil {
    log.Fatal(err)
}

tp := sdktrace.NewTracerProvider(
    sdktrace.WithResource(engine.Resource()),          // topology on the resource
    sdktrace.WithSpanProcessor(engine.SpanProcessor()),
    sdktrace.WithBatcher(exporter),
)
otel.SetTracerProvider(tp)

// otelhttp creates the server span; AgentTel enriches it for the matched "METHOD /route"
handler := otelhttp.NewHandler(agmw.Middleware(agmw.WithEngine(engine))(mux), "server")

Requires go.opentelemetry.io/otel/sdk v1.39.0+.

Also supports Gin (middleware/gin) and gRPC (middleware/grpc) out of the box. See Go Quickstart for details.

Backend: Node.js (Express / Fastify)

1. Install

npm install @agenttel/node @opentelemetry/api @opentelemetry/sdk-trace-node @opentelemetry/instrumentation-http

2. Configure

# agenttel.yml — same format as all other SDKs
agenttel:
  topology:
    service-name: payment-service
    team: payments-platform
    tier: critical
  operations:
    "POST /api/payments":
      expected-latency-p50: 45ms
      expected-latency-p99: 200ms
      retryable: true

3. Instrument

import { AgentTelEngine, expressMiddleware } from '@agenttel/node';

const engine = AgentTelEngine.fromConfigFile('agenttel.yml');

// Register the processor with your tracer provider (alongside your exporter)
const provider = new NodeTracerProvider({
  resource: new Resource(engine.resourceAttributes()),
  spanProcessors: [engine.spanProcessor, new BatchSpanProcessor(exporter)],
});
provider.register();
registerInstrumentations({ instrumentations: [new HttpInstrumentation(), new ExpressInstrumentation()] });

app.use(expressMiddleware({ engine }));  // topology, baselines and decisions on the server span

Works under plain node as ESM (import) or CommonJS (require), Node.js 18+.

Also supports Fastify (fastifyPlugin). See Node.js Quickstart for details.

Frontend: Browser SDK

npm install @agenttel/web

import { AgentTelWeb } from '@agenttel/web';

AgentTelWeb.init({
  appName: 'checkout-web',
  appVersion: '1.0.0',
  environment: 'production',
  collectorEndpoint: '/otlp',

  routes: {
    '/checkout/:step': {
      businessCriticality: 'revenue',
      baseline: { pageLoadP50Ms: 800, apiCallP50Ms: 300 },
      decision: { escalationLevel: 'page_oncall', runbookUrl: 'https://wiki/runbooks/checkout' },
    },
  },

  journeys: {
    checkout: {
      steps: ['/products', '/cart', '/checkout/shipping', '/checkout/payment', '/confirmation'],
      baseline: { completionRate: 0.65, avgDurationS: 300 },
    },
  },

  anomalyDetection: {
    rageClickThreshold: 3,
    apiFailureCascadeThreshold: 3,
    errorLoopThreshold: 5,
  },
});

The SDK automatically instruments page loads, SPA navigation, API calls, clicks, and errors — with cross-stack correlation via W3C Trace Context headers.

IDE: Instrument Agent

Run the instrument agent as an MCP server in your IDE (Cursor, VS Code, etc.):

pip install agenttel-instrument
agenttel-instrument --config instrument.yml

Then ask your IDE agent: "Analyze my codebase and generate AgentTel configuration" — it will scan your endpoints, detect dependencies, and generate a complete agenttel.yml.