Python

from mcpd import FeedbackKind, Mcpd, SpanKind

# Everything here has an MCPD_* environment variable behind it, so the same code
# runs in development and in production with no edit. `Mcpd()` with no arguments
# talks to a local daemon.
mcpd = Mcpd(
    endpoint=os.environ.get("MCPD_ENDPOINT", "http://localhost:4317"),
    project="support",
    environment="development",
    service_name="support-agent",
    service_version="1.4.2",
)

# The root span is the run. Everything opened inside it becomes a child,
# because the active span lives in a `contextvars.ContextVar`.
with mcpd.trace("support_agent", session_id="sess-8121", user_id=user_id,
                tags=["support", "orders"]) as run:
    run.set_input({"question": question})

    # A model call. `set_usage` is what mcpd prices the run from, so the
    # cost on the trace is the cost you were actually billed.
    with mcpd.span("plan", kind=SpanKind.MODEL_GENERATION) as generation:
        generation.set_model("gpt-4o-mini", provider="openai")
        generation.set_input([
            {"role": "system", "content": "You are a support agent."},
            {"role": "user", "content": question},
        ])
        time.sleep(0.12)  # stand-in for the provider round trip
        generation.set_output("I should look the order up first.")
        generation.set_usage(input_tokens=812, output_tokens=96, cache_read=128)

    # A tool call that fails. The `with` block records the exception on the
    # span, marks it as an error, and lets the exception through unchanged.
    order = None
    try:
        with mcpd.span("lookup_order", kind=SpanKind.TOOL_CALL) as tool:
            tool.set_entity("tool", entity_id="orders.lookup", name="Order lookup")
            tool.set_input({"order_id": "A-7741"})
            order = lookup_order("A-7741")
            tool.set_output(order)
    except TimeoutError as exc:
        # The span already carries the error. This is the agent deciding
        # what to do about it, which is a different question.
        mcpd.log("warn", "order lookup failed, answering from context", {"error": str(exc)})
        mcpd.metric("support.tool_failures", 1, "counter", labels={"tool": "orders.lookup"})

    reply = ("I could not reach the order system just now. Your order A-7741 "
             "shipped on Tuesday and tracking should update within the hour.")
    run.set_output(reply)
    run.set_metadata({"used_fallback": order is None})
    return reply, run.trace_id

# 同期も非同期も同じオブジェクト。どちらも抜けるときにスパンを終了し、
# ブロックが例外を出していればそれを記録する。
with mcpd.trace("nightly_summariser") as run:
    with mcpd.span("fetch", kind=SpanKind.TOOL_CALL) as step:
        step.set_output({"documents": 42})

async with mcpd.trace("agent") as run:
    async with mcpd.span("generate", kind=SpanKind.MODEL_GENERATION) as generation:
        generation.set_output(await model.complete(prompt))
    await mcpd.aflush()

def draft(question: str) -> str:
    # 何も渡していない。それでもスパンは実行の下に入る。アクティブなスパンは
    # contextvars.ContextVar で、この呼び出しがブロックの中にあるから。
    with mcpd.span("draft", kind=SpanKind.MODEL_GENERATION) as generation:
        generation.set_input(question)
        return "..."


with mcpd.trace("support_agent") as run:
    run.set_output(draft("where is my order?"))

# 送信側: アクティブなスパンを次のサービスに渡す。
requests.post(url, headers={"traceparent": mcpd.traceparent()}, json=payload)

# 受信側: 新しいトレースを始めず、そのトレースを続ける。
with mcpd.trace("handler", traceparent=request.headers.get("traceparent")) as run:
    ...

generation.set_model("gpt-4o-mini", provider="openai")
generation.set_usage(input_tokens=812, output_tokens=96, cache_read=128)

# 自分で捕まえて、自分で扱いを決めた失敗。
generation.set_error("the provider timed out", kind="TimeoutError", retryable=True)

# スパンの中の一点。それ自体は長さを持たない。
generation.event("first_token")

from mcpd import Mcpd, RecordingSink

sink = RecordingSink()
mcpd = Mcpd(sink=sink)
with mcpd.trace("answer") as run:
    run.set_usage(input_tokens=12)
mcpd.flush()
assert sink.spans[0].usage.input_tokens == 12
print(mcpd.stats())  # queued, dropped, capacity, exported, failed, rejected

support = workflow("support_ticket")


@support.step("classify")
def classify(ctx, input, prior):
    return {"kind": "question" if input["ticket"].startswith("Q") else "bug"}


@support.agent("draft", input={"kind": "${steps.classify.output.kind}"})
def draft(ctx, input, prior):
    # Your own model call, recorded under this step in the run's trace. What
    # the span spends is the step's usage: the daemon prices it from here.
    with ctx.span("model_call", kind="model_generation") as span:
        span.set_model("claude-sonnet-4", "anthropic")
        span.set_usage(input_tokens=812, output_tokens=37)
        text = f"an answer to a {input['kind']}"
    return text


support.wait_for("approval", event="approved", key="${input.ticket}",
                 timeout_ms=7 * 24 * 3_600_000,
                 when=when("steps.classify.output.kind", "eq", "bug"))


@support.step("send", input={"text": "${steps.draft.output}"}, effect="at_most_once",
              retry=RetryPolicy(max_attempts=3, backoff="exponential", initial_ms=500))
def send(ctx, input, prior):
    if not deliver(input["text"], ctx.idempotency_key):
        raise StepFailure("mail relay busy", class_="retryable", retry_after_ms=2_000)
    return {"sent": True}


support.output({"sent": "${steps.send.output.sent}"})

# A machine evaluation. `min` and `max` are required: a bare number with no
# declared scale cannot be compared to anything.
mcpd.score(
    "answer_relevance",
    value=round(random.uniform(0.6, 0.95), 2),
    min=0.0,
    max=1.0,
    trace_id=trace_id,
    reason="named the order and gave a delivery window, without the live lookup",
)

# A human evaluation. Kept separate from a score, because a person's
# judgement and a model's must never be averaged together.
mcpd.feedback(
    FeedbackKind.THUMBS,
    "up",
    trace_id=trace_id,
    comment="fine given the outage",
    author_kind="end_user",
)

def handler(event, context):
    with mcpd.trace("answer") as run:
        run.set_input(event["body"])
        answer = agent(event["body"])
        run.set_output(answer)

    # この時点でバッチはまだメモリの中。ハンドラーが戻った瞬間にランタイムが
    # このプロセスを凍結し、ワーカースレッドは二度と動かない。
    mcpd.flush()
    return {"statusCode": 200, "body": answer}

# Nothing has been sent yet: recording appends to an in-memory queue and a
# background worker does the I/O. Flush before you read your own writes.
mcpd.flush()

detail = mcpd.get_trace(trace_id, include=["logs", "scores", "feedback"])
print(f"\n{detail.trace.name}  {detail.trace.duration_ns / 1e6:.0f} ms  "
      f"{detail.trace.span_count} spans  {detail.trace.error_count} errors")
for span in detail.spans:
    cost = f"  ${span.cost.total_cost:.6f}" if span.cost else ""
    print(f"  {span.kind.value:<18} {span.name:<14} {span.status.value}{cost}")

summary = mcpd.analytics_summary("-1h", "-0s")
print(f"\nlast hour  traces={summary.trace_count}  errors={summary.error_count}  "
      f"p95={summary.latency.p95:.0f}ms  cost=${summary.total_cost:.4f}")