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Documentation Index

Fetch the complete documentation index at: https://docs.aevyra.ai/llms.txt

Use this file to discover all available pages before exploring further.

Interceptors wrap live objects and record each interaction as it happens. Unlike adapters (which parse log files after the fact), interceptors sit in the call path and capture spans in real time.

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MCP interceptor

MCP is the standard protocol for agent-tool connectivity. Every call_tool invocation has a clean input/output boundary — the interceptor captures each one as a KIND_TOOL span with no changes to your agent code.

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Basic usage

from mcp import ClientSession
from aevyra_witness.interceptors import wrap_mcp_session

async with ClientSession(read, write) as session:
    await session.initialize()

    # Wrap once — use exactly like the original session
    mcp = wrap_mcp_session(session, server_name="github")

    issue  = await mcp.call_tool("create_issue", {"title": "Bug", "body": "..."})
    repos  = await mcp.call_tool("list_repos", {})
    commit = await mcp.call_tool("get_commit", {"sha": "abc123"})

    # All three calls captured
    trace = mcp.to_trace()
    print(f"Captured {len(trace.nodes)} spans")

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Attach to a Witness tracer

Combine MCP spans with @span-instrumented reasoning steps into a single unified trace: 
from aevyra_witness.runtime import span, trace as witness_trace
from aevyra_witness.interceptors import wrap_mcp_session

@span("plan", optimize=True, prompt_id="planner_v1")
async def plan(context: str) -> str: ...

async def run_agent(question: str):
    with witness_trace() as tracer:
        async with ClientSession(read, write) as session:
            await session.initialize()
            # Attach tracer — MCP spans are injected automatically
            mcp = wrap_mcp_session(session, server_name="stripe", tracer=tracer)

            plan_result = await plan(question)
            charge = await mcp.call_tool("get_charge", {"id": "ch_123"})
            # ...

    # trace contains both @span and MCP spans in execution order
    return tracer.finish()

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Multiple servers

Wrap each server separately and merge the node lists: 
from aevyra_witness import AgentTrace

github = wrap_mcp_session(gh_session, server_name="github")
slack  = wrap_mcp_session(sl_session, server_name="slack")
stripe = wrap_mcp_session(st_session, server_name="stripe")

# ... run agent ...

trace = AgentTrace(nodes=github.nodes + slack.nodes + stripe.nodes)

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Set a parent span

Wire MCP tool spans as children of a reasoning span in a larger DAG: 
# After capturing the reasoning span's id
mcp = wrap_mcp_session(
    session,
    server_name="github",
    parent_id="plan_step_1",
)

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What each span contains

FieldValue
nameTool name (e.g. "create_issue")
kindKIND_TOOL
inputThe arguments dict passed to call_tool
outputExtracted text from CallToolResult.content
metadata["mcp_server"]server_name you provided
started_at / ended_atWall-clock timestamps
metadata["latency_ms"]Call duration
errorException message if the call raised

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Transparent proxy

The interceptor forwards every non-intercepted attribute to the underlying session — list_tools, read_resource, get_prompt, etc. all pass through unchanged. You can use it as a drop-in replacement: 
# These work exactly as before
tools    = await mcp.list_tools()
resource = await mcp.read_resource("file:///config.json")

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API reference

from aevyra_witness.interceptors import MCPInterceptor, wrap_mcp_session

# Factory (preferred)
mcp = wrap_mcp_session(
    session,
    server_name="github",   # str — shows up in metadata["mcp_server"]
    parent_id=None,         # str | None — parent span id for DAG wiring
    tracer=None,            # Tracer | None — injects spans into an existing tracer
)

# Access captured spans
mcp.nodes           # list[TraceNode] — all captured tool call spans
mcp.to_trace()      # AgentTrace — wraps nodes in a full trace object

# Still a full session proxy
await mcp.call_tool(name, arguments, **kwargs)
await mcp.list_tools()