dominion-observatory-llamaindex 0.1.0

LlamaIndex integration for the Dominion Observatory — instrument llama_index.tools.mcp.McpToolSpec tool calls with anonymised runtime telemetry to the cross-ecosystem MCP trust layer.

dominion-observatory-llamaindex

LlamaIndex integration for the Dominion Observatory — the behavioural trust layer for the AI agent economy. Instrument llama_index.tools.mcp.McpToolSpec so every MCP tool call your LlamaIndex agent makes emits a single six-field runtime telemetry report.

Nothing sensitive ever leaves your process: only agent_id, server_url, success, latency_ms, tool_name, and http_status. No prompts, no tool arguments, no tool outputs, no user IDs, no network metadata. Singapore PDPA compliant, EU AI Act Article 12 compatible.

Why

The Observatory already watches 4,500+ MCP servers via direct probes + user reports. What it cannot see on its own is what real LlamaIndex agents experience against those servers in production — which tools actually work for which agents at what latency, and which servers quietly fail under real traffic. This adapter closes that gap.

Install

pip install 'dominion-observatory-llamaindex[llamaindex]'

The [llamaindex] extra pulls in llama-index-tools-mcp>=0.4.0. The package itself only hard-depends on dominion-observatory-sdk>=0.2.0, so it is safe to install first in layered Dockerfiles.

Usage — drop-in replacement

Swap McpToolSpec for ObservatoryMcpToolSpec:

from llama_index.tools.mcp import BasicMCPClient
from dominion_observatory_llamaindex import ObservatoryMcpToolSpec

client = BasicMCPClient("https://my-mcp-server.example.com/mcp")

spec = ObservatoryMcpToolSpec(
    client=client,
    agent_id="acme-scheduler@1.2.0",
    # server_url is inferred from client.command_or_url when omitted.
)

tools = await spec.to_tool_list_async()
# Hand `tools` to any LlamaIndex agent (ReActAgent, FunctionAgent, Workflow, etc.).

Every tool returned by to_tool_list_async() will emit one Observatory report per call_tool invocation, whether the call succeeds, fails at the MCP level (CallToolResult.isError=True), or raises an exception.

Usage — wrap an existing spec

If a user already has an McpToolSpec somewhere downstream, instrument_tool_spec clones its config into an instrumented replacement without touching the original:

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from dominion_observatory_llamaindex import instrument_tool_spec

client = BasicMCPClient("https://my-mcp-server.example.com/mcp")
spec = McpToolSpec(client=client, allowed_tools=["query"])
spec = instrument_tool_spec(
    spec,
    agent_id="acme-scheduler@1.2.0",
)

tools = await spec.to_tool_list_async()

agent_id conventions

• Required. Must be a non-empty string.
• The strings anonymous and observatory_probe are reserved for Observatory internals and are rejected at construction time.
• Recommended shape: <app>-<role>@<semver>, e.g. acme-scheduler@1.2.0. That way the Observatory can distinguish deploys when you bump versions.

server_url resolution

• If passed explicitly, that value wins.
• Otherwise the wrapper reads client.command_or_url (the field BasicMCPClient uses), then client.url, then client.server_url.
• If none of those produce a non-empty string, telemetry is disabled for the spec — tool calls still execute normally. This is the safe default for stdio MCP servers that have no URL identity.

What is reported

Exactly six fields per invocation, nothing else:

field	type	value
agent_id	str	stable app identifier you passed to the constructor
server_url	str	MCP server URL (explicit or derived from the client)
success	bool	True if the call returned and isError was falsy
latency_ms	int	wall-clock elapsed around client.call_tool(...)
tool_name	str	the MCP tool name dispatched
http_status	int	200 on success, 500 on error (MCP-level or exception)

Failure policy

• An exception raised by call_tool is caught long enough to emit a success=False, http_status=500 report, then re-raised unchanged. Your agent code sees the original traceback.
• A report emission failure (network error, SDK import error, SDK bug) is suppressed at DEBUG level. Telemetry never breaks the agent's critical path.
• If the Observatory SDK is absent at runtime, the wrapper still runs and logs a one-line DEBUG message per dropped report.

Privacy posture

• No tool arguments are sent.
• No tool outputs are sent.
• No user IDs, chat history, prompts, or embeddings are sent.
• agent_id is opaque to the Observatory — pick any stable identifier that does not personally identify an end-user. Recommended: application build ID.

Part of the Dominion Agent Economy Empire

This adapter is part of a family:

Package	Framework	PyPI
dominion-observatory-sdk	framework-free	https://pypi.org/project/dominion-observatory-sdk/
dominion-observatory-langchain	LangChain	https://pypi.org/project/dominion-observatory-langchain/
dominion-observatory-crewai	CrewAI	https://pypi.org/project/dominion-observatory-crewai/
dominion-observatory-autogen	Microsoft AutoGen	https://pypi.org/project/dominion-observatory-autogen/
dominion-observatory-llamaindex	LlamaIndex	this package

Licence

MIT. See LICENSE.