dominion-observatory-sdk 0.3.0

Cross-ecosystem agent behavioral telemetry SDK — report and read trust scores from the Dominion Observatory. The only MCP scoring network that accepts agent-reported runtime data.

dominion-observatory-sdk (Python)

One-line agent behavioral telemetry for MCP servers, LangChain tools, AutoGen agents, and CrewAI workers. Feeds the Dominion Observatory, the only MCP scoring network that accepts agent-reported runtime data instead of static GitHub/registry signals.

Install

pip (recommended):

pip install dominion-observatory-sdk

Live on PyPI: https://pypi.org/project/dominion-observatory-sdk/

CDN alternative (single file, stdlib only, Python 3.9+):

curl -O https://sdk-cdn.sgdata.workers.dev/v1/observatory.py

Then drop observatory.py into your project and import from it directly. Every CDN fetch fires anonymized adoption telemetry back into the Observatory.

Usage

from observatory import report, check_trust, instrument

SERVER_URL = "https://my-mcp-server.example.com/mcp"

# 1. Fire-and-forget telemetry in a tool handler
report(
    server_url=SERVER_URL,
    success=True,
    latency_ms=142,
    tool_name="get_holidays",
)

# 2. Convenience wrapper: measures latency automatically
result = instrument(SERVER_URL, "get_holidays", lambda: do_work())

# 3. Read a trust score before delegating to another server
score = check_trust("https://some-other-mcp.example.com/mcp")
if score.found and (score.trust_score or 0) >= 70:
    call_that_server()

What gets sent

Exactly these five fields, and nothing else:

Field	Example
server_url	https://my-mcp-server.example.com/mcp
success	true
latency_ms	142
tool_name	get_holidays
http_status	200

Not sent: query arguments, tool output, user IDs, IP addresses, auth tokens, or any other payload. This satisfies Singapore PDPA and IMDA's agentic AI transparency requirements, and is compatible with EU AI Act Article 12 logging.

Why this exists

5+ MCP scoring platforms already exist (Glama, Smithery, MCP Scorecard, Nerq, Zarq), but all of them score servers from the outside using GitHub and registry metadata. None of them observe what happens when an agent actually calls a tool. The Dominion Observatory is the only network that collects agent-reported, cross-ecosystem runtime behavior — the only data that ages into compounding value because it can't be backfilled.

Every install of this SDK is a permanent telemetry pipeline.

LangChain integration (v0.2.0)

Drop-in BaseCallbackHandler that reads Observatory trust scores before every tool call and reports anonymized telemetry after. Complements ComplianceCallbackHandler-style audit trails (LangChain #35691) and AIP-style signed-intent kill switches (LangChain #36232) — those prove who acted; Observatory answers is the tool misbehaving right now?

from langchain.agents import AgentExecutor
from dominion_observatory.langchain import ObservatoryTrustCallbackHandler

handler = ObservatoryTrustCallbackHandler(
    tool_server_urls={
        "web_search":    "https://search.example.com/mcp",
        "transfer_funds":"https://payments.example.com/mcp",
    },
    min_trust_score=40.0,
    block_on_low_trust=True,
)

agent = AgentExecutor(..., callbacks=[handler])

Requires langchain-core>=0.3. Install separately; the base SDK has zero runtime dependencies.

Observatory API

Purpose	Endpoint
Report interaction	POST https://dominion-observatory.sgdata.workers.dev/mcp (MCP JSON-RPC)
Trust score	GET https://dominion-observatory.sgdata.workers.dev/api/trust?url=...
Global stats	GET https://dominion-observatory.sgdata.workers.dev/api/stats

License

MIT