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.

Gateway Proxy (recommended)

Route any MCP server through Observatory for automatic trust verification:

from dominion_observatory import gateway_url

# Instead of connecting directly:
#   url = "https://some-mcp-server.com/mcp"
# Route through Observatory:
url = gateway_url("https://some-mcp-server.com/mcp")
# Returns: "https://dominionobservatory.com/gateway/https%3A%2F%2Fsome-mcp-server.com%2Fmcp"

# Use this URL anywhere you'd use the original MCP server URL.
# Free trial: 50 calls/day per IP, no API key needed.

Works with Claude Desktop, LangChain, CrewAI, OpenAI Agents SDK, or any MCP client.

Usage

from dominion_observatory import report, check_trust, instrument, gateway_url

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

# 1. Route through gateway for trust-verified calls
trusted_url = gateway_url(SERVER_URL)

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

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

# 4. 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:

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

License

MIT