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aigp-mcp

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AIGP governance adapter for MCP tool invocations — policy, scope, consent, evidence

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Direct dependencies1From release 1.0.0
Last observed21 Oct, 10:50 UTC

README

Version 1.0.0 documentation

aigp-mcp

AIGP governance for MCP (Model Context Protocol) tool invocations.

Part of MAIGP — the Mediated AI Governance Protocol: a runtime consent / scope / evidence layer for AI systems. Every governed call is CHECKed before it runs (policy, scope, jurisdiction, circuit-breaker) and RECORDed + TRACEd as tamper-evident evidence after.

What it governs

Governs MCP tool calls — policy, scope, consent, classification, and evidence.

Governance model

This adapter maps the framework's lifecycle onto the AIGP agent-governance loop provided by maigp-agent-core:

Framework eventAIGP action
run / invocation startCHECK (pre_invoke) — allowed? under what authority?
each model replytoken accounting (on_model_call)
each tool / function calltool governance (on_tool_call)
run completeRECORD + TRACE (post_invoke)
failureon_error (recorded, then re-raised)

Install

pip install aigp-mcp

Pulls in maigp-agent-core (shared lifecycle) and, transitively, maigp-client.

Quick start

from aigp_mcp import GovernedMCPClient, MCPGovernanceConfig

config = MCPGovernanceConfig(
    mode="ENFORCE",
    gov_url="https://gov.example.com",
    app_id="mcp-gateway",
    hmac_secret="...",
)
governed = GovernedMCPClient(mcp_client, config)

# Pre-check → execute only if allowed → post-record evidence
result = await governed.call_tool("wiz", "listVulnerabilities",
                                  {"severity": "HIGH"})

Modes & exports

mode="ENFORCE" blocks disallowed calls (raising GovernanceDenied); monitor-only modes record without blocking. Exports include AccessDecision, GovernanceDenied, and ToolEvidence.


License: Proprietary. © Kanjani AI Research & Causum.

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VersionPublishedDirect dependenciesOpen version
1.0.05 Oct 20261

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