@clgplatform/mcp
MCP ServerFreeOfficial CLG wrapper for Model Context Protocol: tamper-evident decision and outcome receipts and real-time mandate enforcement for MCP tool calls.
Capabilities6 decomposed
tamper-evident decision receipt generation for mcp tool calls
Medium confidenceGenerates cryptographically signed, immutable receipts for every MCP tool invocation that capture the complete decision context (model, prompt, parameters, output) and bind them to a specific mandate or governance policy. Uses a hash-chain or merkle-tree approach to create tamper-evident audit trails where any modification to prior decisions is cryptographically detectable, enabling compliance with EU AI Act transparency and accountability requirements.
Integrates cryptographic receipt generation directly into the MCP protocol layer, creating tamper-evident decision records at the point of tool invocation rather than as a post-hoc logging layer. This architectural choice ensures no decision can be made without generating a signed receipt, making governance enforcement mandatory rather than optional.
Unlike generic audit logging (which can be disabled or modified), CLG's receipt system makes governance enforcement a first-class MCP protocol concern with cryptographic proof of integrity, directly addressing EU AI Act transparency mandates that require immutable decision documentation.
real-time mandate enforcement for tool call authorization
Medium confidenceIntercepts MCP tool calls before execution and validates them against a set of governance mandates (policies, rules, constraints) in real-time, blocking or modifying calls that violate policy. Implements a policy evaluation engine that can enforce constraints like rate limits, tool whitelists/blacklists, parameter validation, and conditional access rules based on model state, user context, or decision history.
Embeds policy evaluation as a mandatory gate in the MCP tool invocation pipeline, enforcing mandates synchronously before tool execution rather than logging violations asynchronously. This ensures governance is enforced at the point of decision, not discovered after the fact.
Provides real-time, synchronous mandate enforcement integrated into MCP's native tool-calling mechanism, whereas generic policy engines typically operate as external audit layers that detect violations post-execution, making CLG's approach preventative rather than detective.
mcp protocol wrapper with governance metadata injection
Medium confidenceWraps standard MCP tool definitions and invocations to automatically inject governance metadata (mandate IDs, policy context, decision timestamps, audit identifiers) into the protocol layer. Extends MCP's native schema to carry governance context through the entire tool call lifecycle, enabling downstream systems to understand the governance context in which each decision was made without requiring separate metadata channels.
Operates at the MCP protocol layer itself, injecting governance metadata directly into tool definitions and invocations rather than as a separate metadata channel. This ensures governance context is native to the protocol and cannot be bypassed or ignored by downstream systems.
Unlike external governance layers that operate parallel to MCP, this wrapper makes governance a first-class concern in the protocol itself, ensuring all MCP implementations automatically carry governance context without requiring separate integration work.
eu ai act compliance documentation generation
Medium confidenceAutomatically generates compliance documentation artifacts (decision logs, impact assessments, audit reports) formatted to satisfy EU AI Act requirements for high-risk AI systems. Aggregates decision receipts, mandate enforcement records, and governance metadata into structured reports that demonstrate transparency, accountability, and human oversight requirements mandated by the regulation.
Generates EU AI Act-specific compliance documentation directly from the cryptographic decision receipts and mandate enforcement logs, ensuring regulatory reports are grounded in tamper-evident evidence rather than reconstructed from logs that could be modified.
Produces compliance documentation that is directly tied to cryptographically signed decision receipts, providing regulators with verifiable proof of governance enforcement, whereas generic audit logging systems produce reports that lack cryptographic integrity guarantees.
decision context preservation and retrieval for audit trails
Medium confidenceCaptures and stores the complete decision context for every tool call (model version, prompt, parameters, output, timestamp, user/system context) in a structured format that can be retrieved and analyzed for audit purposes. Implements a queryable audit store that allows filtering and searching decisions by mandate, tool, timestamp, or outcome, enabling post-hoc analysis of system behavior and decision patterns.
Preserves complete decision context (not just outcomes) in a queryable store, enabling post-hoc analysis and reconstruction of the reasoning that led to specific tool calls. This goes beyond simple logging by maintaining the full decision context needed for regulatory explanation.
Provides queryable, context-rich audit trails that preserve the complete decision reasoning, whereas generic logging systems typically only record outcomes, making it difficult to reconstruct why a specific decision was made.
model identity and context binding for tool calls
Medium confidenceBinds tool calls to specific model identifiers and execution context (user ID, request ID, session ID) so that decisions can be traced back to the specific model instance and user interaction that triggered them. Implements context propagation through the MCP call stack to ensure governance metadata is associated with the correct model and user.
Implements context binding at the MCP protocol level so that model identity and user context are automatically propagated through tool call chains without requiring explicit context passing at each step. Uses a context propagation pattern similar to distributed tracing systems.
More reliable than application-level context tracking because it's embedded in the MCP stack and cannot be bypassed, whereas application-level approaches depend on developers correctly passing context through their code.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓regulated enterprises building high-risk AI systems (financial services, healthcare, hiring)
- ✓compliance teams needing audit-ready decision logs for regulatory inspection
- ✓AI governance teams implementing accountability frameworks for LLM agents
- ✓teams deploying LLM agents in production with strict governance requirements
- ✓organizations needing to enforce compliance policies across multiple AI systems
- ✓developers building agentic systems where tool access must be dynamically controlled
- ✓teams integrating governance into existing MCP-based systems without rewriting tool code
- ✓organizations standardizing on MCP and needing governance as a protocol-level concern
Known Limitations
- ⚠Receipt generation adds latency per tool call (exact overhead unknown without benchmarks)
- ⚠Requires external storage or database for receipt persistence — no built-in state management
- ⚠Cryptographic verification requires access to signing keys and certificate chain
- ⚠No built-in integration with regulatory reporting systems — receipts must be manually exported or integrated
- ⚠Policy evaluation latency depends on mandate complexity — no published performance benchmarks
- ⚠Mandate definitions must be pre-configured; no dynamic policy generation from natural language
Requirements
Input / Output
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Official CLG wrapper for Model Context Protocol: tamper-evident decision and outcome receipts and real-time mandate enforcement for MCP tool calls.
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