What can @oconnector/mcp-gateway do?

mcp tool call interception and governance, non-repudiation signing for tool call decisions, policy-based tool call filtering and modification, multi-agent tool access control with role-based enforcement, audit logging with cryptographic proof of tool invocations, mcp server endpoint proxying with transparent request/response handling, integration with crewai and langchain agent frameworks, sovereign accountability and compliance reporting

@oconnector/mcp-gateway

MCP ServerFree

Security Proxy for Model Context Protocol — Govern any MCP tool call with ABS Core NRaaS (Non-Repudiation as a Service)

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp tool call interception and governance

Medium confidence

Intercepts all Model Context Protocol tool invocations at the gateway layer before execution, applying configurable governance policies to approve, deny, or modify tool calls based on security rules and compliance requirements. Uses a proxy architecture that sits between LLM agents (CrewAI, LangChain) and MCP servers, inspecting call signatures and payloads against policy definitions without requiring changes to upstream agent code.

Solves for

I need to prevent my AI agents from calling certain dangerous tools or APIs without explicit approvalI want to audit every tool call my agents make for compliance and security purposesI need to enforce rate limits or resource quotas on specific tool invocations across my agent fleetI want to inject additional validation or transformation logic into tool calls without modifying my agent code

Best for

Enterprise teams deploying AI agents in regulated industries (finance, healthcare, legal)

Teams building multi-agent systems where tool access control is critical

Organizations requiring audit trails and compliance evidence for AI tool usage

Requires

Node.js 16+ (typical for npm packages)

MCP-compatible agent framework (CrewAI, LangChain, or direct MCP client)

MCP server endpoints to proxy calls to

Limitations

Adds latency to every tool call (exact overhead depends on policy complexity and NRaaS signing operations)

Requires explicit policy configuration — no sensible defaults for tool restrictions

Does not prevent agents from attempting calls; only governs execution post-interception

What makes it unique

Implements MCP-specific governance as a transparent proxy layer with non-repudiation guarantees via ED25519 signatures, rather than relying on agent-level access control or LLM prompt-based restrictions. Integrates with ABS Core NRaaS to cryptographically bind tool call decisions to identifiable actors.

vs alternatives

Unlike prompt-based tool restrictions (easily bypassed) or agent-level ACLs (require code changes), this gateway approach provides cryptographically-auditable governance that applies uniformly across all agents and cannot be circumvented by prompt injection.

non-repudiation signing for tool call decisions

Medium confidence

Cryptographically signs all tool call governance decisions (approval, denial, modification) using ED25519 digital signatures, creating an immutable audit trail that proves who authorized or rejected each tool invocation and when. Each decision is bound to an actor identity and cannot be forged or altered retroactively, satisfying compliance requirements for accountability in regulated environments.

Solves for

I need to prove to auditors that a specific tool call was approved by an authorized person at a specific timeI want to create a tamper-proof record of all AI tool usage decisions for legal/compliance purposesI need to attribute tool call approvals to specific team members or roles for accountabilityI want to detect if governance decisions have been forged or modified after the fact

Best for

Regulated industries (finance, healthcare, legal) requiring audit trails for AI decisions

Organizations with strict compliance frameworks (HIPAA, SOX, GDPR, NIST)

Teams needing to demonstrate 'human-in-the-loop' approval for sensitive AI operations

Requires

ED25519 key pair (private key for signing, public key for verification)

Key management infrastructure (HSM, secure vault, or KMS integration)

Timestamp service or reliable system clock for decision timestamping

Limitations

Signature verification requires access to public keys — key management infrastructure must be maintained

ED25519 signatures add cryptographic overhead (~5-10ms per signature operation)

Non-repudiation only applies to gateway decisions; does not cover agent reasoning or LLM outputs

What makes it unique

Implements Non-Repudiation as a Service (NRaaS) specifically for MCP tool governance, using ED25519 signatures to bind decisions to actor identities with cryptographic certainty. This is distinct from generic audit logging because signatures are mathematically unforgeable and can be verified by external parties.

vs alternatives

Standard audit logs can be tampered with or deleted; ED25519 signatures create cryptographic proof that a decision was made by a specific actor at a specific time, satisfying compliance requirements that generic logging cannot meet.

policy-based tool call filtering and modification

Medium confidence

Evaluates incoming MCP tool calls against a configurable policy engine that can allow, block, or transform tool invocations based on rules matching tool name, arguments, caller identity, resource usage, or other contextual signals. Policies are evaluated before tool execution, enabling fine-grained control over what agents can do without requiring changes to agent code or LLM prompts.

Solves for

I want to block certain tools entirely (e.g., no file deletion, no external API calls to untrusted services)I need to enforce argument validation (e.g., only allow file operations within a specific directory)I want to rate-limit tool calls (e.g., max 10 API calls per minute per agent)I need to transform tool arguments before execution (e.g., sanitize file paths, add authentication headers)

Best for

Teams building multi-tenant AI systems where different users have different tool access levels

Organizations with strict security policies around tool usage (no external APIs, no file system access, etc.)

Developers needing to enforce resource quotas or rate limits on tool invocations

Requires

Policy definition format (JSON, YAML, or custom DSL)

Policy evaluation engine (built-in or external, e.g., OPA, Rego)

Tool metadata (name, arguments, expected behavior) for policy matching

Limitations

Policy evaluation adds latency proportional to rule complexity (simple rules ~1-5ms, complex rules ~10-50ms)

No built-in policy language — requires custom implementation or integration with external policy engine

Policies must be maintained separately from agent code, creating potential for drift or inconsistency

What makes it unique

Provides MCP-specific policy evaluation at the gateway layer, allowing rules to match on MCP-specific metadata (tool name, schema, arguments) rather than generic HTTP/API patterns. Integrates with ABS Core for policy storage and evaluation, enabling centralized governance across multiple agents.

vs alternatives

Unlike agent-level tool restrictions (which require code changes) or LLM prompt-based controls (which are easily bypassed), gateway-level policy enforcement applies uniformly and cannot be circumvented by prompt injection or agent code modification.

multi-agent tool access control with role-based enforcement

Medium confidence

Manages tool access permissions across multiple AI agents based on actor identity, role, or team membership, ensuring that different agents or users can only invoke tools they are authorized to use. Uses identity context from the MCP request to evaluate role-based access control (RBAC) policies, enabling fine-grained delegation of tool access without modifying individual agent configurations.

Solves for

I want different teams to have access to different sets of tools (e.g., finance team can call payment APIs, HR team cannot)I need to restrict certain sensitive tools to specific users or service accountsI want to implement least-privilege access where agents only get tools they need for their specific taskI need to audit which agent or user called which tool for compliance purposes

Best for

Enterprise organizations with multiple teams using shared AI infrastructure

Multi-tenant SaaS platforms where different customers need different tool access

Organizations with strict role-based access control requirements (RBAC)

Requires

Actor identity information in MCP requests (user ID, service account, API key, JWT token)

Role/permission definitions (stored in policy database, identity provider, or configuration file)

Identity verification mechanism (JWT validation, API key lookup, etc.)

Limitations

Requires reliable actor identity propagation through the MCP request chain — identity spoofing would bypass controls

Role definitions must be maintained in a separate system (identity provider, policy database) — creates operational overhead

No built-in integration with standard identity providers (OIDC, SAML, LDAP) — requires custom implementation

What makes it unique

Implements role-based access control at the MCP gateway layer, allowing fine-grained tool access decisions based on actor identity without requiring changes to individual agent code. Integrates with ABS Core identity management to support centralized role definitions across multiple agents and teams.

vs alternatives

Unlike agent-level tool restrictions (which require per-agent configuration) or LLM-based access control (which is not cryptographically enforceable), gateway-level RBAC provides centralized, auditable, and tamper-proof tool access control.

audit logging with cryptographic proof of tool invocations

Medium confidence

Records comprehensive audit logs of all tool call governance decisions, including tool name, arguments, actor identity, decision (allow/deny/modify), timestamp, and ED25519 signature proving the decision was made by an authorized entity. Logs are structured for compliance reporting and can be exported for external audit or forensic analysis.

Solves for

I need to generate audit reports showing which tools were called, by whom, and when, for compliance purposesI want to investigate a security incident by reviewing the complete history of tool calls made by a specific agent or userI need to prove to auditors that sensitive tool calls were properly authorized and loggedI want to detect unusual patterns in tool usage (e.g., an agent suddenly calling tools it normally doesn't use)

Best for

Regulated industries (finance, healthcare, legal) requiring comprehensive audit trails

Organizations with strict compliance requirements (HIPAA, SOX, GDPR, NIST)

Security teams needing to investigate AI-related incidents or anomalies

Requires

Log storage backend (file system, database, cloud logging service)

ED25519 public keys for signature verification

Timestamp service or reliable system clock

Limitations

Audit logs can grow very large with high-volume tool usage — requires log storage and retention strategy

Signature verification requires access to public keys — key management infrastructure must be maintained

Logs are only as trustworthy as the system that generates them — compromised gateway could produce false logs

What makes it unique

Combines comprehensive audit logging with ED25519 cryptographic signatures, creating tamper-proof records of tool call governance decisions that satisfy compliance requirements. Each log entry is cryptographically bound to the decision maker and timestamp, making it impossible to forge or alter logs retroactively.

vs alternatives

Standard audit logs can be tampered with or deleted; cryptographically-signed audit logs provide mathematical proof that a record was created by an authorized entity at a specific time, satisfying compliance requirements that generic logging cannot meet.

mcp server endpoint proxying with transparent request/response handling

Medium confidence

Acts as a transparent proxy between LLM agents and MCP servers, intercepting all MCP protocol messages (JSON-RPC format), applying governance policies, and forwarding approved calls to the actual MCP server endpoints. Handles request/response transformation, error handling, and timeout management without requiring agents to be aware of the proxy layer.

Solves for

I want to add governance to my existing MCP servers without modifying the servers themselvesI need to route tool calls to different MCP server endpoints based on governance policiesI want to add request/response logging and transformation without changing agent codeI need to handle MCP server failures gracefully while maintaining governance audit trails

Best for

Teams with existing MCP server infrastructure who want to add governance without refactoring

Organizations needing to support multiple MCP server endpoints with unified governance

Developers building agent platforms that need to enforce governance across all tool calls

Requires

MCP server endpoints to proxy to (HTTP, WebSocket, or other MCP transport)

Network connectivity between gateway and MCP servers

Agent configuration pointing to gateway instead of MCP servers

Limitations

Adds latency to every tool call (proxy overhead + policy evaluation + signature generation)

Requires agents to be configured to point to the gateway instead of MCP servers — may require agent code changes

Does not provide end-to-end encryption between agent and MCP server — traffic is visible to the gateway

What makes it unique

Implements MCP-specific proxying that understands the MCP protocol (JSON-RPC, tool schemas, context protocol) rather than generic HTTP proxying, enabling governance decisions based on MCP-specific metadata like tool name, schema, and arguments.

vs alternatives

Unlike generic HTTP proxies (which cannot understand MCP semantics) or agent-level tool wrappers (which require code changes), MCP gateway proxying provides transparent governance that works with any MCP-compatible agent without modification.

integration with crewai and langchain agent frameworks

Medium confidence

Provides native integration points with CrewAI and LangChain agent frameworks, allowing these frameworks to route tool calls through the MCP gateway for governance without requiring custom code. Handles framework-specific tool registration, context passing, and response handling to ensure seamless integration with existing agent code.

Solves for

I'm using CrewAI or LangChain and want to add governance to my agents without rewriting themI need to ensure all tool calls from my CrewAI/LangChain agents go through the governance gatewayI want to use the same governance policies across multiple agent frameworksI need to maintain compatibility with existing CrewAI/LangChain code while adding governance

Best for

Teams using CrewAI or LangChain who want to add governance to existing agents

Organizations standardizing on CrewAI/LangChain and needing governance across the platform

Developers building agent platforms that need to support multiple frameworks with unified governance

Requires

CrewAI 0.x or LangChain 0.x (specific versions depend on integration)

Python 3.9+ (typical for CrewAI/LangChain)

MCP gateway running and accessible

Limitations

Integration is framework-specific — requires separate implementation for each framework version

Framework updates may break integration — requires maintenance and testing

Does not provide governance for framework-internal operations (LLM calls, reasoning) — only tool calls

What makes it unique

Provides native integration with CrewAI and LangChain rather than requiring agents to manually route calls through the gateway, enabling governance to be added with minimal code changes. Handles framework-specific tool registration and context passing transparently.

vs alternatives

Unlike generic MCP client libraries (which require agents to manually route calls), framework-specific integration allows governance to be added as a transparent layer that works with existing agent code.

sovereign accountability and compliance reporting

Medium confidence

Generates compliance reports and audit evidence demonstrating that AI tool usage meets regulatory requirements (NIST, HIPAA, SOX, GDPR, etc.) by providing cryptographically-signed records of tool call governance decisions, actor accountability, and policy enforcement. Reports can be exported for external audit or regulatory review.

Solves for

I need to demonstrate to regulators that my AI agents are operating under proper governance and controlsI want to generate audit reports showing compliance with NIST, HIPAA, SOX, or GDPR requirementsI need to prove that sensitive tool calls were authorized by appropriate personnelI want to show auditors that my AI system has proper accountability and non-repudiation controls

Best for

Regulated industries (finance, healthcare, legal) subject to compliance audits

Organizations building sovereign AI systems with accountability requirements

Enterprises needing to demonstrate compliance with NIST, HIPAA, SOX, GDPR, or similar frameworks

Requires

Complete audit logs with cryptographic signatures

Policy definitions and enforcement records

Actor identity and role information

Limitations

Reports are only as trustworthy as the underlying audit logs — compromised gateway could produce false reports

Compliance requirements vary by jurisdiction and industry — reports may need customization

No built-in integration with specific compliance frameworks — requires manual mapping of controls to requirements

What makes it unique

Combines cryptographically-signed audit logs with compliance reporting to provide auditors with mathematical proof that AI tool usage meets regulatory requirements. Uses ED25519 signatures to create tamper-proof evidence of governance and accountability.

vs alternatives

Unlike generic audit logs (which can be tampered with) or self-attestations (which lack proof), cryptographically-signed compliance reports provide mathematical evidence that governance controls are in place and functioning correctly.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @oconnector/mcp-gateway, ranked by overlap. Discovered automatically through the match graph.

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cordon-cli

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mcp tool-call interception and policy enforcementcontext-aware tool call filtering based on agent/user identity

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Policy-as-code enforcement for MCP tool calls

policy-driven mcp tool call interceptionmcp proxy middleware with transparent tool call routing

2 shared capabilities

MCP Server23

tegata

Enforceable authorization for MCP tool calls

mcp tool call authorization enforcementmcp middleware integration and transparent tool call interception

2 shared capabilities

MCP Server28

@mcptoolgate/client

MCP Tool Gate client for Claude Desktop - secure MCP tool governance with human-in-the-loop approvals

mcp tool call interception and context enrichmenthuman-in-the-loop mcp tool approval gateway

2 shared capabilities

Best For

✓Enterprise teams deploying AI agents in regulated industries (finance, healthcare, legal)
✓Teams building multi-agent systems where tool access control is critical
✓Organizations requiring audit trails and compliance evidence for AI tool usage
✓Regulated industries (finance, healthcare, legal) requiring audit trails for AI decisions
✓Organizations with strict compliance frameworks (HIPAA, SOX, GDPR, NIST)
✓Teams needing to demonstrate 'human-in-the-loop' approval for sensitive AI operations
✓Enterprises building sovereign AI systems with accountability requirements
✓Teams building multi-tenant AI systems where different users have different tool access levels

Known Limitations

⚠Adds latency to every tool call (exact overhead depends on policy complexity and NRaaS signing operations)
⚠Requires explicit policy configuration — no sensible defaults for tool restrictions
⚠Does not prevent agents from attempting calls; only governs execution post-interception
⚠Policy evaluation logic must be maintained separately from agent code, creating potential drift
⚠Signature verification requires access to public keys — key management infrastructure must be maintained
⚠ED25519 signatures add cryptographic overhead (~5-10ms per signature operation)

Requirements

Node.js 16+ (typical for npm packages)MCP-compatible agent framework (CrewAI, LangChain, or direct MCP client)MCP server endpoints to proxy calls toPolicy configuration file or API to define governance rulesED25519 key pair (private key for signing, public key for verification)Key management infrastructure (HSM, secure vault, or KMS integration)Timestamp service or reliable system clock for decision timestampingSignature verification logic in audit/compliance systems

Input / Output

Accepts: MCP tool call requests (JSON-RPC format with tool name, arguments, context), Policy definitions (likely JSON or YAML format), Agent context and metadata, Tool call governance decisions (approval/denial/modification), Actor identity (user ID, role, service account), Timestamp and context metadata, MCP tool call requests (tool name, arguments, caller context), Policy rules (conditions and actions), Actor identity and role information, MCP tool call requests with actor identity context, Role definitions and permission mappings, Tool metadata and access requirements, Tool call governance decisions with metadata, Actor identity and context, Tool call details (name, arguments, result), MCP JSON-RPC requests from agents (tool calls with arguments), MCP server responses (results or errors), CrewAI/LangChain tool definitions and schemas, Agent tool call requests, Framework context and metadata, Audit logs with governance decisions and signatures, Policy definitions and enforcement records, Compliance framework requirements

Produces: Approved/denied/modified MCP tool call responses, Audit logs with call metadata and decision rationale, Non-repudiation signatures (ED25519-based), ED25519 digital signature (base64 or hex encoded), Signed decision record (JSON with signature, timestamp, actor, decision), Audit log entries with cryptographic proof, Decision (allow/deny/modify), Modified tool call (if transformation applied), Denial reason or error message (if blocked), Audit log entry with policy evaluation details, Access decision (allow/deny), Audit log entry with actor identity and tool access decision, Error message if access denied, Structured audit log entries (JSON or similar format), Audit reports (CSV, PDF, or other formats for compliance), Cryptographically-signed log entries, Forwarded MCP JSON-RPC requests to servers, MCP server responses back to agents, Governance decisions and audit logs, Tool call results from MCP gateway, Framework-compatible responses, Compliance reports (PDF, CSV, or other formats), Audit evidence (cryptographically-signed records), Control attestations (proof that specific controls are in place), Gap analysis (areas where compliance is lacking)

UnfragileRank

Adoption5%(25% weight)

Quality31%(25% weight)

Ecosystem50%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit @oconnector/mcp-gateway→

Package Details

npm

Registry

4.3.22

Version

Weekly Downloads

About

Security Proxy for Model Context Protocol — Govern any MCP tool call with ABS Core NRaaS (Non-Repudiation as a Service)

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Capabilities8 decomposed

mcp tool call interception and governance

Medium confidence

Solves for

Best for

Enterprise teams deploying AI agents in regulated industries (finance, healthcare, legal)

Teams building multi-agent systems where tool access control is critical

Organizations requiring audit trails and compliance evidence for AI tool usage

Requires

Node.js 16+ (typical for npm packages)

MCP-compatible agent framework (CrewAI, LangChain, or direct MCP client)

MCP server endpoints to proxy calls to

Limitations

Adds latency to every tool call (exact overhead depends on policy complexity and NRaaS signing operations)

Requires explicit policy configuration — no sensible defaults for tool restrictions

Does not prevent agents from attempting calls; only governs execution post-interception

What makes it unique

vs alternatives

non-repudiation signing for tool call decisions

Medium confidence

Solves for

Best for

Regulated industries (finance, healthcare, legal) requiring audit trails for AI decisions

Organizations with strict compliance frameworks (HIPAA, SOX, GDPR, NIST)

Teams needing to demonstrate 'human-in-the-loop' approval for sensitive AI operations

Requires

ED25519 key pair (private key for signing, public key for verification)

Key management infrastructure (HSM, secure vault, or KMS integration)

Timestamp service or reliable system clock for decision timestamping

Limitations

Signature verification requires access to public keys — key management infrastructure must be maintained

ED25519 signatures add cryptographic overhead (~5-10ms per signature operation)

Non-repudiation only applies to gateway decisions; does not cover agent reasoning or LLM outputs

What makes it unique

vs alternatives

policy-based tool call filtering and modification

Medium confidence

Solves for

Best for

Teams building multi-tenant AI systems where different users have different tool access levels

Organizations with strict security policies around tool usage (no external APIs, no file system access, etc.)

Developers needing to enforce resource quotas or rate limits on tool invocations

Requires

Policy definition format (JSON, YAML, or custom DSL)

Policy evaluation engine (built-in or external, e.g., OPA, Rego)

Tool metadata (name, arguments, expected behavior) for policy matching

Limitations

Policy evaluation adds latency proportional to rule complexity (simple rules ~1-5ms, complex rules ~10-50ms)

No built-in policy language — requires custom implementation or integration with external policy engine

Policies must be maintained separately from agent code, creating potential for drift or inconsistency

What makes it unique

vs alternatives

multi-agent tool access control with role-based enforcement

Medium confidence

Solves for

Best for

Enterprise organizations with multiple teams using shared AI infrastructure

Multi-tenant SaaS platforms where different customers need different tool access

Organizations with strict role-based access control requirements (RBAC)

Requires

Actor identity information in MCP requests (user ID, service account, API key, JWT token)

Role/permission definitions (stored in policy database, identity provider, or configuration file)

Identity verification mechanism (JWT validation, API key lookup, etc.)

Limitations

Requires reliable actor identity propagation through the MCP request chain — identity spoofing would bypass controls

Role definitions must be maintained in a separate system (identity provider, policy database) — creates operational overhead

No built-in integration with standard identity providers (OIDC, SAML, LDAP) — requires custom implementation

What makes it unique

vs alternatives

audit logging with cryptographic proof of tool invocations

Medium confidence

Solves for

Best for

Regulated industries (finance, healthcare, legal) requiring comprehensive audit trails

Organizations with strict compliance requirements (HIPAA, SOX, GDPR, NIST)

Security teams needing to investigate AI-related incidents or anomalies

Requires

Log storage backend (file system, database, cloud logging service)

ED25519 public keys for signature verification

Timestamp service or reliable system clock

Limitations

Audit logs can grow very large with high-volume tool usage — requires log storage and retention strategy

Signature verification requires access to public keys — key management infrastructure must be maintained

Logs are only as trustworthy as the system that generates them — compromised gateway could produce false logs

What makes it unique

vs alternatives

mcp server endpoint proxying with transparent request/response handling

Medium confidence

Solves for

Best for

Teams with existing MCP server infrastructure who want to add governance without refactoring

Organizations needing to support multiple MCP server endpoints with unified governance

Developers building agent platforms that need to enforce governance across all tool calls

Requires

MCP server endpoints to proxy to (HTTP, WebSocket, or other MCP transport)

Network connectivity between gateway and MCP servers

Agent configuration pointing to gateway instead of MCP servers

Limitations

Adds latency to every tool call (proxy overhead + policy evaluation + signature generation)

Requires agents to be configured to point to the gateway instead of MCP servers — may require agent code changes

Does not provide end-to-end encryption between agent and MCP server — traffic is visible to the gateway

What makes it unique

vs alternatives

integration with crewai and langchain agent frameworks

Medium confidence

Solves for

Best for

Teams using CrewAI or LangChain who want to add governance to existing agents

Organizations standardizing on CrewAI/LangChain and needing governance across the platform

Developers building agent platforms that need to support multiple frameworks with unified governance

Requires

CrewAI 0.x or LangChain 0.x (specific versions depend on integration)

Python 3.9+ (typical for CrewAI/LangChain)

MCP gateway running and accessible

Limitations

Integration is framework-specific — requires separate implementation for each framework version

Framework updates may break integration — requires maintenance and testing

Does not provide governance for framework-internal operations (LLM calls, reasoning) — only tool calls

What makes it unique

vs alternatives

sovereign accountability and compliance reporting

Medium confidence

Solves for

Best for

Regulated industries (finance, healthcare, legal) subject to compliance audits

Organizations building sovereign AI systems with accountability requirements

Enterprises needing to demonstrate compliance with NIST, HIPAA, SOX, GDPR, or similar frameworks

Requires

Complete audit logs with cryptographic signatures

Policy definitions and enforcement records

Actor identity and role information

Limitations

Reports are only as trustworthy as the underlying audit logs — compromised gateway could produce false reports

Compliance requirements vary by jurisdiction and industry — reports may need customization

No built-in integration with specific compliance frameworks — requires manual mapping of controls to requirements

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@oconnector/mcp-gateway

Capabilities8 decomposed

mcp tool call interception and governance

non-repudiation signing for tool call decisions

policy-based tool call filtering and modification

multi-agent tool access control with role-based enforcement

audit logging with cryptographic proof of tool invocations

mcp server endpoint proxying with transparent request/response handling

integration with crewai and langchain agent frameworks

sovereign accountability and compliance reporting

Related Artifactssharing capabilities

mcp-runtime-guard

cordon-cli

@getcordon/core

@policylayer/intercept

tegata

@mcptoolgate/client

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @oconnector/mcp-gateway

Are you the builder of @oconnector/mcp-gateway?

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Data Sources

@oconnector/mcp-gateway

Capabilities8 decomposed

mcp tool call interception and governance

non-repudiation signing for tool call decisions

policy-based tool call filtering and modification

multi-agent tool access control with role-based enforcement

audit logging with cryptographic proof of tool invocations

mcp server endpoint proxying with transparent request/response handling

integration with crewai and langchain agent frameworks

sovereign accountability and compliance reporting

Related Artifactssharing capabilities

mcp-runtime-guard

cordon-cli

@getcordon/core

@policylayer/intercept

tegata

@mcptoolgate/client

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @oconnector/mcp-gateway

Are you the builder of @oconnector/mcp-gateway?

Get the weekly brief

Data Sources