What can cordon-cli do?

mcp tool-call interception and policy enforcement, human-in-the-loop approval workflow for tool calls, comprehensive audit logging and call tracing, dynamic policy configuration and hot-reload, parameter sanitization and constraint enforcement, tool-call rate limiting and quota enforcement, tool-call result inspection and output filtering, agent identity and authentication verification, tool-call dependency tracking and circular-call prevention

cordon-cli

MCP ServerFree

The security gateway for AI agents — firewall, auditor, and remote control for MCP tool calls

Open Source

/ 100

9 capabilities

Capabilities9 decomposed

mcp tool-call interception and policy enforcement

Medium confidence

Intercepts outbound tool calls from MCP clients before execution, evaluates them against declarative security policies (allowlists, denylists, parameter constraints), and blocks or permits execution based on policy rules. Operates as a proxy layer between the AI agent and MCP servers, inspecting call signatures, arguments, and metadata without modifying the MCP protocol itself.

Solves for

I need to prevent my AI agent from calling dangerous tools like file deletion or external API calls without approvalI want to enforce parameter constraints on tool calls — e.g., only allow file operations within a specific directoryI need to whitelist which tools an AI agent can access based on its role or the task it's performing

Best for

teams deploying AI agents in production environments with security-sensitive operations

enterprises requiring tool-call governance and compliance auditing

developers building multi-tenant AI systems where different agents need different tool access

Requires

Node.js 16+

MCP server compatible with stdio or SSE transport

Policy file in JSON or YAML format

Limitations

Policy evaluation adds latency to each tool call — synchronous blocking required before execution

No built-in machine learning-based anomaly detection — relies on static policy rules only

Policies must be manually authored; no automatic policy generation from usage patterns

What makes it unique

Operates as a transparent MCP proxy that enforces policies at the protocol level without requiring changes to client or server code; uses declarative policy syntax that maps directly to MCP tool schemas for precise parameter-level control

vs alternatives

More granular than generic API gateways because it understands MCP tool semantics; simpler to deploy than building custom security middleware into each agent application

human-in-the-loop approval workflow for tool calls

Medium confidence

Routes flagged or high-risk tool calls to a human reviewer for explicit approval before execution, with configurable risk scoring and escalation rules. Implements a queue-based approval system where pending calls are held until a human reviews and approves/rejects them, with timeout and fallback policies for unreviewed requests.

Solves for

I want to require manual approval for sensitive operations like database writes or external API callsI need to audit what my AI agent is trying to do before it actually does itI want to implement a 'break glass' workflow where certain tool calls always require human sign-off

Best for

regulated industries (finance, healthcare) where AI actions must be human-auditable

teams running autonomous agents that need oversight without full manual control

organizations building AI systems for non-technical stakeholders who need visibility

Requires

Node.js 16+

HTTP server or webhook endpoint for approval UI/API

Persistent queue storage (in-memory, Redis, or database)

Limitations

Introduces blocking latency — tool execution is delayed until human approval, potentially minutes or hours

Requires operational overhead to staff approval queues and handle SLA expectations

No built-in escalation to multiple reviewers or consensus-based approval

What makes it unique

Integrates approval workflow directly into the MCP call path rather than as a separate audit system; uses configurable risk scoring to determine which calls require approval, reducing approval fatigue for low-risk operations

vs alternatives

More integrated than post-hoc audit logging because it blocks execution until approval; lighter-weight than full workflow orchestration platforms because it's purpose-built for MCP tool calls

comprehensive audit logging and call tracing

Medium confidence

Records all tool-call attempts (approved, denied, executed, failed) with full context including caller identity, tool name, arguments, decision rationale, execution result, and timestamps. Logs are structured and queryable, supporting export to SIEM systems, compliance databases, or audit dashboards for forensic analysis and compliance reporting.

Solves for

I need to maintain a complete audit trail of what my AI agent attempted to do for compliance and debuggingI want to investigate security incidents by replaying the sequence of tool calls that led to a problemI need to generate compliance reports showing that sensitive operations were properly authorized and logged

Best for

regulated organizations requiring SOC 2, HIPAA, or PCI-DSS compliance

security teams investigating AI agent behavior anomalies

teams building multi-tenant systems needing per-tenant audit isolation

Requires

Node.js 16+

persistent storage backend (file system, database, or log aggregation service)

sufficient disk space for log retention policy

Limitations

Audit log volume can be high for agents making frequent tool calls — requires storage and indexing strategy

Logs contain sensitive data (tool arguments, results) — requires encryption and access controls

No built-in log retention policies or automatic archival — requires external log management

What makes it unique

Captures audit context at the MCP protocol level, recording both policy decisions and execution outcomes in a unified log; supports structured logging with queryable fields rather than unstructured text logs

vs alternatives

More complete than application-level logging because it captures all tool calls regardless of agent implementation; more compliance-ready than generic audit logs because it understands MCP semantics and tool call context

dynamic policy configuration and hot-reload

Medium confidence

Allows security policies to be updated without restarting the gateway or interrupting active agent operations. Policies are loaded from configuration files or APIs, validated against a schema, and applied to new tool calls immediately upon update. Supports versioning and rollback of policy changes.

Solves for

I need to quickly block a newly discovered dangerous tool without restarting my agent infrastructureI want to adjust tool-call policies based on real-time threat intelligence or incident responseI need to test policy changes in a staging environment before rolling them out to production

Best for

security teams responding to incidents and needing rapid policy updates

organizations with frequent policy changes due to regulatory updates or threat landscape shifts

teams running 24/7 agent systems where restarts are costly

Requires

Node.js 16+

file system access or API endpoint for policy updates

policy schema definition (JSON Schema or similar)

Limitations

Hot-reload introduces race conditions if policies are updated while tool calls are in flight

No built-in policy versioning or audit trail of policy changes — requires external tracking

Policy validation is synchronous — invalid policies can block all tool calls until corrected

What makes it unique

Implements zero-downtime policy updates by loading new policies in parallel and switching atomically, rather than requiring gateway restart; includes policy validation before activation to prevent invalid policies from blocking all calls

vs alternatives

Faster incident response than alternatives requiring restart or redeployment; safer than manual policy editing because validation prevents invalid policies from being activated

parameter sanitization and constraint enforcement

Medium confidence

Inspects tool-call arguments against declared constraints (type, length, regex patterns, value ranges, allowed values) and either rejects calls that violate constraints or sanitizes arguments to safe values. Supports custom sanitization functions for domain-specific validation (e.g., path traversal prevention, SQL injection detection).

Solves for

I want to prevent path traversal attacks by ensuring file operations only access whitelisted directoriesI need to enforce maximum request sizes to prevent DoS attacks via oversized tool argumentsI want to validate that API keys or credentials passed to tools match expected formats

Best for

teams building agents that interact with file systems or databases

security-conscious teams preventing injection attacks via tool arguments

organizations with strict input validation requirements

Requires

Node.js 16+

tool schema definitions with constraint metadata

custom sanitization functions (optional, for domain-specific validation)

Limitations

Constraint definitions must be manually authored for each tool — no automatic constraint inference

Custom sanitization functions can introduce security bugs if not carefully reviewed

Sanitization can silently modify arguments, potentially changing agent intent

What makes it unique

Operates at the MCP argument level with awareness of tool schemas, enabling type-aware validation and sanitization; supports both declarative constraints (JSON Schema) and imperative custom validators for complex rules

vs alternatives

More precise than generic input validation because it understands tool semantics; more flexible than hardcoded validation because constraints are declarative and reusable across tools

tool-call rate limiting and quota enforcement

Medium confidence

Enforces per-agent, per-tool, or global rate limits on tool-call frequency, preventing resource exhaustion and abuse. Supports multiple rate-limiting strategies (token bucket, sliding window, quota-based) with configurable time windows and burst allowances. Tracks usage across distributed agents via shared state.

Solves for

I want to prevent my agent from making too many API calls and hitting rate limits or incurring excessive costsI need to enforce fair resource sharing when multiple agents are using the same toolsI want to detect and block runaway agents that are making abnormally high numbers of tool calls

Best for

teams running cost-sensitive agents that call expensive external APIs

multi-tenant systems where agents must share limited tool resources

security teams detecting and responding to agent misbehavior or compromise

Requires

Node.js 16+

rate limit configuration (per-agent, per-tool, global)

shared state store for distributed deployments (Redis, database, or in-memory for single-instance)

Limitations

Rate limit enforcement adds per-call overhead for state lookup and update

Distributed rate limiting requires shared state store (Redis, database) — adds complexity and latency

No built-in adaptive rate limiting based on actual API limits or cost

What makes it unique

Implements rate limiting at the MCP gateway level with awareness of tool identity and agent identity, enabling fine-grained per-tool and per-agent quotas; supports multiple rate-limiting algorithms to match different use cases

vs alternatives

More granular than API-level rate limiting because it can enforce per-agent quotas; more efficient than application-level rate limiting because it blocks calls before they reach the tool

tool-call result inspection and output filtering

Medium confidence

Inspects tool execution results before returning them to the agent, detecting and filtering sensitive data (credentials, PII, API keys) or suspicious patterns. Can redact, mask, or reject results based on configurable rules, preventing agents from exfiltrating sensitive information or being poisoned by malicious tool responses.

Solves for

I want to prevent my agent from accidentally logging or exfiltrating database credentials returned by a toolI need to detect if a tool has been compromised and is returning malicious payloads to my agentI want to mask PII in tool results to comply with privacy regulations

Best for

teams concerned about data exfiltration via agent logs or outputs

organizations handling sensitive data (PII, credentials, financial information)

security teams defending against compromised or malicious tools

Requires

Node.js 16+

output filtering rules (regex patterns, data type detectors, custom validators)

sensitive data patterns or PII detection library

Limitations

Output inspection adds latency to tool-call completion — synchronous processing required

Sensitive data detection relies on patterns or heuristics — can have false positives/negatives

Redaction can break agent logic if it removes data the agent expects

What makes it unique

Operates on tool results at the MCP protocol level, filtering before the agent receives data; supports both pattern-based detection (regex, data types) and custom validators for domain-specific sensitive data

vs alternatives

More effective than agent-level filtering because it catches exfiltration attempts before the agent can log or process data; more transparent than application-level redaction because it operates at the gateway

agent identity and authentication verification

Medium confidence

Verifies the identity of agents making tool calls through multiple authentication methods (API keys, JWT tokens, mTLS certificates, OAuth) and enforces per-agent access control policies. Maps authenticated agents to roles or permissions that determine which tools they can access and under what constraints.

Solves for

I want to ensure only authorized agents can call my tools, not arbitrary clientsI need to enforce different tool-access policies for different agents based on their role or trust levelI want to audit which agent made each tool call for accountability

Best for

multi-agent systems where different agents have different tool access rights

organizations requiring strong agent authentication and authorization

teams building agent platforms with customer-provided agents

Requires

Node.js 16+

authentication method configuration (API keys, JWT, mTLS, OAuth)

agent identity and role database or directory service

Limitations

Authentication adds per-call overhead for credential verification

Credential management (key rotation, revocation) requires operational infrastructure

No built-in federation with external identity providers — requires custom integration

What makes it unique

Integrates agent authentication directly into the MCP call path, enabling per-agent access control without requiring changes to agent code; supports multiple authentication methods to accommodate different deployment scenarios

vs alternatives

More granular than network-level authentication because it enforces per-agent policies; more flexible than hardcoded access control because policies are declarative and updatable

tool-call dependency tracking and circular-call prevention

Medium confidence

Tracks the call graph of tool invocations to detect circular dependencies (Tool A calls Tool B which calls Tool A) and prevent infinite loops. Maintains call stack context across the MCP gateway to identify when an agent is attempting to call a tool that's already in its execution path.

Solves for

I want to prevent my agent from getting stuck in infinite loops of tool callsI need to detect if a tool is maliciously calling itself or creating circular dependenciesI want to understand the dependency graph of tools to optimize agent behavior

Best for

teams running autonomous agents with complex tool interactions

security teams detecting malicious or buggy tool behavior

organizations optimizing agent performance by understanding tool dependencies

Requires

Node.js 16+

call-stack tracking mechanism (in-memory for single-instance, distributed tracing for multi-instance)

configuration for allowed recursion depth

Limitations

Circular-call detection adds per-call overhead for call-stack tracking

Does not prevent legitimate recursive tool calls (e.g., tree traversal) — requires configuration

Distributed agents make call-stack tracking complex — requires shared state or tracing infrastructure

What makes it unique

Operates at the MCP gateway level with full visibility into the call graph, enabling detection of circular calls regardless of agent implementation; tracks call context across the entire execution path

vs alternatives

More effective than agent-level loop detection because it operates at the gateway and can block calls before execution; more complete than timeout-based detection because it identifies circular patterns immediately

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 cordon-cli, ranked by overlap. Discovered automatically through the match graph.

MCP Server22

mcp-runtime-guard

Policy-based MCP tool call proxy

tool call audit logging and monitoringpolicy-based mcp tool call interception and validationtool call denial and error handling

3 shared capabilities

MCP Server26

@policylayer/intercept

Policy-as-code enforcement for MCP tool calls

policy-driven mcp tool call interceptionmcp proxy middleware with transparent tool call routingaudit logging and compliance reporting for tool invocations

3 shared capabilities

MCP Server29

promptspeak-mcp-server

Pre-execution governance for AI agents. Intercepts MCP tool calls before execution with deterministic blocking, human-in-the-loop holds, and behavioral drift detection.

pre-execution tool call interception with deterministic blockingaudit logging and compliance tracking for all tool callshuman-in-the-loop approval holds for flagged tool calls

3 shared capabilities

MCP Server25

@aiclude/mcp-guard

MCP runtime security proxy — intercepts and enforces security policies on MCP tool calls

comprehensive tool call audit logging and tracingmcp tool call interception and policy enforcement

2 shared capabilities

MCP Server29

@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

MCP Server30

imara

Runtime governance layer for AI agents — audit trails, policy enforcement, and compliance for MCP tool calls

mcp tool call interception and audit logging

1 shared capability

Best For

✓teams deploying AI agents in production environments with security-sensitive operations
✓enterprises requiring tool-call governance and compliance auditing
✓developers building multi-tenant AI systems where different agents need different tool access
✓regulated industries (finance, healthcare) where AI actions must be human-auditable
✓teams running autonomous agents that need oversight without full manual control
✓organizations building AI systems for non-technical stakeholders who need visibility
✓regulated organizations requiring SOC 2, HIPAA, or PCI-DSS compliance
✓security teams investigating AI agent behavior anomalies

Known Limitations

⚠Policy evaluation adds latency to each tool call — synchronous blocking required before execution
⚠No built-in machine learning-based anomaly detection — relies on static policy rules only
⚠Policies must be manually authored; no automatic policy generation from usage patterns
⚠Does not prevent indirect attacks via tool chaining or multi-step exploitation
⚠Introduces blocking latency — tool execution is delayed until human approval, potentially minutes or hours
⚠Requires operational overhead to staff approval queues and handle SLA expectations

Requirements

Node.js 16+MCP server compatible with stdio or SSE transportPolicy file in JSON or YAML formatHTTP server or webhook endpoint for approval UI/APIPersistent queue storage (in-memory, Redis, or database)persistent storage backend (file system, database, or log aggregation service)sufficient disk space for log retention policyfile system access or API endpoint for policy updates

Input / Output

Accepts: MCP tool-call requests (JSON-RPC 2.0 format), policy configuration (JSON/YAML), tool schema definitions, MCP tool-call requests with risk metadata, approval policy configuration, human reviewer decisions (approve/reject with reason), MCP tool-call requests and responses, policy evaluation results, approval/rejection decisions, execution outcomes, policy configuration files (JSON/YAML), policy update API requests, policy schema definitions, MCP tool-call arguments, constraint definitions (JSON Schema with custom validators), sanitization function definitions, MCP tool-call requests, rate limit policy configuration, usage tracking data, MCP tool-call results (JSON, text, binary), output filtering rules, sensitive data patterns, MCP tool-call requests with authentication credentials, agent identity and role definitions, access control policy configuration, current call-stack context, recursion policy configuration

Produces: allow/deny decision with reason, modified tool call (if parameter sanitization applied), audit log entry, approval decision with timestamp and reviewer identity, audit trail linking decision to original request, execution permission or rejection response, structured audit log entries (JSON format), queryable log database, compliance reports (CSV, PDF), SIEM-compatible log format (syslog, JSON Lines), policy load confirmation with version, validation errors if policy is invalid, policy change audit log, validation pass/fail decision, sanitized arguments (if modification applied), validation error details, allow/deny decision based on quota, remaining quota information, rate limit exceeded error response, filtered/redacted tool result, detection alerts if suspicious patterns found, audit log of filtered data, authenticated agent identity, authorization decision (allow/deny), audit log with agent identity, circular-call detection alert, call-stack trace, execution rejection if circular call detected

UnfragileRank

Adoption15%(25% weight)

Quality19%(25% weight)

Ecosystem60%(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

9 capabilities

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Package Details

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0.1.8

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The security gateway for AI agents — firewall, auditor, and remote control for MCP tool calls

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Capabilities9 decomposed

mcp tool-call interception and policy enforcement

Medium confidence

Solves for

Best for

teams deploying AI agents in production environments with security-sensitive operations

enterprises requiring tool-call governance and compliance auditing

developers building multi-tenant AI systems where different agents need different tool access

Requires

Node.js 16+

MCP server compatible with stdio or SSE transport

Policy file in JSON or YAML format

Limitations

Policy evaluation adds latency to each tool call — synchronous blocking required before execution

No built-in machine learning-based anomaly detection — relies on static policy rules only

Policies must be manually authored; no automatic policy generation from usage patterns

What makes it unique

vs alternatives

More granular than generic API gateways because it understands MCP tool semantics; simpler to deploy than building custom security middleware into each agent application

human-in-the-loop approval workflow for tool calls

Medium confidence

Solves for

Best for

regulated industries (finance, healthcare) where AI actions must be human-auditable

teams running autonomous agents that need oversight without full manual control

organizations building AI systems for non-technical stakeholders who need visibility

Requires

Node.js 16+

HTTP server or webhook endpoint for approval UI/API

Persistent queue storage (in-memory, Redis, or database)

Limitations

Introduces blocking latency — tool execution is delayed until human approval, potentially minutes or hours

Requires operational overhead to staff approval queues and handle SLA expectations

No built-in escalation to multiple reviewers or consensus-based approval

What makes it unique

vs alternatives

More integrated than post-hoc audit logging because it blocks execution until approval; lighter-weight than full workflow orchestration platforms because it's purpose-built for MCP tool calls

comprehensive audit logging and call tracing

Medium confidence

Solves for

Best for

regulated organizations requiring SOC 2, HIPAA, or PCI-DSS compliance

security teams investigating AI agent behavior anomalies

teams building multi-tenant systems needing per-tenant audit isolation

Requires

Node.js 16+

persistent storage backend (file system, database, or log aggregation service)

sufficient disk space for log retention policy

Limitations

Audit log volume can be high for agents making frequent tool calls — requires storage and indexing strategy

Logs contain sensitive data (tool arguments, results) — requires encryption and access controls

No built-in log retention policies or automatic archival — requires external log management

What makes it unique

vs alternatives

dynamic policy configuration and hot-reload

Medium confidence

Solves for

Best for

security teams responding to incidents and needing rapid policy updates

organizations with frequent policy changes due to regulatory updates or threat landscape shifts

teams running 24/7 agent systems where restarts are costly

Requires

Node.js 16+

file system access or API endpoint for policy updates

policy schema definition (JSON Schema or similar)

Limitations

Hot-reload introduces race conditions if policies are updated while tool calls are in flight

No built-in policy versioning or audit trail of policy changes — requires external tracking

Policy validation is synchronous — invalid policies can block all tool calls until corrected

What makes it unique

vs alternatives

Faster incident response than alternatives requiring restart or redeployment; safer than manual policy editing because validation prevents invalid policies from being activated

parameter sanitization and constraint enforcement

Medium confidence

Solves for

Best for

teams building agents that interact with file systems or databases

security-conscious teams preventing injection attacks via tool arguments

organizations with strict input validation requirements

Requires

Node.js 16+

tool schema definitions with constraint metadata

custom sanitization functions (optional, for domain-specific validation)

Limitations

Constraint definitions must be manually authored for each tool — no automatic constraint inference

Custom sanitization functions can introduce security bugs if not carefully reviewed

Sanitization can silently modify arguments, potentially changing agent intent

What makes it unique

vs alternatives

More precise than generic input validation because it understands tool semantics; more flexible than hardcoded validation because constraints are declarative and reusable across tools

tool-call rate limiting and quota enforcement

Medium confidence

Solves for

Best for

teams running cost-sensitive agents that call expensive external APIs

multi-tenant systems where agents must share limited tool resources

security teams detecting and responding to agent misbehavior or compromise

Requires

Node.js 16+

rate limit configuration (per-agent, per-tool, global)

shared state store for distributed deployments (Redis, database, or in-memory for single-instance)

Limitations

Rate limit enforcement adds per-call overhead for state lookup and update

Distributed rate limiting requires shared state store (Redis, database) — adds complexity and latency

No built-in adaptive rate limiting based on actual API limits or cost

What makes it unique

vs alternatives

More granular than API-level rate limiting because it can enforce per-agent quotas; more efficient than application-level rate limiting because it blocks calls before they reach the tool

tool-call result inspection and output filtering

Medium confidence

Solves for

Best for

teams concerned about data exfiltration via agent logs or outputs

organizations handling sensitive data (PII, credentials, financial information)

security teams defending against compromised or malicious tools

Requires

Node.js 16+

output filtering rules (regex patterns, data type detectors, custom validators)

sensitive data patterns or PII detection library

Limitations

Output inspection adds latency to tool-call completion — synchronous processing required

Sensitive data detection relies on patterns or heuristics — can have false positives/negatives

Redaction can break agent logic if it removes data the agent expects

What makes it unique

vs alternatives

agent identity and authentication verification

Medium confidence

Solves for

Best for

multi-agent systems where different agents have different tool access rights

organizations requiring strong agent authentication and authorization

teams building agent platforms with customer-provided agents

Requires

Node.js 16+

authentication method configuration (API keys, JWT, mTLS, OAuth)

agent identity and role database or directory service

Limitations

Authentication adds per-call overhead for credential verification

Credential management (key rotation, revocation) requires operational infrastructure

No built-in federation with external identity providers — requires custom integration

What makes it unique

vs alternatives

More granular than network-level authentication because it enforces per-agent policies; more flexible than hardcoded access control because policies are declarative and updatable

tool-call dependency tracking and circular-call prevention

Medium confidence

Solves for

Best for

teams running autonomous agents with complex tool interactions

security teams detecting malicious or buggy tool behavior

organizations optimizing agent performance by understanding tool dependencies

Requires

Node.js 16+

call-stack tracking mechanism (in-memory for single-instance, distributed tracing for multi-instance)

configuration for allowed recursion depth

Limitations

Circular-call detection adds per-call overhead for call-stack tracking

Does not prevent legitimate recursive tool calls (e.g., tree traversal) — requires configuration

Distributed agents make call-stack tracking complex — requires shared state or tracing infrastructure

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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cordon-cli

Capabilities9 decomposed

mcp tool-call interception and policy enforcement

human-in-the-loop approval workflow for tool calls

comprehensive audit logging and call tracing

dynamic policy configuration and hot-reload

parameter sanitization and constraint enforcement

tool-call rate limiting and quota enforcement

tool-call result inspection and output filtering

agent identity and authentication verification

tool-call dependency tracking and circular-call prevention

Related Artifactssharing capabilities

mcp-runtime-guard

@policylayer/intercept

promptspeak-mcp-server

@aiclude/mcp-guard

@mcptoolgate/client

imara

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to cordon-cli

Are you the builder of cordon-cli?

Get the weekly brief

Data Sources

cordon-cli

Capabilities9 decomposed

mcp tool-call interception and policy enforcement

human-in-the-loop approval workflow for tool calls

comprehensive audit logging and call tracing

dynamic policy configuration and hot-reload

parameter sanitization and constraint enforcement

tool-call rate limiting and quota enforcement

tool-call result inspection and output filtering

agent identity and authentication verification

tool-call dependency tracking and circular-call prevention

Related Artifactssharing capabilities

mcp-runtime-guard

@policylayer/intercept

promptspeak-mcp-server

@aiclude/mcp-guard

@mcptoolgate/client

imara

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to cordon-cli

Are you the builder of cordon-cli?

Get the weekly brief

Data Sources