How does the hold queue work?

Risky operations are held pending human review; humans can approve with modifications or reject and halt the agent.

Can it detect when an agent is drifting from its intended behavior?

Yes, it includes behavioral drift detection to identify gradual shifts in agent goals or actions.

Is there an audit trail of agent actions?

Yes, every tool call attempt is logged whether it was allowed, held, or blocked.

How fast is the validation process?

Validation and interception occur in approximately 0.1 milliseconds before execution.

Promptspeak MCP Server

securitygovernancehuman-in-the-loopauditvalidation

Pre-execution governance server that intercepts, validates, and controls AI agent tool calls with human-in-the-loop and audit trails.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

We ran the install command below but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx promptspeak-mcp-server

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "chrbailey-promptspeak-mcp-server": {
      "command": "npx",
      "args": [
        "promptspeak-mcp-server"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This server acts as a governance layer for AI agents by intercepting every tool call before execution and validating it through an 8-stage pipeline including risk classification, behavioral drift detection, and security scanning. It can block or hold risky operations for human approval and maintains a complete audit trail of all attempted tool calls. Use this server when you need deterministic constraints on agent actions, human-in-the-loop approval workflows, or compliance in regulated domains.

Use cases

Intercept and validate AI agent tool calls before execution

Hold high-risk operations for human review and approval

Detect behavioral drift in agent actions over time

Maintain an audit trail of all agent tool call attempts

Scan code or commands for security vulnerabilities pre-execution

Key features

8-stage validation pipeline including risk and drift detection

Human-in-the-loop hold queue with approve/reject workflows

Security scanning of code and commands before execution

Deterministic governance frames for constraint enforcement

Audit trail of allowed and blocked tool calls

45+ governance tools and 650+ tests included

Capabilities & Tool Schemas (43)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Promptspeak MCP Server.

ps_validate

Validate a frame against all rules without executing

ps_validate_batch

Batch validation for efficiency

ps_execute

Full pipeline: validate → hold check → execute → audit

ps_execute_dry_run

Run full pipeline without executing the tool

ps_hold_list

List all operations awaiting human approval

ps_hold_approve

Approve with optional modified arguments

Documentation Overview

promptspeak-mcp-server

Pre-execution governance for AI agents. Blocks dangerous tool calls before they execute.

AI agents call tools (file writes, API requests, shell commands) with no validation layer between intent and execution. A prompt injection, hallucinated argument, or drifting goal can trigger irreversible actions. PromptSpeak intercepts every MCP tool call, validates it against deterministic rules, and blocks or holds risky operations for human approval — in 0.1ms, before anything executes.

PromptSpeak Governance Demo

When to use this

You run AI agents that call tools (MCP servers, function calling, tool use) and need a governance layer between the agent and the tools.
You need human-in-the-loop approval for high-risk operations (production deployments, financial transactions, legal filings).
You want to detect behavioral drift — an agent gradually shifting away from its assigned task.
You need an audit trail of every tool call an agent attempted, whether it was allowed or blocked.
You operate in a regulated domain (legal, financial, healthcare) where agent actions must be deterministically constrained.

Install

Claude Code

Add to ~/.claude/settings.json (or project-level .claude/settings.json):

config.json

{
  "mcpServers": {
    "promptspeak": {
      "command": "npx",
      "args": ["promptspeak-mcp-server"]
    }
  }
}

Restart Claude Code. All 56 governance tools are immediately available.

Claude Desktop

Add to claude_desktop_config.json:

config.json

{
  "mcpServers": {
    "promptspeak": {
      "command": "npx",
      "args": ["promptspeak-mcp-server"]
    }
  }
}

As a library

Terminal

npm install promptspeak-mcp-server

From source

bash

git clone https://github.com/chrbailey/promptspeak-mcp-server.git
cd promptspeak-mcp-server
npm install && npm run build
npm start

Usage examples

1. Validate a governance frame

Validate that a PromptSpeak frame is structurally and semantically correct before using it. Frames encode governance constraints as symbol sequences — mode first, then domain, action, and entity.

jsonc

// Tool call: ps_validate
{
  "name": "ps_validate",
  "arguments": {
    "frame": "⊕◊▶α",
    "validationLevel": "full"
  }
}

jsonc

// Response
{
  "valid": true,
  "frame": "⊕◊▶α",
  "parsedFrame": {
    "mode": { "symbol": "⊕", "meaning": "strict" },
    "domain": { "symbol": "◊", "meaning": "financial" },
    "action": { "symbol": "▶", "meaning": "execute" },
    "entity": { "symbol": "α", "meaning": "primary" }
  },
  "parseConfidence": 1.0,
  "report": {
    "valid": true,
    "errors": [],
    "warnings": [
      { "code": "ACTION_MISSING_DOMAIN", "message": "Consider adding domain context", "severity": "warning" }
    ]
  },
  "summary": { "errors": 0, "warnings": 1, "passed": 16 }
}

Invalid frames return actionable suggestions:

jsonc

// Tool call: validate a frame with conflicting modes
{
  "name": "ps_validate",
  "arguments": { "frame": "⊕⊖▶", "validationLevel": "semantic" }
}

// Response: blocked — strict + flexible modes conflict
{
  "valid": false,
  "summary": { "errors": 1, "warnings": 0, "passed": 10 },
  "suggestions": ["Remove either ⊕ (strict) or ⊖ (flexible) - cannot have both"]
}

2. Hold queue workflow — human-in-the-loop approval

When an agent attempts a risky operation (high drift score, low confidence, security finding), the action is held for human review instead of executing. This is the full hold lifecycle: list, inspect, approve or reject.

Step 1: List pending holds

jsonc

// Tool call: ps_hold_list
{
  "name": "ps_hold_list",
  "arguments": {}
}

jsonc

// Response: one hold awaiting human review
{
  "holds": [
    {
      "holdId": "hold_7k2m9x",
      "agentId": "devops-agent",
      "frame": "⊕◈▶α",
      "tool": "deploy_to_production",
      "severity": "high",
      "reason": "drift_prediction",
      "state": "pending",
      "evidence": { "driftScore": 0.72, "predictedDrift": 0.85 }
    }
  ],
  "count": 1,
  "expiredCount": 0
}

Step 2: Approve with modifications (or reject)

jsonc

// Tool call: ps_hold_approve — approve but downgrade to staging
{
  "name": "ps_hold_approve",
  "arguments": {
    "holdId": "hold_7k2m9x",
    "reason": "Reviewed — safe for staging, not production",
    "modifiedArgs": { "environment": "staging" }
  }
}

jsonc

// Response
{
  "success": true,
  "decision": {
    "holdId": "hold_7k2m9x",
    "state": "approved",
    "decidedBy": "human",
    "reason": "Reviewed — safe for staging, not production"
  },
  "executionResult": { "success": true }
}

To reject instead:

JSON Config

{
  "name": "ps_hold_reject",
  "arguments": {
    "holdId": "hold_7k2m9x",
    "reason": "Drift too high — recalibrate agent first",
    "haltAgent": true
  }
}
// Agent is halted (circuit breaker tripped) and the operation is denied.

3. Security scanning — catch vulnerabilities before execution

Scan code content for security issues before an agent writes it to disk. Critical findings block execution; high-severity findings are held for human review.

Dockerfile

// Tool call: ps_security_scan
{
  "name": "ps_security_scan",
  "arguments": {
    "content": "const query = `SELECT * FROM users WHERE id = ${userId}`;\nconst API_KEY = 'sk-1234567890abcdef1234567890abcdef';"
  }
}

Dockerfile

// Response: two findings — one critical (blocked), one critical (blocked)
{
  "findings": [
    {
      "patternId": "sql-injection",
      "severity": "critical",
      "match": "SELECT * FROM users WHERE id = ${userId}",
      "line": 1,
      "context": "const query = `SELECT * FROM users WHERE id = ${userId}`;",
      "suggestion": "Use parameterized queries or prepared statements instead of template literals"
    },
    {
      "patternId": "hardcoded-secret",
      "severity": "critical",
      "match": "API_KEY = 'sk-1234567890abcdef1234567890abcdef'",
      "line": 2,
      "context": "const API_KEY = 'sk-1234567890abcdef1234567890abcdef';",
      "suggestion": "Move secrets to environment variables or a secrets manager"
    }
  ],
  "scannedAt": "2026-03-19T12:00:00.000Z",
  "contentLength": 98,
  "patternsChecked": 10,
  "enforcement": {
    "blocked": [
      { "patternId": "sql-injection", "severity": "critical" },
      { "patternId": "hardcoded-secret", "severity": "critical" }
    ],
    "held": [],
    "warned": [],
    "logged": []
  }
}

Use ps_security_gate instead of ps_security_scan to enforce the policy — it blocks on critical, holds high-severity for human review, and warns on medium:

jsonc

// Tool call: ps_security_gate — scan AND enforce
{
  "name": "ps_security_gate",
  "arguments": {
    "content": "app.use(cors());\napp.listen(0.0.0.0, 3000);",
    "action": "write_file"
  }
}

// Response: held for review (insecure defaults = high severity)
{
  "decision": "held",
  "reason": "Security: 1 high-severity finding(s) held for review — insecure-defaults",
  "scan": { "findings": [{ "patternId": "insecure-defaults", "severity": "high", "line": 1 }] }
}

How it works: 9-stage validation pipeline

Every tool call passes through this pipeline. If any stage fails, execution is blocked.

Code

Agent calls tool
  │
  ├─ 1. Circuit Breaker ──── Halted agents blocked instantly (no further checks)
  ├─ 2. Frame Validation ─── Structural, semantic, and chain rule checks
  ├─ 3. Drift Prediction ─── Pre-flight behavioral anomaly detection
  ├─ 4. Hold Check ────────── Risky operations held for human approval
  ├─ 5. Interceptor ───────── Final permission gate (confidence thresholds)
  ├─ 6. Security Scan ─────── Scans write actions for vulnerabilities (see below)
  ├─ 7. Tool Execution ────── Only reached if all 6 pre-checks pass
  ├─ 8. Post-Audit ────────── Confirms behavior matched prediction
  └─ 9. Immediate Action ──── Halts agent if critical drift detected post-execution

Stages 1-6 are pre-execution — the tool never runs if any check fails. Stages 8-9 are post-execution — they detect drift and can halt the agent for future calls.

Security scanning

When an agent writes code (write_file, edit_file, create_file, patch_file), the content is scanned against 10 detection patterns before execution. Severity determines enforcement:

Severity	Enforcement	What it catches
CRITICAL	Blocked — execution denied	SQL injection via template literals, hardcoded API keys/passwords/tokens
HIGH	Held — queued for human review	Security-related TODOs, logging sensitive data, insecure defaults (`cors()`, `0.0.0.0`, `debug: true`)
MEDIUM	Warned — logged, execution continues	Empty catch blocks, hedging comments ("probably works"), disabled tests
LOW	Logged — no enforcement	`DROP TABLE`, `rm -rf` (flagged for awareness)

What works (tested)

All claims below are backed by passing tests (104 tests across 5 test files):

Read the full README →View source on GitHub →

Related MCP Servers

View all in Security View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Install check

Inconclusive

Didn't respond in our test window — often a slow first install.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Promptspeak MCP Server

It governs AI agent tool calls including file writes, API requests, shell commands, and other MCP tool invocations.

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Technical Specs & Signals

CategorySecurity

PricingFree

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Verified ownership10/20

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More in 🔒 Security →Best MCP servers for Security →Alternatives to Promptspeak MCP Server →Install in Claude Desktop Install in Cursor Install in VS Code

Promptspeak MCP Server

securitygovernancehuman-in-the-loopauditvalidation

Pre-execution governance server that intercepts, validates, and controls AI agent tool calls with human-in-the-loop and audit trails.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

We ran the install command below but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx promptspeak-mcp-server

No response to initialize.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "chrbailey-promptspeak-mcp-server": {
      "command": "npx",
      "args": [
        "promptspeak-mcp-server"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

Use cases

Intercept and validate AI agent tool calls before execution

Hold high-risk operations for human review and approval

Detect behavioral drift in agent actions over time

Maintain an audit trail of all agent tool call attempts

Scan code or commands for security vulnerabilities pre-execution

Key features

8-stage validation pipeline including risk and drift detection

Human-in-the-loop hold queue with approve/reject workflows

Security scanning of code and commands before execution

Deterministic governance frames for constraint enforcement

Audit trail of allowed and blocked tool calls

45+ governance tools and 650+ tests included

Capabilities & Tool Schemas (43)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Promptspeak MCP Server.

ps_validate

Validate a frame against all rules without executing

ps_validate_batch

Batch validation for efficiency

ps_execute

Full pipeline: validate → hold check → execute → audit

ps_execute_dry_run

Run full pipeline without executing the tool

ps_hold_list

List all operations awaiting human approval

ps_hold_approve

Approve with optional modified arguments

Documentation Overview

promptspeak-mcp-server

Pre-execution governance for AI agents. Blocks dangerous tool calls before they execute.

PromptSpeak Governance Demo

When to use this

You run AI agents that call tools (MCP servers, function calling, tool use) and need a governance layer between the agent and the tools.
You need human-in-the-loop approval for high-risk operations (production deployments, financial transactions, legal filings).
You want to detect behavioral drift — an agent gradually shifting away from its assigned task.
You need an audit trail of every tool call an agent attempted, whether it was allowed or blocked.
You operate in a regulated domain (legal, financial, healthcare) where agent actions must be deterministically constrained.

Install

Claude Code

Add to ~/.claude/settings.json (or project-level .claude/settings.json):

config.json

{
  "mcpServers": {
    "promptspeak": {
      "command": "npx",
      "args": ["promptspeak-mcp-server"]
    }
  }
}

Restart Claude Code. All 56 governance tools are immediately available.

Claude Desktop

Add to claude_desktop_config.json:

config.json

{
  "mcpServers": {
    "promptspeak": {
      "command": "npx",
      "args": ["promptspeak-mcp-server"]
    }
  }
}

As a library

Terminal

npm install promptspeak-mcp-server

From source

bash

git clone https://github.com/chrbailey/promptspeak-mcp-server.git
cd promptspeak-mcp-server
npm install && npm run build
npm start

Usage examples

1. Validate a governance frame

Validate that a PromptSpeak frame is structurally and semantically correct before using it. Frames encode governance constraints as symbol sequences — mode first, then domain, action, and entity.

jsonc

// Tool call: ps_validate
{
  "name": "ps_validate",
  "arguments": {
    "frame": "⊕◊▶α",
    "validationLevel": "full"
  }
}

jsonc

// Response
{
  "valid": true,
  "frame": "⊕◊▶α",
  "parsedFrame": {
    "mode": { "symbol": "⊕", "meaning": "strict" },
    "domain": { "symbol": "◊", "meaning": "financial" },
    "action": { "symbol": "▶", "meaning": "execute" },
    "entity": { "symbol": "α", "meaning": "primary" }
  },
  "parseConfidence": 1.0,
  "report": {
    "valid": true,
    "errors": [],
    "warnings": [
      { "code": "ACTION_MISSING_DOMAIN", "message": "Consider adding domain context", "severity": "warning" }
    ]
  },
  "summary": { "errors": 0, "warnings": 1, "passed": 16 }
}

Invalid frames return actionable suggestions:

jsonc

// Tool call: validate a frame with conflicting modes
{
  "name": "ps_validate",
  "arguments": { "frame": "⊕⊖▶", "validationLevel": "semantic" }
}

// Response: blocked — strict + flexible modes conflict
{
  "valid": false,
  "summary": { "errors": 1, "warnings": 0, "passed": 10 },
  "suggestions": ["Remove either ⊕ (strict) or ⊖ (flexible) - cannot have both"]
}

2. Hold queue workflow — human-in-the-loop approval

Step 1: List pending holds

jsonc

// Tool call: ps_hold_list
{
  "name": "ps_hold_list",
  "arguments": {}
}

jsonc

// Response: one hold awaiting human review
{
  "holds": [
    {
      "holdId": "hold_7k2m9x",
      "agentId": "devops-agent",
      "frame": "⊕◈▶α",
      "tool": "deploy_to_production",
      "severity": "high",
      "reason": "drift_prediction",
      "state": "pending",
      "evidence": { "driftScore": 0.72, "predictedDrift": 0.85 }
    }
  ],
  "count": 1,
  "expiredCount": 0
}

Step 2: Approve with modifications (or reject)

jsonc

// Tool call: ps_hold_approve — approve but downgrade to staging
{
  "name": "ps_hold_approve",
  "arguments": {
    "holdId": "hold_7k2m9x",
    "reason": "Reviewed — safe for staging, not production",
    "modifiedArgs": { "environment": "staging" }
  }
}

jsonc

// Response
{
  "success": true,
  "decision": {
    "holdId": "hold_7k2m9x",
    "state": "approved",
    "decidedBy": "human",
    "reason": "Reviewed — safe for staging, not production"
  },
  "executionResult": { "success": true }
}

To reject instead:

JSON Config

{
  "name": "ps_hold_reject",
  "arguments": {
    "holdId": "hold_7k2m9x",
    "reason": "Drift too high — recalibrate agent first",
    "haltAgent": true
  }
}
// Agent is halted (circuit breaker tripped) and the operation is denied.

3. Security scanning — catch vulnerabilities before execution

Scan code content for security issues before an agent writes it to disk. Critical findings block execution; high-severity findings are held for human review.

Dockerfile

// Tool call: ps_security_scan
{
  "name": "ps_security_scan",
  "arguments": {
    "content": "const query = `SELECT * FROM users WHERE id = ${userId}`;\nconst API_KEY = 'sk-1234567890abcdef1234567890abcdef';"
  }
}

Dockerfile

// Response: two findings — one critical (blocked), one critical (blocked)
{
  "findings": [
    {
      "patternId": "sql-injection",
      "severity": "critical",
      "match": "SELECT * FROM users WHERE id = ${userId}",
      "line": 1,
      "context": "const query = `SELECT * FROM users WHERE id = ${userId}`;",
      "suggestion": "Use parameterized queries or prepared statements instead of template literals"
    },
    {
      "patternId": "hardcoded-secret",
      "severity": "critical",
      "match": "API_KEY = 'sk-1234567890abcdef1234567890abcdef'",
      "line": 2,
      "context": "const API_KEY = 'sk-1234567890abcdef1234567890abcdef';",
      "suggestion": "Move secrets to environment variables or a secrets manager"
    }
  ],
  "scannedAt": "2026-03-19T12:00:00.000Z",
  "contentLength": 98,
  "patternsChecked": 10,
  "enforcement": {
    "blocked": [
      { "patternId": "sql-injection", "severity": "critical" },
      { "patternId": "hardcoded-secret", "severity": "critical" }
    ],
    "held": [],
    "warned": [],
    "logged": []
  }
}

Use ps_security_gate instead of ps_security_scan to enforce the policy — it blocks on critical, holds high-severity for human review, and warns on medium:

jsonc

// Tool call: ps_security_gate — scan AND enforce
{
  "name": "ps_security_gate",
  "arguments": {
    "content": "app.use(cors());\napp.listen(0.0.0.0, 3000);",
    "action": "write_file"
  }
}

// Response: held for review (insecure defaults = high severity)
{
  "decision": "held",
  "reason": "Security: 1 high-severity finding(s) held for review — insecure-defaults",
  "scan": { "findings": [{ "patternId": "insecure-defaults", "severity": "high", "line": 1 }] }
}

How it works: 9-stage validation pipeline

Every tool call passes through this pipeline. If any stage fails, execution is blocked.

Code

Agent calls tool
  │
  ├─ 1. Circuit Breaker ──── Halted agents blocked instantly (no further checks)
  ├─ 2. Frame Validation ─── Structural, semantic, and chain rule checks
  ├─ 3. Drift Prediction ─── Pre-flight behavioral anomaly detection
  ├─ 4. Hold Check ────────── Risky operations held for human approval
  ├─ 5. Interceptor ───────── Final permission gate (confidence thresholds)
  ├─ 6. Security Scan ─────── Scans write actions for vulnerabilities (see below)
  ├─ 7. Tool Execution ────── Only reached if all 6 pre-checks pass
  ├─ 8. Post-Audit ────────── Confirms behavior matched prediction
  └─ 9. Immediate Action ──── Halts agent if critical drift detected post-execution

Stages 1-6 are pre-execution — the tool never runs if any check fails. Stages 8-9 are post-execution — they detect drift and can halt the agent for future calls.

Security scanning

When an agent writes code (write_file, edit_file, create_file, patch_file), the content is scanned against 10 detection patterns before execution. Severity determines enforcement:

Severity	Enforcement	What it catches
CRITICAL	Blocked — execution denied	SQL injection via template literals, hardcoded API keys/passwords/tokens
HIGH	Held — queued for human review	Security-related TODOs, logging sensitive data, insecure defaults (`cors()`, `0.0.0.0`, `debug: true`)
MEDIUM	Warned — logged, execution continues	Empty catch blocks, hedging comments ("probably works"), disabled tests
LOW	Logged — no enforcement	`DROP TABLE`, `rm -rf` (flagged for awareness)

What works (tested)

All claims below are backed by passing tests (104 tests across 5 test files):

Read the full README →View source on GitHub →

Related MCP Servers

View all in Security View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Install check

Inconclusive

Didn't respond in our test window — often a slow first install.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Promptspeak MCP Server

It governs AI agent tool calls including file writes, API requests, shell commands, and other MCP tool invocations.

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Technical Specs & Signals

CategorySecurity

PricingFree

56Quality signal: Good · 56/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools29/30

Adoption & activity3/15

Community engagement0/10

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Critical 0High 0Medium 0Low 0

Scanned 24d ago via OSV.dev · promptspeak-mcp-server (npm)

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Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

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