Mcp Pickaxe

MCP Pickaxe Server

[Architecture]

[npm version] [CI] [License: MIT] [MCP]

An MCP (Model Context Protocol) server that connects AI assistants like Claude to the Pickaxe platform. Manage your AI agents, knowledge bases, users, and analytics directly through natural language.

Why Use This?

If you're building AI agents on Pickaxe, this MCP server lets you:

• Analyze agent conversations - Review chat history to identify knowledge gaps and improve agent performance
• Manage knowledge bases - Create, update, and connect documents to your agents without leaving your AI workflow
• Handle user management - Create users, manage access, send invitations, and track usage
• Work across multiple studios - Seamlessly switch between different Pickaxe studios in a single session
• Automate workflows - Let Claude handle repetitive Pickaxe admin tasks

Features

Prerequisites

• Node.js 18+
• A Pickaxe account with API access
• Your Pickaxe Studio API key(s)

Installation

Option 1: Install from npm (recommended)

npx mcp-pickaxe

Or install globally:

npm install -g mcp-pickaxe

Option 2: Clone and Build

git clone https://github.com/aplaceforallmystuff/mcp-pickaxe.gitcd mcp-pickaxenpm installnpm run build

Configuration

1. Get Your Pickaxe API Key

1. Log in to Pickaxe Studio
2. Navigate to Settings > API
3. Copy your Studio API key (starts with studio-)

2. Configure Your MCP Client

For Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{  "mcpServers": {    "pickaxe": {      "command": "node",      "args": ["/path/to/mcp-pickaxe/dist/index.js"],      "env": {        "PICKAXE_STUDIO_MAIN": "studio-your-api-key-here"      }    }  }}

For Claude Code

Add to ~/.claude.json:

{  "mcpServers": {    "pickaxe": {      "command": "node",      "args": ["/path/to/mcp-pickaxe/dist/index.js"],      "env": {        "PICKAXE_STUDIO_MAIN": "studio-your-api-key-here"      }    }  }}

Multi-Studio Configuration

To work with multiple Pickaxe studios, add multiple environment variables:

{  "env": {    "PICKAXE_STUDIO_PRODUCTION": "studio-xxx-xxx-xxx",    "PICKAXE_STUDIO_STAGING": "studio-yyy-yyy-yyy",    "PICKAXE_STUDIO_DEV": "studio-zzz-zzz-zzz",    "PICKAXE_DEFAULT_STUDIO": "PRODUCTION"  }}

Then specify which studio to use in your requests:

• If you set PICKAXE_DEFAULT_STUDIO, that studio is used when none is specified
• If only one studio is configured, it's used automatically
• Otherwise, pass studio="STAGING" (or similar) to any tool

Use Cases

These are real workflows built with mcp-pickaxe in production environments.

1. Security Monitoring with n8n

Scenario: Detect prompt injection attempts across 29+ AI agents in real-time.

Implementation: An n8n workflow polls chat_history hourly for all agents, runs messages against injection detection patterns (stored in Notion), and routes alerts by severity:

• HIGH/CRITICAL → Telegram alert + Notion log
• LOW/MEDIUM → Notion log only

n8n Schedule (hourly)    → Fetch patterns from Notion    → Loop through 29 pickaxe IDs    → Fetch chat_history for each    → Detect injections (regex patterns)    → Route by severity → Alert/Log

Tools used: chat_history, studios_list

Result: Real-time security monitoring across an entire studio with dynamic pattern management and severity-based alerting.

2. Knowledge Base Auto-Research Pipeline

Scenario: Automatically fact-check and maintain 31+ knowledge base articles.

Implementation: An n8n workflow queries KB articles from Notion, extracts key claims, fact-checks via Perplexity API, classifies changes by risk level, and routes to auto-update or human review.

Daily Schedule (2am)    → Query KB articles from Notion    → Filter by day (hash-based, ~1/7th daily)    → Extract key claims    → Perplexity fact-check    → Classify: none/low/major risk    → Route: auto-update or create review task

Tools used: doc_list, doc_get, doc_create, doc_connect

Result: KB content stays current with automated fact-checking and human-in-the-loop for major changes.

3. Agent Performance Review

Scenario: Quarterly review of a training studio to identify KB gaps and user pain points.

Workflow:

1. "Fetch chat history from my training agents"2. "Analyze: which questions got unclear or uncertain responses?"3. "List all KB documents - which topics are missing?"4. "Check user stats - who's most active, who's churning?"5. "Create KB documents addressing the top 3 gaps"6. "Connect new documents to the relevant agents"

Tools used: chat_history, doc_list, doc_create, doc_connect, user_list

Result: Data-driven KB improvements based on actual user conversations rather than guesswork.

4. Multi-Studio Operations

Scenario: Managing multiple Pickaxe studios from a single Claude session.

Configuration:

{  "env": {    "PICKAXE_STUDIO_PRODUCTION": "studio-xxx",    "PICKAXE_STUDIO_STAGING": "studio-yyy",    "PICKAXE_STUDIO_DEV": "studio-zzz",    "PICKAXE_DEFAULT_STUDIO": "PRODUCTION"  }}

Workflow:

1. "List users in PRODUCTION - how many signups this month?"2. "Switch to STAGING - list products"3. "Compare KB document counts across all studios"4. "Find which studio has the most chat activity"

Tools used: studios_list, user_list, doc_list, products_list

Result: Cross-studio visibility without switching contexts or API keys manually.

5. User Memory Auditing

Scenario: Review what your agents remember about users for personalization and privacy compliance.

Workflow:

1. "List all memory schemas defined in the studio"2. "Get memories for [email protected]"3. "What does the system know about this user's situation?"4. "Which memory fields are most populated across users?"

Example output:

User: [email protected]Nickname: "Cautious Educator from Madrid"Summary: "Teaching [language] for [platform] at low hourly rate,         considering self-employment status due to         uncertain income"Memories: 1 stored

Tools used: memory_list, memory_get_user, user_list

Result: Visibility into personalization data for both product improvement and GDPR compliance.

Quick Start Examples

Once configured, you can interact with Pickaxe through natural language:

Analyze Agent Performance

"Show me the last 20 conversations from my support agent"

"What questions are users asking that my agent can't answer?"

Manage Knowledge Base

"Create a new document called 'FAQ' with this content: [your content]"

"Connect the FAQ document to my customer support agent"

"List all documents in my knowledge base"

User Management

"Show me all users and their usage stats"

"Create a new user with email [email protected] and give them access to the Pro product"

"Send invitations to these emails: [list of emails]"

Multi-Studio Operations

"List all users in my staging studio"

"Compare the documents between production and staging"

Available Tools

Studio Management

• studios_list - List all configured studios and the current default

Chat History

• chat_history - Fetch conversation history for an agent
  • Parameters: pickaxeId, skip, limit, format ("messages" or "raw"), studio

Document Management

• doc_create - Create document from content or URL
• doc_list - List all documents (with pagination)
• doc_get - Get a specific document
• doc_delete - Delete a document
• doc_connect - Link document to an agent
• doc_disconnect - Unlink document from an agent

User Management

• user_list - List all users with access and usage info
• user_get - Get a specific user by email
• user_create - Create a new user
• user_update - Update user details, products, or usage
• user_delete - Delete a user
• user_invite - Send email invitations

Products

• products_list - List available products/bundles

Memory

• memory_list - List memory schemas
• memory_get_user - Get collected memories for a user

Development

# Run in development mode (auto-reloads)npm run dev
# Build for productionnpm run build
# Run the built versionnpm start

Troubleshooting

"No Pickaxe studios configured"

Ensure you have at least one PICKAXE_STUDIO_* environment variable set in your MCP config.

"Studio not found"

Check that the studio name matches exactly (case-insensitive). Run studios_list to see available options.

"Pickaxe API error (401)"

Your API key is invalid or expired. Get a new one from Pickaxe Studio settings.

"Pickaxe API error (403)"

Your API key doesn't have permission for this operation. Check your Pickaxe account permissions.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Links

• Pickaxe Platform
• Pickaxe Studio
• Model Context Protocol
• MCP Specification