Custom MCP Tools

Custom MCP (Model Context Protocol) tools allow you to extend Fluxbase’s AI capabilities with domain-specific functionality. Write tools in TypeScript, deploy them to your Fluxbase instance, and they become available to all AI chatbots configured to use them.

Overview

MCP tools are functions that AI assistants can invoke during conversations. Custom tools let you:

• Integrate external APIs (weather, payments, notifications)
• Implement business logic accessible to AI
• Create domain-specific data transformations
• Build custom validation and processing pipelines

Custom resources provide read-only data that AI can access during conversations, such as configuration, analytics, or dynamic content.

Quick Start

1. Create a Custom Tool

Create a TypeScript file with your tool implementation:

get_user_orders.ts

// @fluxbase:description Get orders for a user

export async function handler(
  args: { user_id: string; limit?: number },
  fluxbase: any,           // User-scoped client (respects RLS)
  fluxbaseService: any,    // Service-scoped client (bypasses RLS)
  utils: any               // Tool metadata and helpers
) {
  const { user_id, limit = 10 } = args;

  // Same signature as edge functions: handler(args, fluxbase, fluxbaseService, utils)
  const { data: orders } = await fluxbase
    .from("orders")
    .select("id, status, total, created_at")
    .eq("user_id", user_id)
    .order("created_at", { ascending: false })
    .limit(limit)
    .execute();

  return {
    content: [{ type: "text", text: JSON.stringify(orders, null, 2) }]
  };
}

2. Deploy via CLI

# Create the tool
fluxbase mcp tools create get_user_orders --code ./get_user_orders.ts

# Or sync a directory of tools (all .ts files become tools)
fluxbase mcp tools sync --dir ./mcp-tools

3. Use in Chatbots

Configure your chatbot to use the custom tool. Custom tools use a colon-separated naming format:

• Default namespace: custom:tool_name
• Other namespaces: custom:namespace:tool_name

order_assistant.ts

/**
 * @fluxbase:mcp-tools custom:get_user_orders,query_table
 */

export default `You are an order management assistant.
You can look up user orders and query tables.`;

For tools in non-default namespaces:

production_assistant.ts

/**
 * @fluxbase:mcp-tools custom:production:get_user_orders,custom:analytics:track_event
 */

export default `You are a production assistant with access to production tools.`;

Tool Annotations

All annotations are optional. The @fluxbase: prefix is consistent with edge functions and jobs.

Annotation	Description	Default
@fluxbase:name	Tool name	Filename (e.g., weather_forecast.ts → weather_forecast)
@fluxbase:namespace	Tool namespace for isolation	default
@fluxbase:description	Human-readable description for AI	None
@fluxbase:scopes	Additional MCP scopes	execute:custom
@fluxbase:timeout	Execution timeout in seconds	30
@fluxbase:memory	Memory limit in MB	128
@fluxbase:allow-net	Allow network access	false
@fluxbase:allow-env	Allow secrets/environment access	false

Input Schema

Define input validation using JSON Schema:

// Via API or dashboard, set input_schema:
{
  "type": "object",
  "properties": {
    "location": {
      "type": "string",
      "description": "City name or coordinates"
    },
    "days": {
      "type": "number",
      "default": 3,
      "minimum": 1,
      "maximum": 14
    }
  },
  "required": ["location"]
}

Handler Signature

MCP tools use the same handler signature as edge functions and jobs:

handler(args, fluxbase, fluxbaseService, utils)

Parameter	Description
args	Input arguments passed to the tool
fluxbase	User-scoped Fluxbase client (respects RLS)
fluxbaseService	Service-scoped Fluxbase client (bypasses RLS)
utils	Tool metadata and helpers

Utils Object

interface ToolUtils {
  // Tool metadata
  tool_name: string;
  namespace: string;

  // User information
  user_id: string;
  user_email: string;
  user_role: string;
  scopes: string[];

  // Secrets accessor (requires allow_env permission)
  secrets: {
    get(name: string): string | undefined;
  };

  // Environment access (requires allow_env permission)
  env: {
    get(name: string): string | undefined;
  };

  // AI capabilities - access AI completions and embeddings
  ai: {
    // Chat completion
    chat(options: {
      messages: Array<{ role: string; content: string }>;
      provider?: string;  // AI provider name (uses default if not specified)
      model?: string;     // Model override
      maxTokens?: number; // Max response tokens (default: 1024)
      temperature?: number; // 0-1, default: 0.7
    }): Promise<{
      content: string;
      model: string;
      finish_reason?: string;
      usage?: { prompt_tokens: number; completion_tokens: number; total_tokens: number };
    }>;

    // Generate embeddings
    embed(options: {
      text: string;
      provider?: string; // Embedding provider
    }): Promise<{
      embedding: number[];
      model: string;
    }>;

    // List available providers
    listProviders(): Promise<{
      providers: Array<{ name: string; type: string; model: string; enabled: boolean }>;
      default: string;
    }>;
  };
}

interface FluxbaseClient {
  // Query builder
  from(table: string): QueryBuilder;
  insert(table: string, data: any): InsertBuilder;
  update(table: string, data: any): UpdateBuilder;
  delete(table: string): DeleteBuilder;

  // RPC calls
  rpc(functionName: string, params?: object): Promise<{ data: any; error: null }>;

  // Storage operations
  storage: {
    list(bucket: string, options?: { prefix?: string; limit?: number }): Promise<any>;
    download(bucket: string, path: string): Promise<Response>;
    upload(bucket: string, path: string, file: any, options?: { contentType?: string }): Promise<any>;
    remove(bucket: string, paths: string | string[]): Promise<any>;
    getPublicUrl(bucket: string, path: string): string;
  };

  // Edge functions
  functions: {
    invoke(name: string, options?: { body?: any; headers?: object }): Promise<{ data: any; error: null }>;
  };
}

Accessing Fluxbase Data

export async function handler(args: { userId: string }, fluxbase, fluxbaseService, utils) {
  // Query using user context (respects RLS - user can only see their own data)
  const { data: userData } = await fluxbase
    .from("profiles")
    .select("id, name, email")
    .eq("id", args.userId)
    .single()
    .execute();

  // Query using service context (bypasses RLS - admin access)
  const { data: allUsers } = await fluxbaseService
    .from("profiles")
    .select("id, name")
    .limit(10)
    .execute();

  return {
    content: [{ type: "text", text: JSON.stringify({ user: userData, recentUsers: allUsers }) }]
  };
}

Insert, Update, Delete

export async function handler(args: { name: string; email: string }, fluxbase, fluxbaseService, utils) {
  // Insert a new record
  const { data: created } = await fluxbaseService
    .insert("users", { name: args.name, email: args.email })
    .select("id, name, email")
    .execute();

  // Update a record
  const { data: updated } = await fluxbaseService
    .update("users", { last_login: new Date().toISOString() })
    .eq("email", args.email)
    .execute();

  // Delete a record
  const { data: deleted } = await fluxbaseService
    .delete("users")
    .eq("id", args.userId)
    .execute();

  return created;
}

Custom Resources

Resources provide read-only data to AI assistants. The URI defaults to fluxbase://custom/{name} based on filename.

Resources use the same handler signature:

analytics_summary.ts

// @fluxbase:description Real-time analytics summary

export async function handler(params: {}, fluxbase, fluxbaseService, utils) {
  const { data } = await fluxbase
    .from("analytics_events")
    .select("*")
    .execute();

  return [
    {
      type: "text",
      text: JSON.stringify({
        total_events: data.length,
        last_updated: new Date().toISOString()
      })
    }
  ];
}

Template Resources

For parameterized URIs, specify a custom URI with {param} placeholders. Templates are auto-detected:

user_profile.ts

// @fluxbase:uri fluxbase://custom/users/{id}/profile

export async function handler(params: { id: string }, fluxbase, fluxbaseService, utils) {
  const { data: user } = await fluxbase
    .from("users")
    .select("*")
    .eq("id", params.id)
    .single()
    .execute();

  return [{ type: "text", text: JSON.stringify(user) }];
}

API Reference

List Tools

GET /api/v1/mcp/tools

Create Tool

POST /api/v1/mcp/tools
Content-Type: application/json

{
  "name": "weather_forecast",
  "namespace": "default",
  "description": "Get weather forecast",
  "code": "export async function handler...",
  "input_schema": { "type": "object", ... },
  "required_scopes": ["execute:custom"],
  "timeout_seconds": 30,
  "memory_limit_mb": 128,
  "allow_net": true,
  "allow_env": false
}

Sync Tool (Upsert)

POST /api/v1/mcp/tools/sync
Content-Type: application/json

{
  "name": "weather_forecast",
  "namespace": "default",
  "code": "...",
  "upsert": true
}

Test Tool

POST /api/v1/mcp/tools/:id/test
Content-Type: application/json

{
  "args": { "location": "New York" }
}

CLI Commands

# List tools
fluxbase mcp tools list

# Get tool details
fluxbase mcp tools get weather_forecast

# Create tool
fluxbase mcp tools create weather_forecast --code ./weather.ts

# Update tool
fluxbase mcp tools update weather_forecast --code ./weather.ts

# Delete tool
fluxbase mcp tools delete weather_forecast

# Sync directory
fluxbase mcp tools sync --dir ./mcp-tools --namespace production

# Test tool
fluxbase mcp tools test weather_forecast --args '{"location": "NYC"}'

# Resources
fluxbase mcp resources list
fluxbase mcp resources create analytics --uri "fluxbase://custom/analytics" --code ./analytics.ts
fluxbase mcp resources sync --dir ./mcp-resources

Security

Deno Sandbox

Custom tools run in a sandboxed Deno environment with explicit permissions:

• Network (allow_net): Required for external API calls
• Environment (allow_env): Access to environment variables and secrets
• Read (allow_read): File system read access
• Write (allow_write): File system write access

Scopes

Tools require the execute:custom scope plus any additional scopes you specify. Users must have these scopes to invoke the tool.

Secrets

Access secrets securely via context.secrets.get("SECRET_NAME"). Secrets are:

• Encrypted at rest
• Never exposed in logs
• Only available when allow_env is enabled

Integration with Chatbots

Custom tools are automatically available to chatbots. Configure which tools a chatbot can use:

my_chatbot.ts

/**
 * @fluxbase:mcp-tools custom:check_order_status,query_table
 */
export default `You are a customer service assistant.`;

Tool Naming Format

Tools use a colon-separated naming format to distinguish custom tools from built-in tools and to include namespace information:

• Default namespace: custom:{name} (e.g., custom:check_order_status)
• Named namespace: custom:{namespace}:{name} (e.g., custom:production:check_order_status)

So check_order_status.ts in the default namespace becomes custom:check_order_status, while one in the production namespace becomes custom:production:check_order_status.

production_chatbot.ts

/**
 * @fluxbase:mcp-tools custom:production:get_inventory,custom:production:update_stock
 */
export default `You are an inventory management assistant with production access.`;

AI-Powered Tools

Custom MCP tools can leverage AI capabilities via utils.ai to create intelligent, context-aware functionality.

AI Chat Completions

smart_support.ts

// @fluxbase:description Analyze customer support ticket and suggest response
// @fluxbase:allow-net

export async function handler(
  args: { ticket_id: string },
  fluxbase,
  fluxbaseService,
  utils
) {
  // Get the support ticket
  const { data: ticket } = await fluxbase
    .from("support_tickets")
    .select("subject, description, customer_email")
    .eq("id", args.ticket_id)
    .single()
    .execute();

  if (!ticket) {
    return {
      content: [{ type: "text", text: "Ticket not found" }],
      isError: true
    };
  }

  // Use AI to analyze and generate response
  const response = await utils.ai.chat({
    messages: [
      {
        role: "system",
        content: "You are a helpful customer support assistant. Analyze the ticket and suggest a professional response."
      },
      {
        role: "user",
        content: `Subject: ${ticket.subject}\n\nDescription: ${ticket.description}`
      }
    ],
    maxTokens: 500,
    temperature: 0.7
  });

  return {
    content: [{
      type: "text",
      text: `Suggested response for ticket ${args.ticket_id}:\n\n${response.content}`
    }]
  };
}

AI Embeddings for Semantic Search

semantic_search.ts

// @fluxbase:description Search knowledge base using semantic similarity
// @fluxbase:allow-net

export async function handler(
  args: { query: string; limit?: number },
  fluxbase,
  fluxbaseService,
  utils
) {
  // Generate embedding for the search query
  const { embedding } = await utils.ai.embed({ text: args.query });

  // Use pgvector to find similar documents
  const { data: results } = await fluxbaseService.rpc("match_documents", {
    query_embedding: embedding,
    match_threshold: 0.7,
    match_count: args.limit || 5
  });

  return {
    content: [{
      type: "text",
      text: JSON.stringify(results, null, 2)
    }]
  };
}

Combining AI with Business Logic

analyze_orders.ts

// @fluxbase:description Analyze recent orders and provide insights
// @fluxbase:allow-net

export async function handler(
  args: { days?: number },
  fluxbase,
  fluxbaseService,
  utils
) {
  const days = args.days || 7;
  const since = new Date();
  since.setDate(since.getDate() - days);

  // Get recent orders
  const { data: orders } = await fluxbaseService
    .from("orders")
    .select("id, total, status, created_at")
    .gte("created_at", since.toISOString())
    .execute();

  // Calculate stats
  const totalRevenue = orders.reduce((sum, o) => sum + o.total, 0);
  const statusCounts = orders.reduce((acc, o) => {
    acc[o.status] = (acc[o.status] || 0) + 1;
    return acc;
  }, {});

  // Use AI to generate insights
  const response = await utils.ai.chat({
    messages: [
      {
        role: "system",
        content: "You are a business analyst. Provide brief, actionable insights."
      },
      {
        role: "user",
        content: `Analyze these ${days}-day order stats:
- Total orders: ${orders.length}
- Total revenue: $${totalRevenue.toFixed(2)}
- Status breakdown: ${JSON.stringify(statusCounts)}`
      }
    ],
    maxTokens: 300
  });

  return {
    content: [{
      type: "text",
      text: `Order Analysis (${days} days):\n\n${response.content}`
    }]
  };
}

Best Practices

1. Validate inputs - Use JSON Schema for input validation
2. Handle errors gracefully - Return isError: true with helpful messages
3. Keep tools focused - One tool, one responsibility
4. Use timeouts - Set appropriate timeouts for external API calls
5. Secure secrets - Never hardcode API keys; use the secrets system
6. Test thoroughly - Use the test endpoint before deploying to production
7. Document tools - Clear descriptions help AI use tools correctly
8. Use AI judiciously - AI calls add latency; use for complex reasoning, not simple lookups

Example: Complete Integration

check_order_status.ts

// @fluxbase:description Check the status of a customer order

export async function handler(args: { order_id: string }, fluxbase, fluxbaseService, utils) {
  const { order_id } = args;

  // Validate input
  if (!order_id || !order_id.match(/^ORD-\d+$/)) {
    return {
      content: [{ type: "text", text: "Invalid order ID format. Expected: ORD-XXXXX" }],
      isError: true
    };
  }

  // Query Fluxbase
  const { data: orders } = await fluxbase
    .from("orders")
    .select("id, status, created_at, total, items")
    .eq("id", order_id)
    .execute();

  if (!orders || orders.length === 0) {
    return {
      content: [{ type: "text", text: `Order ${order_id} not found` }],
      isError: true
    };
  }

  const order = orders[0];

  return {
    content: [{
      type: "text",
      text: `Order ${order.id}:
- Status: ${order.status}
- Created: ${order.created_at}
- Total: $${order.total}
- Items: ${order.items.length} item(s)`
    }]
  };
}

Deploy and test:

fluxbase mcp tools create check_order_status --code ./check_order_status.ts
fluxbase mcp tools test check_order_status --args '{"order_id": "ORD-12345"}'