POMA Grill MCP server for AI agents and assistants

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with POMA Grill MCP or direct API to ingest documents, index content, retrieve prompt-ready context, and manage project scopes through natural language.

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Api Key

POMA Grill is a project-scoped context engine for ingesting and indexing documents. Use it to manage indexed content and retrieve prompt-ready context for LLMs.

17 Tools

Try POMA Grill now

Type what you want done — sign in and watch it run live in the Tool Router playground.

TOOL ROUTER PLAYGROUND
POMA Grill
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TOOLS

Supported Tools

Every POMA Grill action and event your agent gets out of the box.

Cancel Bucket Ingest Task

Irreversibly cancel a non-terminal POMA Grill bucket-ingest task.

Cancel Ingest Job

Request best-effort cancellation and removal of an ingestion job.

Delete Document

Delete one selected indexed document and its vectors.

Get Bucket Ingest Task

Get the current state, progress, processing rate, credential status, and recent failures for a POMA Grill bucket-ingest task.

Get Document

Get metadata for one indexed document by its document ID.

Get Ingest Job

Get an ingestion job's current status and usage; poll this after Ingest Document until status is done, failed, or blocked.

Ingest Document

Upload one file or provide one public remote URL for asynchronous indexing, and return the ingestion job ID to poll with Get Ingest Job.

List Bucket Ingest Failures

Return one page of failed items for a bucket ingest task, ordered by item ID, with an opaque cursor for the next page.

List Documents

List one page of document metadata from the connected project.

Pause Bucket Ingest Task

Pause a non-terminal bucket ingest task in paused_user state.

Preview Bucket Ingest

Validate S3 or GCS credentials and preview matching source objects without creating a bucket-ingest task.

Resume Bucket Ingest Task

Resume a bucket ingest task paused by the user, restoring its pre-pause state and waking processing for the next scheduler tick.

Retry Bucket Ingest Failures

Retry all or selected failed items in a bucket ingest task that is in done_with_errors or materialising.

Search Documents

Search indexed content across the project or within one document, returning either prompt-ready context or structured matching chunks.

Submit Bucket Ingest

Create an asynchronous task to ingest matching objects from an S3 or GCS bucket into POMA Grill, optionally delivering results to another bucket.

Update Bucket Ingest Credentials

Replace and validate the source credentials for an active bucket ingest task.

Update Document Attributes

Set, change, or remove selected typed attributes on one indexed document.

SETUP GUIDE

Connect POMA Grill MCP Tool with your Agent

1

Install Composio

typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
Install the Composio SDK for Python or TypeScript
2

Initialize Client and Create Tool Router Session

typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
Import and initialize the Composio client, then create a Tool Router session for POMA Grill
3

Connect to AI Agent

typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
Use the MCP server with your AI agent (Anthropic Claude or Mastra)
SETUP GUIDE

Connect POMA Grill API Tool with your Agent

1

Install Composio

typescript
npm install @composio/openai
Install the Composio SDK
2

Initialize Composio and Create Tool Router Session

typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
Import and initialize Composio client, then create a Tool Router session
3

Execute POMA Grill Tools via Tool Router with Your Agent

typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
Get tools from Tool Router session and execute POMA Grill actions with your Agent

Why Use Composio?

AI Native POMA Grill Integration

  • Supports both POMA Grill MCP and direct API based integrations
  • Structured, LLM-friendly schemas for reliable tool execution
  • Rich coverage for reading, writing, and querying your POMA Grill data

Managed Auth

  • Built-in API key handling with secure storage
  • Central place to manage, scope, and revoke POMA Grill access
  • Per user and per environment credentials instead of hard-coded keys

Agent Optimized Design

  • Tools are tuned using real error and success rates to improve reliability over time
  • Comprehensive execution logs so you always know what ran, when, and on whose behalf

Enterprise Grade Security

  • Fine-grained RBAC so you control which agents and users can access POMA Grill
  • Scoped, least privilege access to POMA Grill resources
  • Full audit trail of agent actions to support review and compliance

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