# MCP AI Agent Research Brief Tool (`glowing_glove/mcp-ai-agent-research-brief-tool`) Actor

Gives AI agents a clean research tool that turns web sources into a decision-ready answer, evidence, recommended actions, and gaps.

- **URL**: https://apify.com/glowing\_glove/mcp-ai-agent-research-brief-tool.md
- **Developed by:** [Ushba Khan](https://apify.com/glowing_glove) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $18.00 / 1,000 agent research briefs

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## MCP AI Agent Research Brief Tool

Gives AI agents a clean research tool that turns web sources into a decision-ready answer, evidence, recommended actions, and gaps.

### What it does

This tool gives AI agents a clean research step. It reads source pages, extracts useful evidence, and returns a concise answer the agent can use before writing, recommending, triaging, or planning.

### Best use cases

- AI sales or consulting research
- Product and market discovery
- Source-backed recommendations
- Preparing agent context before a larger workflow

### Input

The main input is a plain-language research prompt. Add URLs in the prompt or in the Source URLs field. Optional fields let the agent describe the decision context and desired research depth.

### Output

The dataset contains only useful agent results:

- Answer
- Key findings
- Source evidence
- Recommended agent actions
- Open questions
- Confidence

### Pricing

Pay per agent research brief: $0.018.

### Notes

The tool is designed for MCP clients and AI-agent workflows that need structured, source-backed context instead of raw page text.

# Actor input Schema

## `prompt` (type: `string`):

Tell the tool what to research and what decision or workflow output you need. URLs can be included here.

## `sourceUrls` (type: `array`):

Optional URLs to analyze. URLs in the prompt are automatically used too.

## `maxSources` (type: `integer`):

Maximum number of source URLs to analyze.

## `decisionContext` (type: `string`):

The decision the agent is trying to make after this research.

## `depth` (type: `string`):

Controls how detailed the brief should be.

## Actor input object example

```json
{
  "prompt": "Research https://apify.com and explain what an AI automation consultant should know before recommending Apify MCP tools to a client.",
  "maxSources": 3,
  "decisionContext": "recommend the next automation step",
  "depth": "standard"
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "prompt": "Research https://apify.com and explain what an AI automation consultant should know before recommending Apify MCP tools to a client."
};

// Run the Actor and wait for it to finish
const run = await client.actor("glowing_glove/mcp-ai-agent-research-brief-tool").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "prompt": "Research https://apify.com and explain what an AI automation consultant should know before recommending Apify MCP tools to a client." }

# Run the Actor and wait for it to finish
run = client.actor("glowing_glove/mcp-ai-agent-research-brief-tool").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "prompt": "Research https://apify.com and explain what an AI automation consultant should know before recommending Apify MCP tools to a client."
}' |
apify call glowing_glove/mcp-ai-agent-research-brief-tool --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,glowing_glove/mcp-ai-agent-research-brief-tool"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/KXwdFSfJzICsjGmaR/builds/hBXZIduCKQW9pkcAt/openapi.json