# FeedBear

```json
{
  "name": "FeedBear",
  "slug": "feedbear",
  "url": "https://composio.dev/toolkits/feedbear",
  "markdown_url": "https://composio.dev/toolkits/feedbear.md",
  "logo_url": "https://logos.composio.dev/api/feedbear",
  "categories": [
    "productivity & project management"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-10-04T18:11:27.168Z"
}
```

![FeedBear logo](https://logos.composio.dev/api/feedbear)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with FeedBear MCP or direct API to collect feedback, manage feature requests, review comments, and publish changelog updates through natural language.

## Summary

FeedBear is a customer feedback and feature request platform for product teams.
It helps teams organize user ideas, comments, votes, and changelog updates in one place.

## Categories

- productivity & project management

## Toolkit Details

- Tools: 10

## Images

- Logo: https://logos.composio.dev/api/feedbear

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for FeedBear.
  - Setup:
    - Configure Api Key credentials for FeedBear.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- Summarize latest FeedBear feature requests
- Create changelog update from shipped features
- Find top customer feedback themes

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FEEDBEAR_ADD_ITEM_COMMENT` | Add Item Comment | Publish a public comment on a FeedBear item as the API key creator. This action may notify post participants. |
| `FEEDBEAR_CREATE_BOARD_ITEM` | Create Board Item | Create a feedback item on a FeedBear board without notifying users. |
| `FEEDBEAR_DELETE_BOARD_ITEM` | Delete Board Item | Permanently delete a FeedBear board item, with explicit force confirmation available for items with merged posts. |
| `FEEDBEAR_GET_BOARD_ITEM` | Get Board Item | Get the full details of one feedback item from a FeedBear board. |
| `FEEDBEAR_LIST_BOARD_ITEMS` | List Board Items | List or search feedback items on a FeedBear board, with optional status and sort filters. Returns one page and a cursor for controlled continuation. |
| `FEEDBEAR_LIST_BOARDS` | List Boards | List boards in the connected FeedBear project, optionally including private team-only boards. |
| `FEEDBEAR_LIST_CHANGELOG_ENTRIES` | List Changelog Entries | List published FeedBear changelog entries newest first. Draft and scheduled entries are excluded. |
| `FEEDBEAR_LIST_ITEM_COMMENTS` | List Item Comments | List non-spam comments on a FeedBear item, newest first, including reply relationships. Returns one page and a cursor for controlled continuation. |
| `FEEDBEAR_LIST_STATUSES` | List Statuses | List the statuses configured for the connected FeedBear project. |
| `FEEDBEAR_UPDATE_BOARD_ITEM` | Update Board Item | Update selected fields of an existing FeedBear board item without notifying voters. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK, the OpenAI provider, and the OpenAI SDK
```python
pip install composio composio_openai openai
```

```typescript
npm install @composio/core @composio/openai openai
```

#### Path 1, Step 2: Create a Composio session

Initialize Composio with the OpenAI Responses provider and create a session scoped to FeedBear
```python
import json
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
client = OpenAI()

session = composio.create(user_id="your-user-id", toolkits=["feedbear"])
tools = session.tools()
```

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

const composio = new Composio({ provider: new OpenAIResponsesProvider() });
const client = new OpenAI();

const session = await composio.create("your-user-id", { toolkits: ["feedbear"] });
const tools = await session.tools();
```

#### Path 1, Step 3: Run FeedBear tools with your agent

Send a request, execute the FeedBear tool calls through the session, and print the final answer
```python
response = client.responses.create(
    model="gpt-5.6-sol",
    tools=tools,
    input=[{"role": "user", "content": "List the latest FeedBear feature requests and summarize the top customer pain points"}],
)

while True:
    tool_calls = [o for o in response.output if o.type == "function_call"]
    if not tool_calls:
        break
    results = composio.provider.handle_tool_calls(response=response, session=session)
    response = client.responses.create(
        model="gpt-5.6-sol",
        tools=tools,
        previous_response_id=response.id,
        input=[
            {"type": "function_call_output", "call_id": call.call_id, "output": json.dumps(results[i])}
            for i, call in enumerate(tool_calls)
        ],
    )

print(response.output_text)
```

```typescript
let response = await client.responses.create({
  model: "gpt-5.6-sol",
  tools,
  input: [{ role: "user", content: "List the latest FeedBear feature requests and summarize the top customer pain points" }],
});

while (response.output.some((o) => o.type === "function_call")) {
  const outputs = await composio.provider.handleToolCalls(session, response.output);
  response = await client.responses.create({
    model: "gpt-5.6-sol",
    tools,
    previous_response_id: response.id,
    input: outputs,
  });
}

console.log(response.output_text);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK and your agent framework
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/mcp @ai-sdk/openai
```

#### Path 2, Step 2: Create a session with MCP enabled

Create a session scoped to FeedBear and read its MCP URL and headers
```python
from composio import Composio

composio = Composio()
session = composio.create(user_id="your-user-id", toolkits=["feedbear"], mcp=True)
```

```typescript
import { Composio } from "@composio/core";

const composio = new Composio();
const { mcp } = await composio.create("your-user-id", {
  toolkits: ["feedbear"],
  mcp: true,
});
```

#### Path 2, Step 3: Connect your agent to the MCP server

Pass the session's MCP URL and headers to your agent and run a FeedBear request
```python
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions, AssistantMessage, TextBlock

options = ClaudeAgentOptions(
    mcp_servers={
        "composio": {
            "type": "http",
            "url": session.mcp.url,
            "headers": session.mcp.headers,
        }
    },
    system_prompt="You are a helpful assistant with access to FeedBear tools.",
    tools=[],
    allowed_tools=["mcp__composio__*"],
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query("List the latest FeedBear feature requests and summarize the top customer pain points")
        async for message in client.receive_response():
            if isinstance(message, AssistantMessage):
                for block in message.content:
                    if isinstance(block, TextBlock):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { createMCPClient } from "@ai-sdk/mcp";
import { openai } from "@ai-sdk/openai";
import { generateText, stepCountIs } from "ai";

const client = await createMCPClient({
  transport: { type: "http", url: mcp.url, headers: mcp.headers },
});

const { text } = await generateText({
  model: openai("gpt-5.6-sol"),
  tools: await client.tools(),
  prompt: "List the latest FeedBear feature requests and summarize the top customer pain points",
  stopWhen: stepCountIs(10),
});

console.log(text);
await client.close();
```

## Why Use Composio?

### 1. AI Native FeedBear Integration

- Supports both FeedBear MCP and direct API based integrations
- Structured, LLM-friendly schemas so agents can work with feedback, feature requests, comments, and changelogs without brittle custom code
- Rich coverage for reading, writing, and querying your FeedBear product feedback data

### 2. Managed Auth

- Secure API key handling so you don't have to hard-code FeedBear credentials in your agent code
- Central place to manage, scope, and revoke FeedBear access across users and environments
- Per user and per environment credentials for safer feedback and roadmap workflows

### 3. Agent Optimized Design

- FeedBear tools are shaped for natural language tasks like summarizing customer pain points or finding top requested features
- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what feedback data was read or updated, when, and on whose behalf

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access FeedBear
- Scoped, least privilege access to FeedBear feedback, comments, requests, and changelog data
- Full audit trail of agent actions to support review, compliance, and product operations

## Use FeedBear with any AI Agent Framework

Choose a framework you want to connect FeedBear with:

None listed.

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## Frequently Asked Questions

### Do I need my own developer credentials to use FeedBear with Composio?

Yes, FeedBear requires you to configure your own API key. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)