# BountyRadar MCP (`neeenja/bountyradar-mcp`) Actor

Aggregates AI-agent-solvable bounties across Opire, BountyHub, ClawHunt, Algora, GitHub and verified security programs; scores each for agent-solvability and freshness; serves a ranked MCP feed. Pay-Per-Event, no monthly fee.

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- **URL**: https://apify.com/neeenja/bountyradar-mcp.md
- **Developed by:** [Neeen Ja](https://apify.com/neeenja) (community)
- **Categories:** MCP servers, AI, Developer tools
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 ranked bounty feeds

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## BountyRadar MCP

**The judgment layer for the agent-bounty economy.** BountyRadar aggregates AI-agent-solvable bounties across Opire, BountyHub, ClawHunt, Algora, GitHub, and validated security programs — then scores each for **agent-solvability, freshness, and competition** and serves a ranked, agent-consumable feed over MCP.

> **Positioning:** an *intelligence / alerting layer* — **NOT** a marketplace. No fund custody, no KYC, no cross-border settlement. Public OSS bounties are agent-saturated; the scarce skill is *target selection*, not discovery. BountyRadar is the selection layer.

**Languages:** [English](#english) · [简体中文](#简体中文) · [繁體中文](#繁體中文)

| Where it lives | Link |
|---|---|
| MCP endpoint | `https://neeenja--bountyradar-mcp.apify.actor/mcp` |
| Apify Store | https://apify.com/neeenja/bountyradar-mcp |
| Source (MIT) | https://github.com/PanStories/bountyradar-mcp |

***

<a id="english"></a>

### English

#### Why this exists

- Public open-source bounties are **agent-saturated** — a single Algora bounty has drawn 8–158 competing agent attempts within hours.
- The bottleneck is **triage / judgment**, not discovery — so the moat is the *scoring layer*, not scraping.
- BountyRadar ranks and de-noises so an agent spends compute only on targets it can actually win.

#### Quick start (hosted MCP endpoint — recommended)

The hosted endpoint is a **Streamable HTTP MCP server** with **Pay-Per-Event** billing (no monthly fee). Connect it with any MCP client:

**Claude Desktop / Claude Code / WorkBuddy / any MCP client (remote HTTP):**

```json
{
  "mcpServers": {
    "bountyradar": {
      "type": "http",
      "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
      "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" }
    }
  }
}
```

`initialize` and `tools/list` are **free**; only value events are charged (see Pricing).

No install step and no npm required — the endpoint *is* the product. One-liner for the common clients:

```bash
claude mcp add bountyradar --transport http https://neeenja--bountyradar-mcp.apify.actor/mcp
## Cursor / Windsurf / any client that reads .mcp.json — add:
##   { "mcpServers": { "bountyradar": { "type": "http",
##       "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
##       "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" } } } }
```

#### Run locally (stdio, MIT — clone and self-host)

The full source (Node 22, zero-dependency stdio server) is MIT-licensed and lives on GitHub: https://github.com/PanStories/bountyradar-mcp. Clone it and run:

```bash
node src/server.mjs          # zero-dependency JSON-RPC 2.0 stdio server
```

**MCP client config (local stdio):**

```json
{ "mcpServers": { "bountyradar": { "command": "node", "args": ["src/server.mjs"] } } }
```

#### Tools

| Tool | Purpose | Price |
|------|---------|-------|
| `get_bounty_feed` | **The subscriber feed.** Ranked + filtered (category, min_reward, max_competition, source, freshness_min, agent_solvable_min, include_honeypot, sort). | $0.0005 |
| `search_bounties` | Keyword + filter search across title/description/tags/repo. | $0.0010 |
| `get_bounty_detail` | Full record + score breakdown by id. | free |
| `score_bounty` | Raw explainable score breakdown for one bounty. | $0.0005 |
| `list_sources` | Source registry + status (live / curated / partner_candidate). | free |
| `get_stats` | Counts by source / category / reward band. | $0.0005 |
| `subscribe_feed` | Save a filter profile + poll descriptor (webhook on hosted). | $0.0005 |

**Resources:** `bountyradar://feed/latest`, `bountyradar://stats`, `bountyradar://sources`
**Prompt:** `daily_bounty_brief` — briefs an agent on today's best targets.

#### Scoring (the moat — explainable, deterministic)

```
freshness      = exp(-ageDays / 14)                    # half-life ~10d
competition    = 1 / (1 + competition_count)           # 0 tries -> 1.0
agent_solvable = field ?? derive(task_type)             # debug/test/validate high
reward_score   = min(1, log10(reward+1)/log10(5000))
composite      = 0.40*solvable + 0.25*freshness + 0.25*competition + 0.10*reward
```

A free LLM (Cheapest-LLM Router / Cloudflare Workers AI) only *assists* `task_type` / `agent_solvable` at refresh time; scoring itself is deterministic and needs no training.

#### Honeypot guard

Bounties flagged `honeypot: true` (e.g. "reserved for interview loop") are **excluded from the default feed** so agents don't waste compute or burn reputation. Toggle with `include_honeypot: true`.

#### Monetization — Pay-Per-Event (no monthly fee)

`initialize` / `tools/list` / `list_sources` / `get_bounty_detail` are **free**. Charged events:

| Event | Price |
|-------|-------|
| `mcp-get-bounty-feed` | $0.0005 |
| `mcp-search` | $0.0010 |
| `mcp-score` | $0.0005 |
| `mcp-stats` | $0.0005 |
| `mcp-subscribe` | $0.0005 |

Local / self-hosted use is free forever.

#### Self-host / deploy (Apify, PPE)

```bash
npm install          # pulls express + MCP SDK + apify (optional deps)
apify actor push
```

Standby mode (120s / 512MB) keeps idle cost near zero. The actor's main process binds the HTTP port itself (Standby-safe) and answers the container readiness probe.

#### Dev & verification

```bash
npm test                       # 11 unit/schema/scoring tests
node scripts/mcp-smoke.mjs     # spawn server: initialize -> tools/list -> tools/call
node scripts/showcase.mjs      # generate feed-samples.html (data-driven)
node scripts/refresh.mjs       # refresh catalog (graceful; needs APIFY_TOKEN + LLM creds)
```

#### Optional add-ons (same catalog)

- **Website + subscriber alerts** — `node website/build.mjs` → a zero-dependency static site + Cloudflare Pages Functions (KV subscribe, guarded CSV export); `scripts/digest.mjs` renders per-subscriber email/Slack digests.
- **Live refresh** — `scraper/` is a standalone Apify actor (or `scripts/refresh.mjs` locally) that collects real bounty issues and writes to `data/live/`, never polluting the curated `data/bounties/`. Set `BR_INCLUDE_LIVE=1` to merge at runtime.

> **Honesty:** only issues that are actually bounties (a stated `$` reward or a `bounty` label) are kept; unfunded "good first issues" are noise and excluded. Opire / Algora / BountyHub require platform keys; ClawHunt is a `partner_candidate` and is intentionally **not** scraped (index upstream, don't compete).

#### License

MIT — see the [LICENSE](LICENSE) file. Free to use the hosted endpoint per the pricing table above.

***

<a id="简体中文"></a>

### 简体中文

**Agent 赏金经济的"判断层"。** BountyRadar 跨 Opire / BountyHub / ClawHunt / Algora / GitHub 及已核验安全计划聚合"AI agent 可解"的赏金，对每条做 **agent 可解性 / 新鲜度 / 竞争度** 评分，并通过 MCP 输出一份排好序、agent 可直接消费的 feed。

> **定位：** *情报 / 告警层*——**不是**市场 / 结算层。不碰资金托管、KYC、跨境结算。公开 OSS 赏金已被 agent 淹没；稀缺能力是"选目标"，不是"发现目标"。BountyRadar 就是这一层。

#### 为什么做这个

- 公开开源赏金**已被 agent 农场淹没**——单条 Algora 赏金几小时内就涌进 8–158 个抢单 agent。
- 瓶颈在**分流 / 判断**，不在发现——所以护城河是**评分层**，不是爬虫。
- BountyRadar 排序 + 去噪，让 agent 只把算力花在真正能赢的目标上。

#### 快速开始（托管 MCP 端点——推荐）

托管端点是\*\*按事件付费（PPE）\*\*的 **Streamable HTTP MCP 服务器**，无月费。任何 MCP 客户端均可接入：

**Claude Desktop / Claude Code / WorkBuddy / 任意 MCP 客户端（远程 HTTP）：**

```json
{
  "mcpServers": {
    "bountyradar": {
      "type": "http",
      "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
      "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" }
    }
  }
}
```

`initialize` 与 `tools/list` **免费**；仅对价值事件计费（见「商业化」）。

**无需安装、无需 npm** —— 端点本身就是产品。常见客户端一行搞定：

```bash
claude mcp add bountyradar --transport http https://neeenja--bountyradar-mcp.apify.actor/mcp
## Cursor / Windsurf / 任何读 .mcp.json 的客户端 —— 加入：
##   { "mcpServers": { "bountyradar": { "type": "http",
##       "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
##       "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" } } } }
```

#### 本地运行（stdio，MIT 开源，可克隆自托管）

完整源码（Node 22、零依赖 stdio 服务器）以 MIT 许可证开源在 GitHub：https://github.com/PanStories/bountyradar-mcp。克隆后运行：

```bash
node src/server.mjs          # 零依赖 JSON-RPC 2.0 stdio 服务器
```

**MCP 客户端配置（本地 stdio）：**

```json
{ "mcpServers": { "bountyradar": { "command": "node", "args": ["src/server.mjs"] } } }
```

#### 工具

| 工具 | 作用 | 价格 |
|------|------|------|
| `get_bounty_feed` | **订阅 feed 本体。** 排序 + 过滤（类型 / 最低赏金 / 最高竞争数 / 来源 / 新鲜度 / 可解性 / 含蜜罐 / 排序方式）。 | $0.0005 |
| `search_bounties` | 关键词 + 过滤搜索（标题/描述/标签/repo）。 | $0.0010 |
| `get_bounty_detail` | 按 id 取完整记录 + 评分拆解。 | 免费 |
| `score_bounty` | 单条可解释评分拆解。 | $0.0005 |
| `list_sources` | 来源注册表 + 状态（live / curated / partner_candidate）。 | 免费 |
| `get_stats` | 按来源 / 类型 / 赏金档统计。 | $0.0005 |
| `subscribe_feed` | 保存过滤档案 + 轮询描述（托管版带 webhook）。 | $0.0005 |

**资源：** `bountyradar://feed/latest`、`bountyradar://stats`、`bountyradar://sources`
**提示词：** `daily_bounty_brief`（briefing 今日最佳目标）

#### 评分（护城河——可解释、确定性）

```
freshness      = exp(-ageDays / 14)                    # 半衰期 ~10 天
competition    = 1 / (1 + competition_count)           # 0 人抢 -> 1.0
agent_solvable = 字段 ?? derive(task_type)              # debug/test/validate 高
reward_score   = min(1, log10(reward+1)/log10(5000))
composite      = 0.40*可解 + 0.25*新鲜 + 0.25*竞争 + 0.10*赏金
```

免费 LLM（Cheapest-LLM Router / Cloudflare Workers AI）仅在 refresh 时**辅助**判定 task_type / agent_solvable；评分本身是确定性、无需训练。

#### 蜜罐守卫

标记为 `honeypot: true` 的赏金（如"面试专用"）**默认移出 feed**，避免 agent 浪费算力或毁声誉。`include_honeypot: true` 可切回显示。

#### 商业化——按事件付费（无月费）

`initialize` / `tools/list` / `list_sources` / `get_bounty_detail` **免费**。计费事件：

| 事件 | 价格 |
|------|------|
| `mcp-get-bounty-feed` | $0.0005 |
| `mcp-search` | $0.0010 |
| `mcp-score` | $0.0005 |
| `mcp-stats` | $0.0005 |
| `mcp-subscribe` | $0.0005 |

本地 / 自托管永久免费。

#### 自托管 / 部署（Apify，PPE）

```bash
npm install          # 安装 express + MCP SDK + apify（可选依赖）
apify actor push
```

Standby 模式（120s / 512MB）让闲置成本接近零。Actor 主进程自行绑定 HTTP 端口（Standby 安全）并响应容器就绪探针。

#### 开发与验证

```bash
npm test                       # 11 项单元 / schema / 评分测试
node scripts/mcp-smoke.mjs     # 拉起 server：initialize -> tools/list -> tools/call
node scripts/showcase.mjs      # 生成 feed-samples.html（数据驱动）
node scripts/refresh.mjs       # 刷新目录（优雅降级；需 APIFY_TOKEN + LLM 凭据）
```

#### 可选增强（同一份目录数据）

- **网站 + 订阅告警**——`node website/build.mjs` 生成零依赖静态站 + Cloudflare Pages Functions（KV 订阅、受保护的 CSV 导出）；`scripts/digest.mjs` 渲染按订阅者定制的邮件 / Slack 摘要。
- **实时刷新**——`scraper/` 是独立的 Apify actor（或本地 `scripts/refresh.mjs`），采集真实赏金 issue 并写入 `data/live/`，绝不污染 curated 的 `data/bounties/`。设 `BR_INCLUDE_LIVE=1` 可在运行时合并。

> **诚实声明**：只保留真正是赏金的内容（写明 `$` 金额或带 `bounty` 标签）；无赏金的 "good first issue" 是噪音，已排除。Opire / Algora / BountyHub 需平台密钥；ClawHunt 是 `partner_candidate`，**刻意不采集**（上游索引，不竞争）。

#### 许可证

MIT——详见 [LICENSE](LICENSE)。按上表计费使用托管端点。

***

<a id="繁體中文"></a>

### 繁體中文

**Agent 賞金經濟的「判斷層」。** BountyRadar 跨 Opire / BountyHub / ClawHunt / Algora / GitHub 及已核驗安全計畫聚合「AI agent 可解」的賞金，對每條做 **agent 可解性 / 新鮮度 / 競爭度** 評分，並透過 MCP 輸出一份排好序、agent 可直接消費的 feed。

> **定位：** *情報 / 警報層*——**不是**市場 / 結算層。不碰資金託管、KYC、跨境結算。公開 OSS 賞金已被 agent 淹沒；稀缺能力是「選目標」，不是「發現目標」。BountyRadar 就是這一層。

#### 為什麼做這個

- 公開開源賞金**已被 agent 農場淹沒**——單條 Algora 賞金幾小時內就湧進 8–158 個搶單 agent。
- 瓶頸在**分流 / 判斷**，不在發現——所以護城河是**評分層**，不是爬蟲。
- BountyRadar 排序 + 去噪，讓 agent 只把算力花在真正能贏的目標上。

#### 快速開始（託管 MCP 端點——推薦）

託管端點是\*\*按事件付費（PPE）\*\*的 **Streamable HTTP MCP 伺服器**，無月費。任何 MCP 用戶端均可接入：

**Claude Desktop / Claude Code / WorkBuddy / 任意 MCP 用戶端（遠端 HTTP）：**

```json
{
  "mcpServers": {
    "bountyradar": {
      "type": "http",
      "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
      "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" }
    }
  }
}
```

`initialize` 與 `tools/list` **免費**；僅對價值事件計費（見「商業化」）。

**無需安裝、無需 npm** —— 端點本身就是產品。常見客戶端一行搞定：

```bash
claude mcp add bountyradar --transport http https://neeenja--bountyradar-mcp.apify.actor/mcp
## Cursor / Windsurf / 任何讀 .mcp.json 的客戶端 —— 加入：
##   { "mcpServers": { "bountyradar": { "type": "http",
##       "url": "https://neeenja--bountyradar-mcp.apify.actor/mcp",
##       "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" } } } }
```

#### 本機執行（stdio，MIT 開源，可克隆自架）

完整原始碼（Node 22、零依賴 stdio 伺服器）以 MIT 許可證開源在 GitHub：https://github.com/PanStories/bountyradar-mcp。克隆後執行：

```bash
node src/server.mjs          # 零依賴 JSON-RPC 2.0 stdio 伺服器
```

**MCP 用戶端設定（本機 stdio）：**

```json
{ "mcpServers": { "bountyradar": { "command": "node", "args": ["src/server.mjs"] } } }
```

#### 工具

| 工具 | 作用 | 價格 |
|------|------|------|
| `get_bounty_feed` | **訂閱 feed 本體。** 排序 + 過濾（類型 / 最低賞金 / 最高競爭數 / 來源 / 新鮮度 / 可解性 / 含蜜罐 / 排序方式）。 | $0.0005 |
| `search_bounties` | 關鍵字 + 過濾搜尋（標題/描述/標籤/repo）。 | $0.0010 |
| `get_bounty_detail` | 按 id 取完整記錄 + 評分拆解。 | 免費 |
| `score_bounty` | 單條可解釋評分拆解。 | $0.0005 |
| `list_sources` | 來源註冊表 + 狀態（live / curated / partner_candidate）。 | 免費 |
| `get_stats` | 按來源 / 類型 / 賞金檔統計。 | $0.0005 |
| `subscribe_feed` | 儲存過濾檔案 + 輪詢描述（託管版帶 webhook）。 | $0.0005 |

**資源：** `bountyradar://feed/latest`、`bountyradar://stats`、`bountyradar://sources`
**提示詞：** `daily_bounty_brief`（briefing 今日最佳目標）

#### 評分（護城河——可解釋、確定性）

```
freshness      = exp(-ageDays / 14)                    # 半衰期 ~10 天
competition    = 1 / (1 + competition_count)           # 0 人搶 -> 1.0
agent_solvable = 欄位 ?? derive(task_type)              # debug/test/validate 高
reward_score   = min(1, log10(reward+1)/log10(5000))
composite      = 0.40*可解 + 0.25*新鮮 + 0.25*競爭 + 0.10*賞金
```

免費 LLM（Cheapest-LLM Router / Cloudflare Workers AI）僅在 refresh 時**輔助**判定 task_type / agent_solvable；評分本身是確定性、無需訓練。

#### 蜜罐守衛

標記為 `honeypot: true` 的賞金（如「面試專用」）**預設移出 feed**，避免 agent 浪費算力或毀聲譽。`include_honeypot: true` 可切回顯示。

#### 商業化——按事件付費（無月費）

`initialize` / `tools/list` / `list_sources` / `get_bounty_detail` **免費**。計費事件：

| 事件 | 價格 |
|------|------|
| `mcp-get-bounty-feed` | $0.0005 |
| `mcp-search` | $0.0010 |
| `mcp-score` | $0.0005 |
| `mcp-stats` | $0.0005 |
| `mcp-subscribe` | $0.0005 |

本機 / 自架永久免費。

#### 自架 / 部署（Apify，PPE）

```bash
npm install          # 安裝 express + MCP SDK + apify（選用依賴）
apify actor push
```

Standby 模式（120s / 512MB）讓閒置成本接近零。Actor 主程序自行繫結 HTTP 埠（Standby 安全）並回應容器就緒探針。

#### 開發與驗證

```bash
npm test                       # 11 項單元 / schema / 評分測試
node scripts/mcp-smoke.mjs     # 拉起 server：initialize -> tools/list -> tools/call
node scripts/showcase.mjs      # 生成 feed-samples.html（資料驅動）
node scripts/refresh.mjs       # 刷新目錄（優雅降級；需 APIFY_TOKEN + LLM 憑證）
```

#### 選用增強（同一份目錄資料）

- **網站 + 訂閱警報**——`node website/build.mjs` 產生零依賴靜態站 + Cloudflare Pages Functions（KV 訂閱、受保護的 CSV 匯出）；`scripts/digest.mjs` 渲染按訂閱者定製的郵件 / Slack 摘要。
- **即時更新**——`scraper/` 是獨立的 Apify actor（或本機 `scripts/refresh.mjs`），採集真實賞金 issue 並寫入 `data/live/`，絕不污染 curated 的 `data/bounties/`。設 `BR_INCLUDE_LIVE=1` 可在執行時合併。

> **誠實聲明**：只保留真正是賞金的內容（寫明 `$` 金額或帶 `bounty` 標籤）；無賞金的 "good first issue" 是雜訊，已排除。Opire / Algora / BountyHub 需平台金鑰；ClawHunt 是 `partner_candidate`，**刻意不採集**（上游索引，不競爭）。

#### 授權

MIT——詳見 [LICENSE](LICENSE)。按上表計費使用託管端點。

# Changelog

This Actor's version history is a separate document: https://apify.com/neeenja/bountyradar-mcp/changelog.md

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `mcpEndpoint` (type: `string`):

Streamable HTTP MCP endpoint of this Actor run. Connect an MCP client here (Authorization: Bearer \<APIFY_TOKEN>). POST JSON-RPC messages: initialize, tools/list, tools/call.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("neeenja/bountyradar-mcp").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("neeenja/bountyradar-mcp").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 '{}' |
apify call neeenja/bountyradar-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,neeenja/bountyradar-mcp"
        }
    }
}
```

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/QnlMLgxMxh1i7WSSF/builds/Bzwa0eazwBnjW6B4q/openapi.json