BountyRadar MCP

Judgment layer for the agent-bounty economy. Aggregates agent-solvable bounties across Opire, BountyHub, ClawHunt, Algora and GitHub, then scores each for solvability, freshness and competition - served as a ranked, pay-per-event MCP feed. Source: https://github.com/PanStories/bountyradar-mcp

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เอกสาร

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 (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 section below.

Each client library has reference documentation the quickstart does not restate: JavaScript/TypeScript (npm install apify-client) and Python (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 · 简体中文 · 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

English

Current version: 1.0.1

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):

{
  "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:

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:

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

MCP client config (local stdio):

{ "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 (stored in server memory; webhook URL echoed, never called).	$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)

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

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 file. Free to use the hosted endpoint per the pricing table above.

简体中文

Current version: 1.0.1

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）：

{
  "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：

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：

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

MCP 客户端配置（本地 stdio）：

{ "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（仅存于服务器内存；webhook URL 只回显、绝不调用）。	$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）

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

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。按上表计费使用托管端点。

繁體中文

Current version: 1.0.1

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）：

{
  "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：

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：

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

MCP 用戶端設定（本機 stdio）：

{ "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（僅存於伺服器記憶體；webhook URL 僅回顯、絕不呼叫）。	$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）

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

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。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

{}

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

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

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

echo '{}' |
apify call neeenja/bountyradar-mcp --silent --output-dataset

MCP server setup

{
    "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/LeGvsNghNkQpxA0Au/openapi.json

BountyRadar MCP

เอกสาร

BountyRadar MCP (neeenja/bountyradar-mcp) Actor

Pricing

What's an Apify Actor?

How to integrate an Actor?

README

BountyRadar MCP

English

Why this exists

Quick start (hosted MCP endpoint — recommended)

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

Tools

Scoring (the moat — explainable, deterministic)

Honeypot guard

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

Self-host / deploy (Apify, PPE)

Dev & verification

Optional add-ons (same catalog)

License

简体中文

为什么做这个

快速开始（托管 MCP 端点——推荐）

本地运行（stdio，MIT 开源，可克隆自托管）

工具

评分（护城河——可解释、确定性）

蜜罐守卫

商业化——按事件付费（无月费）

自托管 / 部署（Apify，PPE）

开发与验证

可选增强（同一份目录数据）

许可证

繁體中文

為什麼做這個

快速開始（託管 MCP 端點——推薦）

本機執行（stdio，MIT 開源，可克隆自架）

工具

評分（護城河——可解釋、確定性）

蜜罐守衛

商業化——按事件付費（無月費）

自架 / 部署（Apify，PPE）

開發與驗證

選用增強（同一份目錄資料）

授權

Changelog

Actor input Schema

Actor input object example

Actor output Schema

mcpEndpoint (type: string):

API

JavaScript example

Python example

CLI example

MCP server setup

OpenAPI specification

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

`mcpEndpoint` (type: `string`):