MCP Agent Sentinel is a proactive AI intelligence server that monitors diverse sources (ArXiv, GitHub, SDK release notes, web) to deliver real-time breaking change alerts and curated AI news into your agent's context window.
get_latest_news: Retrieve the latest AI news, releases, and breaking changes filtered by category (all, models, research, dev_tools, business), persona (dev, product, investor, creator), timeframe (24h, 7d, 30d), and result limit (1–50).
search_ai_news: Perform real-time full-text semantic search across the curated live feed using keywords or AI model names (e.g., "Claude 3.7", "FastMCP"), with optional persona-based filtering.
get_niche_digest: Generate a condensed executive briefing for a specific sub-niche (e.g., "Agentic Frameworks", "Local LLMs") in markdown or json format, tailored to a selected persona.
get_sources_status: Check the live health, last execution timestamp, and processing metrics of all active scrapers and data sources.
Persona-based filtering tailors results to: dev (SDK updates, code), product (pricing, benchmarks), investor (research, adoption trends), and creator (trending repos, viral tools).
Monitors arXiv for latest research papers and provides tools to search and retrieve technical news and breaking changes.
Monitors GitHub repositories for trending projects, releases, and breaking changes, providing search and news retrieval tools.
Monitors Google SDK release notes for deprecations and updates, providing proactive intelligence.
Monitors OpenAI SDK release notes for breaking changes and new features, delivering real-time alerts.
mcp-agent-sentinel)Real-time Proactive Sentinel & Breaking-Change Intelligence Server for Autonomous AI Agents
Compatible with Cursor IDE, Hermes, Claude Desktop, LangChain, AutoGen, and Windsurf
Most AI agents rely on reactive search APIs (like Tavily or Exa) where the agent has to guess what to search for. If an SDK introduces a breaking change or deprecates an API parameter, your agent won't know until your production code breaks.
MCP Agent Sentinel is a proactive context server that continuously monitors ArXiv, GitHub Repositories, SDK Release Notes (MCP, OpenAI, Anthropic, Google), and Web sources. It injects clean, pre-classified intelligence and breaking change alerts directly into your agent's context window before issues occur.
Related MCP server: agent immune
Tool | Description | Key Parameters |
🚨 | Real-time technical news, releases & breaking change alerts |
|
🔍 | Fast full-text semantic search on the curated live feed |
|
📊 | Executive markdown briefing for sub-niches (e.g. AI Engineering) |
|
📡 | Live health, timestamp & processing metrics of all scrapers | N/A |
Data is automatically indexed and served according to consumer personas:
🛠️ dev: Code diffs, breaking changes, SDK updates (@modelcontextprotocol/sdk), deprecations & bug fixes.
📊 product: Pricing matrices, token efficiency, LLM benchmarks & feature availability.
📈 investor: Frontier ArXiv research papers, agentic framework adoption & cloud distribution deals.
📣 creator: Trending GitHub repos, viral AI tools & hooks for newsletters/youtube.
Connect directly to the hosted server without running local Node.js processes:
Smithery Server URL: https://mcp.smithery.run/rmicael
{
"mcpServers": {
"mcp-agent-sentinel": {
"url": "https://mcp.smithery.run/rmicael"
}
}
}Locate your Claude Desktop configuration file:
🪟 Windows: %APPDATA%\Claude\claude_desktop_config.json
🍎 macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Add mcp-agent-sentinel:
{
"mcpServers": {
"mcp-agent-sentinel": {
"command": "npx",
"args": ["-y", "mcp-agent-sentinel@latest"]
}
}
}Add under Cursor Settings -> Features -> MCP Servers:
Name: mcp-agent-sentinel
Type: command
Command: npx -y mcp-agent-sentinel@latest
Add to your ~/.gemini/config/mcp_config.json or ~/.hermes/config/mcp_config.json:
{
"mcpServers": {
"mcp-agent-sentinel": {
"command": "npx",
"args": ["-y", "mcp-agent-sentinel@latest"]
}
}
}# 1. Clone repository
git clone https://github.com/rmikael7/mcp-agent-sentinel.git
cd mcp-agent-sentinel
# 2. Install & Build
npm install
npm run build
# 3. Add to your MCP config using the built file:
# command: "node"
# args: ["/path/to/mcp-agent-sentinel/dist/index.js"]Test the live feed parser right from your terminal:
# Interactive Persona Visualizer
npm run demo
# Verify Cursor/IDE Setup
npm run verify-cursorRepository: github.com/rmikael7/mcp-agent-sentinel
Glama.ai Hub: glama.ai/mcp/servers/rmikael7/mcp-agent-sentinel
Smithery Cloud: smithery.ai/server/rmicael
Maintainers: Agent Principal Core Team
License: MIT License
Gera um briefing/resumo executivo condensado e estruturado para um sub-nicho específico.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Nome do nicho ou sub-nicho (ex: "Agentic Frameworks", "Local LLMs", "Multimodal RAG") | |
| format | No | Formato de saída desejado (markdown ou json) | markdown |
| persona | No | Persona do consumidor | dev |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks behavioral traits such as authentication needs, rate limits, or side effects. It only states the output type, which is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the purpose, making it concise. However, it lacks structure or additional details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is incomplete. It does not explain the output format or provide enough context for an agent to fully understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'gera' (generates) and the resource 'briefing/resumo executivo condensado e estruturado para um sub-nicho específico', differentiating it from the sibling tool 'get_sources_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining a niche digest but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retorna o status atual de funcionamento e última execução dos scrapers e fontes de dados.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the tool returns status and last execution, implying a read-only operation, but does not explicitly disclose behavioral traits like side effects, permissions, or rate limits. It is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the purpose. No redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema), the description fully captures its functionality: returning status and last execution. It is complete for the intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is trivially 100%. Per rubric, baseline is 4. Description adds no parameter-specific info, which is acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the current operational status and last execution of scrapers and data sources (specific verb+resource). It distinguishes from the only sibling tool, get_niche_digest, which likely concerns a different domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not indicate when to use this tool versus alternatives, nor does it mention prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have clearly distinct purposes: one generates executive briefings for niches, the other returns source status. No overlap.
Both tools use the consistent verb_noun pattern with 'get_' prefix, following a predictable convention.
With only 2 tools, the server feels severely under-scoped for a name implying 'Agent Sentinel' (monitoring agents). Expected more tools for a monitoring/management domain.
The tool surface is very thin: missing essential monitoring operations (e.g., list agents, pause/resume, view alerts). The niche digest tool seems out of place for a sentinel.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
API governance for AI agents. Detects breaking changes, scores blast radius, blocks unsafe calls.
Watchdog for unattended AI agents: alerts, evidence checks and a verifiable proof per run.
The WAF for agents. Pattern-based + heuristic firewall scans prompts, RAG documents, tool argume...
npm & PyPI freshness for AI agents: latest version, deprecations, dated breaking-change diffs.
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