Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-greynoise check IP 8.8.8.8"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for the GreyNoise API — check if IP addresses are internet background noise or potentially targeted attacks.
npx mcp-greynoiseThat's it. Works out of the box with 10 lookups/day (no API key needed).
Related MCP server: Yunaq GAC MCP Server
GreyNoise collects and analyzes internet-wide scan traffic. It tells you:
Noise: Is this IP mass-scanning the internet? (botnets, researchers, crawlers)
RIOT: Is this IP a known benign service? (Google, Cloudflare, Microsoft, etc.)
Classification: Malicious, benign, or unknown
When you see suspicious traffic in your logs:
GreyNoise Result | Interpretation |
NOISE + Malicious | Background attack traffic (scanners, botnets) — likely untargeted |
NOISE + Benign | Security researchers, search crawlers — usually safe |
RIOT | Known good service (CDN, DNS, cloud) — almost certainly benign |
NOT NOISE | ⚠️ This IP is NOT mass-scanning — traffic may be targeted at you |
The "NOT NOISE" case is often the most important — it suggests someone is specifically interested in your systems.
Example output from check_ip:
IP: 51.91.185.74
Classification: MALICIOUS
Noise: YES - This IP has been observed scanning the internet
RIOT: NO - Not a known benign service IP
Last Seen: 2024-01-15
Details: https://viz.greynoise.io/ip/51.91.185.74
--- Interpretation ---
🚨 This IP is actively scanning the internet and classified as MALICIOUS.
Likely a scanner, botnet, or threat actor.npm install -g mcp-greynoisenpx mcp-greynoisegit clone https://github.com/nickjlucker/mcp-greynoise.git
cd mcp-greynoise
npm install
npm run build
node build/index.jsAdd to your claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"greynoise": {
"command": "npx",
"args": ["mcp-greynoise"],
"env": {
"GREYNOISE_API_KEY": "your-api-key-here"
}
}
}
}Variable | Required | Description |
| No | API key for higher rate limits (50/day vs 10/day) |
Get a free API key at viz.greynoise.io/signup.
⚠️ Never commit API keys. See
.env.examplefor the recommended setup.
check_ipCheck a single IP address against GreyNoise.
Input:
ip (string): IPv4 address to check
check_ipsCheck multiple IP addresses in one call (max 10).
Input:
ips (string[]): Array of IPv4 addresses
Example output:
=== Results ===
8.8.8.8: RIOT (benign service) [Google]
51.91.185.74: NOISE - MALICIOUS
192.168.1.1: NOT NOISE (potentially targeted)
--- Legend ---
RIOT: Known benign service (CDN, DNS, etc.)
NOISE: IP is mass-scanning the internet
NOT NOISE: IP is NOT mass-scanning (traffic may be targeted)greynoise://statusReturns API status and rate limit information.
Tier | Daily Lookups |
Unauthenticated | 10 |
Free account | 50 |
Paid plans | Higher |
Rate limits are shared between API calls and the GreyNoise Visualizer.
This server:
Only reads from the GreyNoise API (no scanning, no exploitation)
Does not store any data beyond the current request
Does not transmit your API key anywhere except to GreyNoise
Performs reputation/telemetry enrichment only
Your API key is passed via environment variable and never logged.
SOC Triage: Quickly determine if alert IPs are background noise or targeted
Incident Response: Identify if attacker IPs are mass-scanners or focused threats
Threat Hunting: Find IPs in your logs that aren't mass-scanners (potentially targeted)
Log Analysis: Reduce false positives by filtering out known scanners
# Install dependencies
npm install
# Run in development mode
npm run dev
# Build for production
npm run build
# Run built version
npm startMIT
Check if an IP address is internet background noise or potentially targeted activity. GreyNoise tracks IPs that mass-scan the internet. If an IP is 'noise', traffic from it is likely untargeted scanning. If it's NOT noise, traffic may be targeted at you specifically. RIOT dataset contains known-good IPs (CDNs, DNS servers, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | IPv4 address to check (e.g., 8.8.8.8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It explains the two classification outcomes (noise vs. targeted, and RIOT good IPs) but does not specify the actual output format, fields, or error behavior. The conceptual outline is helpful but lacks concrete details.
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 concise with four sentences, front-loading the core purpose. Every sentence adds value: it defines the action, explains the background, and clarifies interpretation. No redundant or irrelevant content.
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?
For a simple lookup tool, the description covers the reasoning behind the classification, but it lacks explicit mention of the return format or fields. The existence of a sibling ('check_ips') suggests batch capability is not clarified, which could confuse an agent. The description is adequate but not fully complete.
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?
There is one parameter with 100% schema description coverage (example provided). The tool description adds conceptual context about what the IP is checked against, but does not add new constraints or semantic details beyond the schema. Baseline 3 is appropriate.
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 checks if an IP is internet background noise or potentially targeted activity, using GreyNoise and RIOT datasets. It provides a specific verb and resource. However, it does not explicitly differentiate from the sibling tool 'check_ips', which likely handles multiple IPs.
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 explains the context (security analysis, noise vs. targeted) but does not provide explicit guidance on when to use this tool over 'check_ips' or any exclusion criteria. The use case is implied but not directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check multiple IP addresses against GreyNoise in one call. Useful for triaging a list of suspicious IPs from logs or alerts. Rate limits apply (50/day for free tier).
| Name | Required | Description | Default |
|---|---|---|---|
| ips | Yes | Array of IPv4 addresses to check (max 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses rate limits (50/day for free tier) and the batch nature, but does not mention idempotency, error behavior, or whether the tool is read-only. This leaves gaps in the agent's understanding of side effects.
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 two sentences, each earning its place: the first states the action and target, the second provides a use case and a critical constraint (rate limits). No redundancy or excess.
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?
The tool has 1 parameter with full schema coverage, but no output schema. The description does not explain what the tool returns (e.g., risk scores, noise classification), which is essential for an agent to interpret results. It also lacks authentication requirements or explicit differentiation from 'check_ip', leaving the agent slightly under-informed.
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 new information about the 'ips' parameter beyond what the schema already provides (array of strings, max 10, min 1). It does not clarify IP format or validation rules.
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 'Check' and the resource 'multiple IP addresses against GreyNoise'. It also distinguishes itself from the sibling tool 'check_ip' by emphasizing batch capability ('in one call'), leaving no ambiguity about its purpose.
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 provides clear context: 'useful for triaging a list of suspicious IPs from logs or alerts.' It also mentions rate limits. While it does not explicitly contrast with 'check_ip' or state when not to use it, the use case is well-defined and implicitly differentiates from a single-IP check.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0check_ipcheck_ipsThe two tools have clearly distinct purposes: one checks a single IP address, the other checks multiple IPs in bulk. An agent can easily distinguish between them based on the singular/plural naming and the explicit descriptions.
Both tools follow the consistent pattern of 'check_' followed by a noun indicating the input type ('ip' vs 'ips'). The naming is predictable and uses consistent snake_case throughout.
With only two tools, the server feels minimal for its domain. While both tools are necessary and well-scoped, a more comprehensive IP intelligence server would typically benefit from additional tools (e.g., contextual enrichment, history). The count is borderline thin.
The tool set covers the basic use case of checking IPs against GreyNoise data, both individually and in bulk. However, it lacks other common operations like fetching detailed threat context, searching by tags or queries, or retrieving known-good RIOT entries specifically. Notable gaps exist for a more complete threat intelligence surface.
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If you are the server author, to access and configure the admin panel.
GreyNoise Community MCP — internet scanner classification (free tier with key)
Free no-key IP intelligence: geolocation, VPN detection, DNS, WHOIS, blacklists, breach checks
Enrich and search people and companies, resolve identities, and enrich IP addresses.
Free IPv4 lookups against a distributed attacker-observation corpus.
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