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., "@Offensive360 MCP Server Scan this repo with Offensive360 and fix the critical findings"
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.
Run Offensive360 SAST scans from inside your AI assistant. This Model Context Protocol server gives Claude Code, Claude Desktop, Cursor, and any other MCP client two tools:
Tool | What it does |
| Zips a local directory, runs a full SAST scan (60+ languages, taint/data-flow analysis), returns findings with file/line, severity, and fixes |
| Queue position of a running scan |
Ask your assistant things like "scan this project with Offensive360 and fix the criticals" — it scans, reads the findings, and starts patching.
You need an Offensive360 External scan token:
Open-source / public repos: free — request one at offensive360.com/free-for-open-source
Commercial: any admin of your instance can create one under Settings → Tokens
claude mcp add offensive360 \
-e O360_URL=https://sast.offensive360.com \
-e O360_TOKEN=<your-token> \
-- npx -y o360-mcp{
"mcpServers": {
"offensive360": {
"command": "npx",
"args": ["-y", "o360-mcp"],
"env": {
"O360_URL": "https://sast.offensive360.com",
"O360_TOKEN": "<your-token>"
}
}
}
}O360_URL can point at your own on-premise or air-gapped instance — the server
talks only to the instance you configure.
Related MCP server: Security-Use MCP Server
Scans are synchronous; typical duration is 1–5 minutes depending on codebase size.
The default client timeout is 900s (timeout_seconds parameter to override).
Common junk directories (node_modules, .git, dist, …) are excluded from the
upload automatically; add more via the exclude parameter.
Findings are also visible in your Offensive360 dashboard with full data-flow traces.
Requires Node 18+.
One platform for SAST, DAST, MAST, SCA, malware & binary analysis, and license compliance — flat pricing, cloud or fully air-gapped on-premise. offensive360.com · Book a demo
Run an Offensive360 SAST scan on a local directory (60+ languages, taint/data-flow analysis). Uploads the directory as a zip, waits for the scan, and returns the findings with file/line, severity, and remediation guidance. Typical duration: 1–5 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path of the directory to scan | |
| exclude | No | Extra directory names to exclude from the upload | |
| project_name | No | Project name in the Offensive360 dashboard (default: directory name) | |
| timeout_seconds | No | Max seconds to wait (default 900) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses the upload, waiting, and returning of findings, plus typical duration, which gives a good sense of the operation. It does not mention potential side effects like data being sent to Offensive360 cloud or what happens on timeout, but the main behavior is transparent.
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?
Three sentences, front-loaded with the primary action, and each sentence adds value: what it does, how it works, and expected duration. There is no redundancy or irrelevant detail.
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 description covers the tool's purpose, process, output format, and timing, which is sufficient for a scanning tool. Given the schema fully documents parameters and the description explains the workflow, it is quite complete. It could optionally mention the sibling tool for status checks, but that's not essential.
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?
All four parameters are already described in the schema (path, exclude, project_name, timeout_seconds), so the description doesn't need to add much. The description itself does not elaborate on parameter formats or default behavior beyond what the schema provides, so it meets the baseline without adding extra value.
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 runs an Offensive360 SAST scan on a local directory, listing capabilities (60+ languages, taint/data-flow analysis) and expected output (findings with file/line, severity, remediation). It distinguishes from sibling o360_scan_status by describing the scan-trigger behavior rather than status checking.
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 gives context on what the tool does (uploads zip, waits, returns findings) and typical duration, which implies it's for running a full scan. However, it does not explicitly mention when to use this tool versus o360_scan_status, nor any prerequisites or exclusions for using this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the queue position of an Offensive360 scan started with o360_scan_path. Returns 0 when the scan is actively processing, a positive number for its queue position, and -1 when no scan is queued or running under that name (finished or unknown).
| Name | Required | Description | Default |
|---|---|---|---|
| project_name | Yes | The project name used when the scan was started |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing the exact return contract (0, positive number, -1) and what -1 represents (finished or unknown). While it doesn't mention error cases or side effects, the read-only nature of a status check is implied and the key behaviors are transparent.
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 concise sentences, front-loaded with the core action, and contains zero redundant information. Every word contributes to understanding the tool's function and behavior.
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 one-parameter status tool with no output schema, the description is fully complete: it defines the input, the workflow context, and the exact meaning of all possible return values. There are no gaps in what the agent needs to invoke and interpret this tool correctly.
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 schema already describes project_name, but the description adds that it is the name 'used when the scan was started' via o360_scan_path, linking the parameter to its origin and adding practical context beyond the schema.
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 states a specific verb ('Check'), resource ('queue position of an Offensive360 scan'), and scope, and it explicitly references the sibling tool o360_scan_path, distinguishing this as the status-check follow-up. It also clearly explains the meaning of return values, making the purpose unambiguous.
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 implicitly guides usage by stating scans are 'started with o360_scan_path', indicating this tool should be used afterward to check status. It does not explicitly state exclusions or alternatives, but for a single-sibling workflow the context is clear.
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.
v1.0.1o360_scan_patho360_scan_statusThe two tools have clearly distinct purposes: one initiates a scan and another checks its queue status. There is no overlap or ambiguity in their functions.
Both tools follow the same 'o360_scan_' prefix followed by a noun, creating a consistent and predictable naming pattern.
With only two tools, the set feels thin. The scope is narrowly focused on scanning and status, but the count is at the borderline of being too minimal.
The set covers initiating a scan and checking status, but lacks operations like retrieving results for finished scans, cancelling scans, or listing past scans. This creates a significant gap for users who need to manage scans over time.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Zero-config MCP security scanner for AI-generated apps. 25K+ vulnerability patterns.
Generate SBOMs, scan vulnerabilities, and analyze dependencies from local projects or Git repos.
AI pentesting: run scans, triage vulnerabilities, review PRs, manage schedules and assets.
Deep security scans of repos you own from your editor: dependency CVEs, SAST, git-history secrets.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/offensive360/mcp-server'
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