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shadoprizm

Cyber Lens AI MCP Server

by shadoprizm

scan_repository

Scan any public GitHub, GitLab, or Bitbucket repository to uncover exposed secrets, vulnerable dependencies, suspicious code patterns, and trust issues. Get a scan ID to retrieve detailed findings.

Instructions

Scan a public repository for secrets, dependency risks, and suspicious code patterns.

Uses the live CyberLens cloud repository scanner for:

  • Public GitHub, GitLab, and Bitbucket repositories

  • Supported CLAUDE Hub or direct ZIP download URLs

Checks for:

  • Exposed secrets and credentials

  • Vulnerable dependencies

  • Suspicious or risky code behavior

  • Trust posture and repository hygiene signals

  • Artifact and package reputation issues

Returns a scan ID that can be used with get_scan_results to retrieve findings. If no account is connected yet, the MCP server will open the CyberLens browser flow automatically and then continue the scan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoSurface is faster. Deep runs a broader repository analysis.surface
branchNoOptional branch name to scan. Defaults to the repository's default branch.
repository_urlYesThe repository URL to scan (for example https://github.com/owner/repo)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and covers important behavior: it uses a live cloud scanner, supports only public repositories, returns a scan ID, and will trigger an account connection flow if needed. It does not mention quota usage or rate limits, but that is not essential for a first call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a lead sentence, bulleted source and finding lists, and a concise closing statement about behavior. It uses space efficiently and front-loads the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the essential invocation context: acceptable inputs, what the scan checks, what is returned, and what happens when no account is connected. It is complete enough for an agent to call the tool and know the next step.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already explains all three parameters sufficiently. The description adds no parameter-specific details, but the baseline of 3 is appropriate because the schema handles parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it scans a public repository and lists the specific risk categories it checks for. It also distinguishes the target resource from siblings like scan_website and scan_claw_skill by naming repository sources explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The supported source types and the note about account connection imply when to use this tool, but there is no explicit when-not-to-use guidance or comparison with sibling tools. The mention of get_scan_results as the follow-up step provides useful workflow context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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