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scan_dependencies

Identify known-vulnerable packages in Python dependency manifests, enabling proactive security remediation.

Instructions

Software Composition Analysis: scan a Python dependency manifest (requirements.txt / Pipfile / pyproject) for known-vulnerable packages. Send the manifest text inline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
onlineNoIf true, query live advisory sources; else use the local DB
filenameNoManifest filename (e.g. requirements.txt)requirements.txt
manifestYesDependency manifest contents
Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It only says 'scan' which implies read-only, but it fails to mention that the 'online' parameter causes network queries to live advisory sources, potential data sent externally, or what the output looks like. The schema notes the online behavior, but the description does not contextualize it.

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 two sentences, front-loaded with the primary purpose, and contains zero filler. Every phrase adds information: the scan target, the manifest types, and the invocation method. Extremely economical and well-structured.

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

Completeness3/5

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

For a moderately complex tool with no output schema, the description adequately states what the tool accepts but not what it returns or how results are presented. It also omits any note about network/privacy implications when 'online' is enabled, which is significant given the tool processes inline manifest text. The schema covers parameters, but the description leaves return format and behavioral caveats unspecified.

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 parameters are already documented. The description adds marginal value by listing acceptable filenames and instructing to send the manifest 'inline,' but it does not elaborate on the 'online' vs local DB distinction beyond what the schema already states. This meets the baseline for full schema coverage.

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 specifies a clear verb ('scan') and resource ('Python dependency manifest'), explicitly identifies the vulnerability-checking purpose, and enumerates accepted manifest types (requirements.txt/Pipfile/pyproject). This distinguishes it from sibling tools like scan_code or scan_repo.

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 description implies when to use the tool (when you have a Python dependency manifest to check) and instructs to 'send the manifest text inline,' but it does not explicitly contrast with alternative scan tools or state when this tool is preferred over scan_repo/scan_code. No exclusions or when-not-to-use guidance is provided.

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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