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scan_linear

Scan Linear issues and comments to detect leaked secrets like API keys, tokens, and passwords. Returns redacted findings without modifying your data.

Instructions

Read Linear issues and comments to detect leaked secrets (API keys, tokens, passwords). Never modifies Linear — no issues or comments are written. Auth: requires a Linear personal API key (lin_api_...); set LINEAR_TOKEN env var or pass api_key directly. Side effects: a redacted scan report is uploaded to the n0s1 backend; set allow_secret_upload=True to also upload AES-encrypted secret values for AI validation. Returns redacted findings — raw secret values are never included in the output. Subject to Linear API rate limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesLinear personal API key (lin_api_...) (or set LINEAR_TOKEN env var)
report_formatNoOutput report formatn0s1
show_matched_secret_on_logsNoInclude redacted secret snippets in logs (default: false)
ai_analysisNoQueue async AI credential validation after the scan (requires n0s1 Pro)
n0s1_api_keyNon0s1 API key; overrides the N0S1_TOKEN env var
allow_secret_uploadNoUpload AES-encrypted secret values to the n0s1 backend for AI validation (default: false)
report_uuidNoUUID to assign to the scan report; overrides the auto-generated one

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
report_uuidYes
statusYes
summaryYes
findingsNo
next_cursorNo
usageYes
ai_analysis_statusNo
Behavior5/5

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

The description goes beyond annotations by detailing side effects (upload of redacted report), authentication needs, and that findings are redacted. It confirms the tool is not destructive and is open-world, aligning with annotations.

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 concise, front-loaded with the main purpose, and every sentence adds necessary detail without redundancy.

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

Completeness5/5

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

Given the tool's complexity (7 parameters, side effects, output schema), the description covers all critical aspects: purpose, auth, side effects, output format, and rate limits. The presence of an output schema reduces the need to describe return values.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by explaining the purpose of `allow_secret_upload` and the optional upload for AI validation. This extra context improves parameter understanding.

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 the tool reads Linear issues/comments to detect leaked secrets. It specifies the resource (Linear) and action (scan for secrets), distinguishing it from sibling tools that target different platforms.

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

Usage Guidelines4/5

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

The description provides authentication requirements (Linear API key) and notes that the tool never modifies Linear. However, it does not explicitly guide when to use this tool over others, though sibling names make the platform distinction clear.

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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