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delimit_redact

Scans and redacts sensitive data like API keys, secrets, and PII from text to prevent credential leakage when sharing content externally.

Instructions

Scan or redact sensitive data (API keys, secrets, PII) from text.

When to use: before sending text to external LLMs or publishing output, to prevent leaking credentials or PII. When NOT to use: to manage stored secrets (use delimit_secret_store family) — this is in-memory text redaction.

Sibling contrast: delimit_secret_* manages credentials at rest; this scrubs them out of arbitrary text.

Side effects: read-only on input text — produces a sanitized copy in action="redact". Calls ai.pii_redact.scan / redact. Detects: API keys (OpenAI, xAI, Google, GitHub, npm), passwords, bearer tokens, emails, phone numbers, SSNs, credit cards, IPs, database URLs.

The internal token map is intentionally NOT exposed via MCP — it stays local. action="redact" returns only the redacted text and counts; the original cannot be recovered through this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionNo"scan" (preview, default) or "redact" (replace).scan
textNoText to process.
categoriesNoComma-separated categories — "api_key", "secret", "pii", "infra". Empty = all categories.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Despite no annotations, the description discloses read-only side effect, specific data types detected, internal token map not exposed, and irreversibility of redact action. Completely transparent.

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

Conciseness4/5

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

Well-structured with front-loaded main action, usage guidelines, and details. Slightly verbose but every sentence adds value; could be tighter.

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 parameter count 3, full schema coverage, and output schema exists, the description covers all necessary aspects: purpose, usage, behavior, parameter details, detected types, and security note. No gaps.

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 description adds meaningful context: explains categories (api_key, secret, pii, infra) and that empty means all categories. Provides value beyond schema.

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?

Description clearly states the tool scans or redacts sensitive data from text, with specific verb-resource pairs. It distinguishes from sibling delimit_secret_store family, which manages stored secrets.

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

Usage Guidelines5/5

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

Explicitly provides when to use (before sending text to external LLMs or publishing) and when not to use (for managing stored secrets, redirecting to delimit_secret_store). Also includes sibling contrast.

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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