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Secret Scan

secret_scan
Read-onlyIdempotent

Scan text for accidentally-committed machine credentials and private-key material. FREE.

Reports each match's location and category so it can be rotated before it leaks. Detection is pattern-based over the common leaked-credential formats; it never echoes the matched value back. Typical input {"text": "<file, diff, or config contents>"} returns {"leaked": bool, "count": N, "findings": [{"line": N, "type": ""}], "note": "..."}.

Pattern matching only - a clean result is not proof, and every hit needs human confirmation before anyone acts on it. Not a general security review (security_deep_dive). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe file, diff, or config contents to scan, pasted as a single string.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

With comprehensive annotations (readOnlyHint, idempotentHint, destructiveHint) already declaring safe read-only/idempotent behavior, the description adds important nuance: pattern-based detection, never echoes matched values, clean result is not proof, errors return structured messages rather than protocol errors. This provides behavioral context beyond annotations. The only small gap: does not mention rate limits or auth requirements.

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?

The description is packed with useful information but could be slightly more structured. Front-loaded with purpose, then free cost, then behavior/limitations, then error handling/safety. Every sentence adds value. Minor length is justified given the number of important behavioral clarifications.

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 the complexity (security tool with caveats), single non-enum parameter, and the presence of an output schema, the description covers: purpose, input format, output structure, limitations, error behavior, safety properties, and alternatives. Could be improved by listing a concrete example value for the type field categories.

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% with a single parameter, so the schema already documents the parameter. The description adds value by providing a concrete example input/output format, stating what kind of string to pass (file, diff, or config contents), and clarifying the result structure. This goes beyond the schema's minimal type description.

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 scans text for accidentally-committed machine credentials and private-key material, specifying verb 'scan', resource 'text', and purpose. It distinguishes from sibling 'security_deep_dive' by explicitly noting this is not a general security review.

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?

Provides explicit when-to-use context (scanning for leaked credentials), what it does not replace (not proof, needs human confirmation, not a general security review), and explicitly names the alternative sibling tool 'security_deep_dive'. Also tells the agent it is safe to retry after correcting input errors.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct analysis area: AI-generated code smells, structural complexity, reviewer persona, review checklist, diff risk scanning, secret scanning, and OWASP security deep dive. Even though review_diff and security_deep_dive both touch security, one is diff-based and the other is full-file, and descriptions clarify the difference.

Naming Consistency4/5

All names use snake_case, but there is a mix of verb-first (ai_code_smell_scan, get_reviewer_persona, secret_scan) and noun-first (complexity_report, review_checklist, review_diff, security_deep_dive) patterns. This is mostly consistent but the verb usage varies (scan, get, report, checklist, dive).

Tool Count5/5

7 tools is well-scoped for a code review analysis server. Each tool serves a specific purpose without overlap, covering multiple angles (AI smells, complexity, security, secrets, diff review, checklist, persona) without being overwhelming.

Completeness4/5

The tool set covers essential static analysis tasks for code review: structural, security, secret detection, and AI-generated code detection. It also provides supporting tools (checklist, persona). A minor gap is the lack of an integrated tool that produces a consolidated review summary or comment generation from findings.

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