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Zambo

Provibe Audit

provibe_audit
Read-onlyIdempotent

AI code audit for a public GitHub repository. Returns a Provibe score from 0 to 100, security vulnerabilities, a dead-code map, and an execution plan for addressing the findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoZambo Pass email for full audit (optional — without it you get the free teaser: score + top 3 issues). Get pass: https://zambo.dev/#zambo-pass
repo_urlYesPublic GitHub repository URL. Example: https://github.com/owner/my-saas
vibe_contextNoOptional context: language, framework, specific concerns, or what the project does

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dead_codeNo
provibe_scoreNo
execution_planNo
vulnerabilitiesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description lists what the tool returns, which adds some output context, but it does not disclose behavioral nuances like the free-teaser vs full-audit distinction, whether network access to the repo is made, or any rate/time considerations. It adds only marginal behavioral value beyond 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 a single, tightly packed sentence with no filler. It front-loads the action and resource, then lists the deliverables in a readable list. Every clause earns its place.

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?

With an output schema present, the description doesn't need to detail return formats. It covers the core purpose, the required resource type, and the key outputs. It misses the free-teaser vs full-audit distinction, but that is documented in the schema. Overall it is sufficient for a read-only, idempotent audit tool.

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 coverage is 100%, and the schema already documents each parameter with examples and the email tradeoff. The description adds no extra parameter semantics—it just restates 'public GitHub repository' which the schema already provides. Baseline 3 applies because the schema does the heavy lifting.

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

Purpose4/5

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

The description states a specific action ('AI code audit') on a specific resource ('public GitHub repository') and lists concrete outputs (Provibe score, vulnerabilities, dead-code map, execution plan). It does not explicitly differentiate from sibling ghost_audit tools, but the tool name and unique output set make the purpose clear.

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 usage context: auditing a public GitHub repository. It does not mention alternatives or exclusions, such as when to use ghost_audit_* or when the repo is private. The optional email/full audit behavior is only in the schema, not the description, so guidance on when to provide it is missing.

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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