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nittim

Run a Single Audit Module

run_module

NEEDS A KEY: mint one at https://nittim.com/keys. Run ONE nittim audit module against a GitHub repository. Deterministic modules (secret-scan, dependency-cve) return their scanner evidence directly and are always free. Deep-tier modules make one focused model call and cost 5.03 credits each. A single-module run never produces scores or a verdict. BYOK Pro: the x-nittim-anthropic-key header runs a deep-tier module on your own key at no credit cost. Deep-tier modules follow the standard two-step quote-then-confirmedCost protocol; the two free deterministic modules run straight away — nothing is charged, so there is nothing to confirm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoUrlYesGitHub repository URL or owner/repo string, e.g. https://github.com/owner/repo or owner/repo. Private repos require a githubToken.
moduleKeyYesThe module's key, e.g. 'secret-scan', 'dependency-cve', 'security', 'privacy', 'gdpr'.
githubTokenNoOptional GitHub personal access token (read-only) for private repos. If omitted, only public repos are accessible.
authorizationNoHUMAN CONFIRMATION. Omit until the account owner has pressed Confirm on the link a paid tool answered with; then set to that answer's `authorization` id (single-use, short TTL). Protocol: server instructions.
confirmedCostNoCOST CONFIRMATION. Omit on the first call (it only quotes); then set to the exact `cost` object quoted. A moved price is re-quoted, never charged. Protocol: server instructions.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations are minimal (readOnlyHint=false, destructiveHint=false), so the description carries the behavioral burden and does so thoroughly. It discloses the key requirement, cost, free/deterministic vs paid/deep-tier behavior, the quote-then-confirm protocol, and the BYOK header option. There is no contradiction with annotations, and significant context is added beyond what annotations provide.

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 dense but well-organized, front-loading the critical 'NEEDS A KEY' requirement and then proceeding from action to module categories to protocol. Each sentence carries useful information, though there is minor redundancy around charging ('always free' and 'nothing is charged') and a slight marketing tone in 'BYOK Pro.' Overall, it is appropriately sized for a tool with a multi-step payment protocol.

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?

There is no output schema, so the description must handle return expectations. It clearly states that deterministic modules return scanner evidence directly and that no scores or verdict are produced, which covers the main output expectations. It does not specify what a deep-tier module returns after confirmation, but the cost and protocol flow are sufficiently detailed for an agent to invoke this tool correctly.

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%, so the schema already documents every parameter. The description adds value by explaining the cost protocol that governs confirmedCost and authorization, tying those parameters to a real-world flow, and noting that deterministic modules require no confirmation. This goes beyond the schema descriptions by clarifying when parameters are needed and how they interact.

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 states a specific verb and resource: 'Run ONE nittim audit module against a GitHub repository.' It further distinguishes the tool by clarifying that it handles one module only, never produces scores or a verdict, and names concrete module examples. This separates it clearly from sibling tools like audit_repo or audit_source that likely operate over the whole repo or produce broader results.

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 gives explicit usage rules: deterministic modules run immediately and free, while deep-tier modules require a two-step quote-then-confirmedCost protocol. It also explains when the confirmation step is unnecessary and how BYOK Pro alters the flow. It does not explicitly name sibling alternatives or say 'use audit_repo when you need a full verdict,' but the phrase 'never produces scores or a verdict' provides strong implicit guidance.

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

A4.3/5.0
Disambiguation4/5

Most tools map to distinct workflow stages: estimate, preview, submit audit, retrieve audit, run module, dispute finding, and judge output. The closest pairs are audit_repo vs audit_source and scan_source vs run_module, but the descriptions provide enough boundary detail for an agent to choose correctly.

Naming Consistency5/5

Every tool follows the same snake_case verb_noun pattern: audit_repo, audit_source, estimate_audit, get_audit, list_modules, run_module, scan_source, and so on. There is no mixing of naming conventions or vague standalone verbs.

Tool Count5/5

11 tools is well-scoped for a paid AI audit service with async delivery, approval flows, and both repo-based and source-based inputs. Each tool covers a distinct step in the audit pipeline without feeling redundant or bloated.

Completeness4/5

The core lifecycle is covered: estimate cost, submit audits, run single modules, check status, retrieve results, and dispute findings. Minor gaps include no way to list past audits, cancel a pending audit, or manage credits directly, though estimate_audit does show the balance.

Resources