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generate_audit_report

Generate a bounded Markdown or JSON audit report from a software project without executing code or writing files, so AI agents can review evidence-based findings.

Instructions

Generate a bounded Markdown or JSON audit report in the tool response. No file is written. Findings include evidence and uncertainty; source contents and secret values are not included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo.
limitNo
formatNomarkdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a good job: it discloses that output is bounded, that nothing is written to disk, and that source contents and secret values are excluded from findings. This is meaningful safety/privacy behavior an agent could not get from structured fields. It stops short of explaining whether the tool itself performs scanning or only formats results, which is the remaining behavioral unknown.

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?

Three dense sentences, front-loaded with the verb and no filler; each sentence contributes a distinct fact (purpose/format, no-file-write, findings content). Slightly list-like, but nothing is wasted.

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

Completeness3/5

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

For a 3-parameter tool with no output schema, the description supplies useful return-content detail (evidence, uncertainty, exclusion of secrets), which partially substitutes for a missing output schema. However, the unexplained `path` parameter and the absence of any when-to-use context leave real gaps for correct invocation.

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

Parameters2/5

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

Schema description coverage is 0% for all three parameters. The description covers `format` incidentally ("Markdown or JSON") and hints at bounding, which loosely maps to `limit`, but `path` is never explained — an agent cannot tell whether it is a directory to audit, a report destination, or something else. Two of three parameters remain semantically opaque.

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?

States a specific verb (Generate) and resource (audit report), plus the two output formats and that the report lands in the tool response rather than a file. It implicitly separates itself from scanning/review siblings by being a reporting step, but never names an alternative tool, so the differentiation is left to inference.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to call this versus scan_project, review_security, or the other six siblings, nor any stated prerequisite (e.g., that a scan must run first). A reader can infer it is the aggregation/reporting step, but nothing in the text confirms that.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.