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guardvibe

guardvibe

by goklab

export_sarif

Scan a directory and export findings in SARIF v2.1.0 format for CI/CD integration with GitHub, GitLab, or Azure DevOps.

Instructions

Scan a directory and export results in SARIF v2.1.0 format for CI/CD integration (GitHub, GitLab, Azure DevOps). Returns JSON string.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "export_sarif"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesDirectory to scan

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.66.0
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full disclosure burden and only tells us the output format and that a string is returned. It does not say whether 'export' writes a file to disk or only returns the string, whether the operation is read-only, what rules/engine back the scan, or what happens on an unscannable path - all material for a scan+export tool with zero annotation coverage.

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?

Two compact sentences with no filler; the core action and output format are front-loaded before the return-type note. Nothing repeats the title and 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?

Without an output schema, the description does its minimum duty by stating 'Returns JSON string', and the SARIF version is a useful detail. However, for a tool with no annotations and no output schema, the file-vs-string ambiguity and the lack of any scope/safety statement leave it only partially complete.

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?

There is a single parameter whose schema description ('Directory to scan') already covers it at 100% coverage, which sets the baseline at 3. The prose adds no extra semantics such as whether the path must be absolute, whether it recurses, or how it relates to the working directory.

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 ('Scan'), resource ('a directory') and a concrete output artifact ('SARIF v2.1.0 format for CI/CD integration'), which is far more informative than a bare name restatement. It stops short of differentiating itself from the similarly-scoped sibling scan_directory, so an agent still has to guess which one produces SARIF.

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 phrase 'for CI/CD integration (GitHub, GitLab, Azure DevOps)' implies the consumption context, but the description never says when to choose this over scan_directory, full_audit, or deep_scan, nor does it state prerequisites (e.g. whether a prior scan or config is needed). Usage is inferred rather than directed.

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