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Metis · Software Engineer — Scan Project Scripts

scan_project_scripts

Scan a folder's R/Python scripts to extract packages, variables, and file paths, automatically registering each as a code artifact and returning a summary.

Instructions

Walk all R/Python scripts in a folder, extract metadata, and register them.

For each script found (.R, .Rmd, .qmd, .py):
- Parses packages, file reads/writes, variables, and transforms
- Auto-registers each as a code_artifact in the Code Repository
- Aggregates variables across all scripts
- Maps detected file paths to dataset names

Returns a summary: N scripts, M packages, K datasets, P variables.

Args:
    folder_path: Absolute path to the folder to scan.
    project_id:  Project ID to associate the scripts with (optional).

Input Schema

NameRequiredDescriptionDefault
project_idNo
folder_pathYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It details the scanning, parsing, and registration behavior, and notes the return summary. It does not mention potential side effects like overwriting or permissions, but overall adequately describes the tool's actions.

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 well-structured with a header, bullet points, and an Args list. It is moderately concise, about 8 lines, with no superfluous content, though it could be slightly more compact.

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 tool's complexity (scanning multiple script types, registering artifacts, aggregating data) and the presence of an output schema (not shown but indicated), the description covers the key inputs and outputs. It explains the return summary (N scripts, M packages, etc.), making it sufficiently complete.

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?

The input schema has 0% description coverage, but the tool description includes an 'Args' section explaining both parameters (folder_path and project_id). This adds meaning beyond the schema titles, compensating for the lack of schema-level descriptions.

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 clearly states the tool walks R/Python scripts, extracts metadata, and registers them as code artifacts. It lists specific file types and processing steps, but does not explicitly differentiate from sibling tools like scan_project_folder or scan_folder_for_intent.

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 describes what the tool does but does not specify when to use it versus alternatives, nor does it mention when not to use it. Usage context is implied but not explicit.

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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