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llm-migrate

by Athenaxlee
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Python
Local

Scan Application

scan_application

Scan a local Python application to create a normalized coupling inventory, including prompt files missed by automatic discovery.

Instructions

Scan a local Python application into a normalized coupling inventory.

prompt_sources names prompt files automatic discovery missed (relative to the application root).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
prompt_sourcesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.3.0
    • addedInput schema / properties / prompt_sources
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  2. First observedv1.1.0

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool scans an application into an inventory but does not mention whether the operation is read-only, whether it has side effects, or any prerequisites or constraints. It also does not describe the return format beyond the output schema. The description only states the purpose, not the behavioral traits.

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 two sentences with no fluff. The main purpose is front-loaded, and the optional parameter explanation is concise and relevant. Every sentence 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?

The description covers the core action and explains the one non-obvious parameter. Since an output schema exists, the return values are already defined. However, it does not mention any prerequisites or behavioral nuances, but for a scanning tool with an output schema, it is sufficiently complete for an agent to call it correctly.

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?

The schema description coverage is 0%, so the description must compensate. It does explain the purpose of prompt_sources (names prompt files automatic discovery missed, relative to the application root), adding meaning beyond the schema. However, the 'path' parameter is not explicitly explained; its meaning is only implied by context. Thus, the description partially compensates for the lack of schema coverage.

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 clearly states the tool scans a local Python application into a normalized coupling inventory, with a specific verb (scan), resource (local Python application), and outcome. This clearly distinguishes it from sibling tools focused on research, migration, or validation. No ambiguity.

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 does not explicitly mention when to use this tool versus alternatives, nor does it list any exclusions. The purpose is clear, so an agent could infer its usage, but there is no explicit guidance on when to choose it over other scanning or analysis tools. This is implied usage rather than explicit 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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