Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-migration-tools Detect the stack from these files: ProductList.asp, web.config"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A small, public-safe FastMCP server that exposes practical legacy-to-modern migration helpers as AI-callable tools — the same "thin tool wrapper over tested, pure logic" pattern I use on a larger production MCP server, distilled into a clean, fully generic example.
Modernizing 100+ legacy apps means doing the same analysis over and over: what stack is this? what endpoints does it expose? how do I port this DDL? how big is the job? These are perfect MCP tools — small, deterministic, and callable by an AI agent (or a human) on demand.
Related MCP server: legacy-web-mcp
Tool | What it does |
| Infers the dominant tech stack from a file listing (weights manifests over single files) |
| Pulls |
| Converts SQL Server DDL into a portable Flyway migration (identity cols, |
| Transparent person-day estimate from coarse inventory counts, with a risk band |
tools/*.py ← pure, dependency-free functions (100% unit-tested)
▲
server.py ← FastMCP wrappers: @mcp.tool() delegating to the pure logicKeeping the logic free of the MCP runtime means every tool is fast to test
and reusable outside MCP — the tests don't even import fastmcp.
# install
pip install .[dev]
# run the tests (17 passing)
pytest -q
# run the MCP server (stdio transport)
mcp-migration-toolsfrom mcp_migration_tools.tools import detect_stack, sql_to_flyway
detect_stack(["ProductList.asp", "web.config"])
# {'stack': 'aspnet', 'confidence': 0.75, ...}
sql_to_flyway("CREATE TABLE [dbo].[Product]([Id] INT IDENTITY(1,1))", version=1,
description="create product")
# {'filename': 'V1__create_product.sql', 'body': 'CREATE TABLE Product(Id INT GENERATED BY DEFAULT AS IDENTITY)\n'}Example client config (e.g. for an MCP-capable IDE/agent):
{
"mcpServers": {
"migration-tools": {
"command": "mcp-migration-tools"
}
}
}mcp-migration-tools/
├── src/mcp_migration_tools/
│ ├── server.py # FastMCP server (tool registration)
│ └── tools/ # pure, tested logic
│ ├── stack_detector.py
│ ├── api_extractor.py
│ ├── sql_translator.py
│ └── effort_estimator.py
├── tests/ # pytest suite (one file per tool)
├── .github/workflows/ci.yml # test on Python 3.11 & 3.12
└── pyproject.tomlPart of my modernization portfolio · simeptk.github.io · companion to legacy-to-modern-demo
Infer the dominant technology stack from a list of file paths.
Inputs: files (required). Returns: object
Estimate migration effort in person-days from coarse inventory counts.
Inputs: kloc, pages, tables, integrations, +1 more. Returns: object
Extract REST endpoints ({method, path}) from Spring controller source.
Inputs: source (required). Returns: result
Convert SQL Server DDL into a portable Flyway migration file.
Inputs: ddl (required), version, description. Returns: object
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool targets a unique aspect of migration: API extraction, SQL conversion, stack detection, and effort estimation. No two tools perform similar or overlapping functions, making selection unambiguous.
The naming style is consistent with lowercase snake_case and mostly follows a verb_noun pattern (extract_api_contract, detect_stack, estimate_migration_effort). One tool (sql_to_flyway) uses a noun_to_noun pattern, which is a minor deviation but still clear and readable.
With 4 tools, the set is within the ideal 3-15 range and feels appropriately scoped for a focused migration utility. It is slightly sparse but each tool serves a distinct purpose.
The tools cover analysis, conversion, and estimation, but lack any migration execution or validation operations. There are notable gaps in the end-to-end migration workflow, though the existing tools are sound.
AI-native Day 0 modernization platform for PRDs, architecture, work orders, and code transformation.
Read-only AI coding tools for change verification, release readiness, capacity, and guidance.
AI-callable tools for API mocking, testing, monitoring, security, and automation.
Code intelligence platform for AI agents. 20 tools for architecture, security & impact analysis.
