DB MCP (HR CSV to SQLite)
Enables querying of HR employee data through an in-memory SQLite database, providing tools for executing read-only SQL queries, searching for personnel, and inspecting database schemas.
Click on "Install 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., "@DB MCP (HR CSV to SQLite)Who are the top 5 highest-paid employees?"
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.
DB MCP (HR CSV → SQLite) — Open Source Reference
This folder contains a fully open-source Model Context Protocol (MCP) server implementation that:
Loads an HR “people” CSV file
Reads 3 lines of metadata at the top of the CSV (comment lines starting with
#)Imports the CSV into an in-memory SQLite database
Exposes read-only MCP tools over stdio (newline-delimited JSON-RPC 2.0)
No Claude Desktop setup is required. A small Python client is included for testing.
Files
db_mcp_server.py— MCP server (stdio)db_mcp_client.py— simple MCP stdio client for testingdata/hr_people.csv— sample HR CSV with 3-line metadata header
Run the server
python db_mcp_server.pyOptionally pass a custom CSV path:
python db_mcp_server.py /path/to/your/hr_people.csvOr set an environment variable:
HR_CSV_PATH=/path/to/your/hr_people.csv python db_mcp_server.pyTest with the included client (recommended)
python db_mcp_client.pyYou should see:
initializehandshaketools/lista sample SQL query result
an interactive prompt to run more
SELECTqueries
Tools exposed
hr_metadata— returns the 3-line metadata header as a JSON objecthr_schema— returns the SQLite schema for tableemployeeshr_query— execute read-onlySELECT/WITHSQL querieshr_find_people— structured search without writing SQL
CSV metadata format (first 3 lines)
Example:
# dataset: HR People
# description: Synthetic employee roster for MCP demo (no real PII)
# primary_key: employee_id
employee_id,first_name,last_name,...Metadata lines are parsed as key: value. If a line is not key: value, it is stored as meta_line_1, meta_line_2, etc.
Notes for sharing
Everything here is standard-library Python (SQLite + CSV).
The demo data is synthetic (no real PII).
The server writes only JSON-RPC to stdout. Logs go to stderr (safe for stdio MCP).
Samples
-Terminal 1
C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1>python mcp_server.py data/hr_people.csv
[db_mcp_server] Ready. Loaded data/hr_people.csv. Tools: 4
-Terminal 2
C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1>python client.py --csv ./data/hr_people.csv
[db_mcp_server] Ready. Loaded ./data/hr_people.csv. Tools: 4
[db_mcp_server] Internal error:
Traceback (most recent call last):
File "C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1\db_mcp_server.py", line 537, in main
server.handle(msg)
File "C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1\db_mcp_server.py", line 493, in handle
self.handle_initialize(id_value, params)
File "C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1\db_mcp_server.py", line 438, in handle_initialize
Initialize response:
{
"jsonrpc": "2.0",
"error": {
"code": -32603,
"message": "Internal error"
},
"id": 1
}
_send(resp)
Tools:
File "C:\Users\davidzhang\Downloads\ml\ml\db_mcp_1\db_mcp_server.py", line 45, in _send
{
"jsonrpc": "2.0",
"id": 2,
"result": {
"tools": [
{
"name": "hr_metadata",
"title": "HR dataset metadata",
"description": "Return the 3-line metadata header read from the HR CSV file.",
"inputSchema": {
"type": "object",
"additionalProperties": false
},
"outputSchema": {
"type": "object"
}
},
{
"name": "hr_schema",
"title": "HR table schema",
"description": "Return SQLite schema information for the employees table.",
"inputSchema": {
"type": "object",
"additionalProperties": false
},
"outputSchema": {
"type": "object"
}
},
{
"name": "hr_query",
"title": "Run a read-only SQL query",
"description": "Execute a read-only SQL query (SELECT/WITH only) against the in-memory SQLite database.\nTable name: employees\nExample: SELECT department, COUNT(*) AS n FROM employees GROUP BY department",
"inputSchema": {
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "A SELECT/WITH SQL query to run."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 500,
"description": "Optional row limit (wraps the query)."
}
},
"required": [
"sql"
],
"additionalProperties": false
},
"outputSchema": {
"type": "object",
"properties": {
"rowCount": {
"type": "integer"
},
"rows": {
"type": "array",
"items": {
"type": "object"
}
}
},
"required": [
"rowCount",
"rows"
]
}
},
{
"name": "hr_find_people",
"title": "Find employees (structured filters)",
"description": "Find employees by common HR filters without writing SQL.",
"inputSchema": {
"type": "object",
"properties": {
"name_contains": {
"type": "string",
"description": "Substring match against first or last name (case-insensitive)."
},
"department": {
"type": "string"
},
"title": {
"type": "string"
},
"location": {
"type": "string"
},
"min_salary": {
"type": "number"
},
"max_salary": {
"type": "number"
},
"hired_after": {
"type": "string",
"description": "YYYY-MM-DD"
},
"hired_before": {
"type": "string",
"description": "YYYY-MM-DD"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 200,
"default": 25
}
},
"additionalProperties": false
},
"outputSchema": {
"type": "object",
"properties": {
"rowCount": {
"type": "integer"
},
"rows": {
"type": "array",
"items": {
"type": "object"
}
},
"appliedFilters": {
"type": "object"
}
},
"required": [
"rowCount",
"rows",
"appliedFilters"
]
}
}
}
}
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