point-topic-mcp 0.1.65

UK broadband data analysis MCP server with Snowflake integration

Point Topic MCP Server

UK broadband data analysis server via Model Context Protocol. Simple stdio-based server for local development and Claude Desktop integration.

✅ what's implemented

database tools (requires Snowflake credentials):

• assemble_dataset_context() - get schemas and examples for datasets (upc, upc_take_up, upc_forecast, tariffs, ontology)
• execute_query() - run safe read-only SQL queries
• describe_table() - get table schema details
• get_la_code() / get_la_list_full() - local authority lookups

chart tools:

• get_point_topic_public_chart_catalog() - browse public charts (no auth needed)
• get_point_topic_public_chart_csv() - get public chart data as CSV (no auth needed)
• get_point_topic_chart_catalog() - get complete catalog including private charts (requires API key)
• get_point_topic_chart_csv() - get any chart data as CSV with authentication (requires API key)
• generate_authenticated_chart_url() - create signed URLs for private charts (requires API key)

server info:

• get_mcp_server_capabilities() - check which tools are available and debug missing credentials

conditional availability: tools only appear if required environment variables are set

installation (for end users)

simple pip install:

pip install point-topic-mcp

add to your MCP client (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "point-topic": {
      "command": "point-topic-mcp",
      "env": {
        "SNOWFLAKE_USER": "your_user", 
        "SNOWFLAKE_PASSWORD": "your_password",
        "CHART_API_KEY": "your_chart_api_key"
      }
    }
  }
}

note: environment variables are optional - tools will only appear if credentials are provided. use get_mcp_server_capabilities() to check which tools are available.

Claude Desktop config location:

• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

development setup

setup: uv sync

for local development with claude desktop:

This will add the server to your claude desktop config.

uv run mcp install src/point_topic_mcp/server_local.py --with "snowflake-connector-python[pandas]" -f .env

for mcp inspector:

uv run mcp dev src/point_topic_mcp/server_local.py

environment configuration:

create .env file with your credentials:

# Snowflake database credentials (for database tools)
SNOWFLAKE_USER=your_user
SNOWFLAKE_PASSWORD=your_password

# Chart API key (for authenticated chart generation)
CHART_API_KEY=your_chart_api_key

architecture

stdio transport: communicates with MCP clients via standard input/output for local integration

auto-discovery: tools and datasets are automatically discovered from module files - no manual registration needed

conditional tools: tools only register if required environment variables are present - use get_mcp_server_capabilities() to debug

modular design:

• src/point_topic_mcp/tools/ - tool modules auto-discovered and registered
• src/point_topic_mcp/context/datasets/ - dataset modules auto-discovered for context assembly

adding new tools

this project uses auto-discovery for tools - just add a function and it becomes available.

tool structure

create a file in src/point_topic_mcp/tools/ ending with _tools.py:

# src/point_topic_mcp/tools/my_feature_tools.py

from typing import Optional
from mcp.server.fastmcp import Context
from mcp.server.session import ServerSession

def my_new_tool(param: str, ctx: Optional[Context[ServerSession, None]] = None) -> str:
    """Tool description visible to agents."""
    # your implementation
    return "result"

that's it! the tool is automatically discovered and registered.

conditional tools (require credentials)

use check_env_vars() to conditionally define tools:

from point_topic_mcp.core.utils import check_env_vars
from dotenv import load_dotenv

load_dotenv()

if check_env_vars('my_feature', ['MY_API_KEY']):
    def authenticated_tool(ctx: Optional[Context[ServerSession, None]] = None) -> str:
        """Only available if MY_API_KEY is set."""
        import os
        api_key = os.getenv('MY_API_KEY')
        # use api_key...
        return "result"

key principles

1. auto-discovery: any public function in *_tools.py files becomes a tool
2. conditional registration: wrap in if check_env_vars() for authenticated tools
3. clear docstrings: visible to agents at all times - keep concise and actionable
4. type hints: use for better agent understanding

adding new datasets

this project uses a modular dataset system that allows easy addition of new data sources. each dataset is self-contained and automatically discovered by the MCP server.

dataset structure

each dataset is a python module in src/point_topic_mcp/context/datasets/ with two required functions:

def get_dataset_summary():
    """Brief description visible to agents at all times.
    Keep concise - this goes in every agent prompt."""
    return "short description of what data is available"

def get_db_info():
    """Complete context: schema, instructions, examples.
    Only loaded when agent requests this dataset."""
    return f"""
    {DB_INFO}
    
    {DB_SCHEMA}
    
    {SQL_EXAMPLES}
    """

key principles

1. context window efficiency: keep get_dataset_summary() extremely concise - it's always visible to agents
2. lazy loading: full context via get_db_info() only loads when needed
3. self-contained: each dataset module includes all its own schema, examples, and usage notes
4. auto-discovery: new .py files in the datasets directory are automatically available

adding a new dataset

1. create the module: src/point_topic_mcp/context/datasets/your_dataset.py
2. implement required functions: get_dataset_summary() and get_db_info()
3. test locally: uv run mcp dev src/point_topic_mcp/server_local.py
4. verify discovery: agent should see your dataset in assemble_dataset_context() tool description

see existing modules (upc.py, upc_take_up.py, upc_forecast.py) for structure examples.

optimization tips

• prioritize essential info in summaries
• use clear table descriptions that help agents choose the right dataset
• include common query patterns in examples
• sanity check data against known UK facts in instructions

publishing to PyPI (for maintainers)

build and test locally:

# Build the package with UV (super fast!)
uv build

# Test installation locally
pip install dist/point_topic_mcp-*.whl

# Test the command works
point-topic-mcp

publish to PyPI:

# Set up PyPI credentials in ~/.pypirc first (one time setup)
# [pypi]
#   username = __token__
#   password = pypi-xxxxx...

# Publish to PyPI with the publish script
./publish_to_pypi.sh

test installation from PyPI:

pip install point-topic-mcp
point-topic-mcp