Skip to main content
Glama
chanys

mcp-town-explorer

by chanys

mcp-town-explorer

A learning project that makes every MCP boundary visible: host, client, server, transport, discovery, invocation. The trick that makes the boundaries legible is one server, two hosts: a no-LLM host and an LLM host talk to the exact same server with zero server changes.

What MCP is

The Model Context Protocol (MCP) is a standard way for an application (the host) to let a language model use external tools and data through a uniform interface. The host embeds an MCP client that speaks JSON-RPC to one or more MCP servers; each server advertises tools (callable functions) and resources (readable content) with machine-readable schemas. The point is decoupling: any MCP-speaking host can use any MCP server without custom glue, because discovery ("what tools exist?") and invocation ("call this tool with these arguments") are standardized.

Architecture

Architecture: user to host (LLM + MCP client) over JSON-RPC/stdio to server to data

Which file is which:

  • Host: v1_cli/host.py and v2_llm/host.py. The host owns the user interaction, and in V2 owns the LLM and the validation logic.

  • Client: ClientSession from the MCP SDK, constructed inside each host. It is not hand-written JSON-RPC; the SDK provides it.

  • Server: server/server.py. Built with FastMCP; exposes the tools and resource. Neither host imports it -- they only launch it as a subprocess and speak the protocol.

For a deeper walk-through of the MCP plumbing (transport, client, handshake, schemas, result shapes), see docs/notes.md.

Setup

Uses uv. Python is pinned to 3.12.

uv sync

Run V1 (no LLM, no API key)

Argv selects the tool; the model is never involved. This isolates the protocol from model behavior.

uv run python v1_cli/host.py list-tools            # print the raw advertised JSON schema
uv run python v1_cli/host.py housing Winchester
uv run python v1_cli/host.py distance Winchester
uv run python v1_cli/host.py schools Lexington
uv run python v1_cli/host.py safety Woburn
uv run python v1_cli/host.py resource Winchester   # read the town://{town} resource
uv run python v1_cli/host.py --verbose housing Winchester   # log the protocol lifecycle

The error path, deliberately shown:

uv run python v1_cli/host.py housing Nowhere
# -> ERROR from server: Unknown town: 'Nowhere'. Known towns: ...
# exits non-zero; the server error surfaces across the protocol rather than being swallowed

Run V2 (adds the LLM)

Natural language in. The model sees the tool schemas, proposes a call, the host validates it, the client executes it, the result is fed back, and the model explains. The loop repeats (capped at 5 iterations) until the model stops requesting tools.

Put OPENAI_API_KEY in a .env file at the repo root (it is git-ignored); the host loads it automatically via python-dotenv. Exporting the variable works too.

# .env at the repo root contains: OPENAI_API_KEY=sk-...   (loaded automatically)
uv run python v2_llm/host.py "Compare schools in Winchester and Lexington"
uv run python v2_llm/host.py --show-tokens "How safe is Woburn?"   # print tool-schema token cost

# or export it instead of using .env:
export OPENAI_API_KEY=sk-...
uv run python v2_llm/host.py "How safe is Woburn?"

The validation step is the reason V2 exists. Before any execution the host checks the proposed tool name against an allowlist and, for tools that take a town, checks that the town exists in the dataset; a rejected call is logged ([REJECTED] ...) and never reaches the server. The exact line where a model proposal becomes an execution is commented in v2_llm/host.py.

Provenance

This dataset describes real Massachusetts towns, but it is stitched together from several sources of differing years and methodologies. It is a demo for teaching MCP architecture. Do not use it for any real decision (buying a home, choosing a school district, judging safety).

Sources, one per column:

column

source

median_home_price

Zillow Home Value Index (ZHVI), town level

school_rating

GreatSchools, rounded average of the town's public schools (GreatSchools publishes no single district number)

violent_crime_rate

NeighborhoodScout, incidents per 1,000 (2024 FBI-derived vintage)

distance_to_boston_mi

computed: haversine from the town's US Census/Wikipedia centroid to Boston City Hall (42.3601, -71.0589)

population

US Census (2020 decennial or ACS estimate)

Per-column source URLs, vintages, retrieval notes, and known caveats are recorded in PROVENANCE.md. The habit is the lesson: record where every number came from, even in a teaching dataset.

What this does NOT demonstrate

  • Deployment. stdio transport means the server is a local subprocess the host spawns. There is no network service, no container, no host/port.

  • Auth. No authentication or authorization between host and server. The V2 "permission boundary" is application-level input validation, not identity or access control.

  • Remote transport. No HTTP/SSE/streamable transport. Everything is local stdio.

  • Multi-user / concurrency. One host, one server subprocess, one user, one request at a time.

-
license - not tested
-
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/chanys/mcp-town-explorer'

If you have feedback or need assistance with the MCP directory API, please join our Discord server