vle-mcp

Hero image generated with ChatGPT Images via OpenAI Codex (GPT-5.6 Sol).

An educational Model Context Protocol server for the vle and stages-thermo scientific libraries.

The first tool calculates the boiling temperature of water at an absolute pressure using vle-steam's IAPWS-IF97 implementation. The chatbot interprets and explains the request; tested scientific software supplies the number.

Status

M0 through M3 are implemented and locally verified. The project supports local stdio and an OAuth-protected Streamable HTTP ASGI application factory for remote deployment. Verification includes protocol round trips over both transports, a real stdio subprocess handshake, RFC 9728 metadata, bearer scope and resource binding, and remote resource limits. See PLAN.md, TODO.md, the M2 verification record, and the M3 remote guide.

Related MCP server: Agentic-AI-Planning-And-Reasoning-MCP

Why MCP?

MCP gives AI clients a standard way to discover and call typed tools. It separates natural-language reasoning from authoritative programs and data:

• the model determines intent;

• the MCP client controls connections and tool use;

• this server validates a typed request;

• vle-steam performs the scientific calculation.

See docs/mcp-primer.md for the protocol path and docs/architecture.md for the component architecture. PLAN.md records the Python and API-granularity decisions.

Development setup

Python 3.11 or newer is required.

python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -e '.[dev]'
.venv/bin/vle-mcp

The last command starts a stdio MCP server and waits for a client. It is not an interactive shell; use an MCP client or the protocol tests.

For development against a local vle checkout, install its Python package into the virtual environment according to that repository's build instructions. Do not commit a machine-specific path.

For remote clients, integrate the ASGI factory with a maintained OAuth token verifier and HTTPS ingress. The remote endpoint deliberately cannot start without those operator-owned security inputs. See docs/remote-http.md.

Tool

water_saturation_temperature

Inputs:

• pressure: positive finite number;

• pressure_unit: Pa, kPa, MPa, bar, or atm;

• all pressure is absolute.

Example arguments:

{"pressure": 101.325, "pressure_unit": "kPa"}

The result is approximately 373.1243 K or 99.9743 °C. It includes the normalized pressure and IAPWS-IF97 provenance.

Verification

.venv/bin/python scripts/check_public_data.py
.venv/bin/ruff check .
.venv/bin/ruff format --check .
.venv/bin/pytest
.venv/bin/python scripts/verify_stdio_client.py --command .venv/bin/vle-mcp

Security

The tools remain read-only and deterministic. Stdio requires no credentials; Streamable HTTP requires a resource-bound bearer token with vle:read. Read SECURITY.md, docs/security-model.md, and docs/remote-http.md before deployment or extension.

Authorship

Miguel Jackson owns and directs the project. M0 through M3 were implemented collaboratively by OpenAI Codex using the GPT-5.6 Sol model, following Miguel's instructions and approved plan. See AUTHORS.md for the precise human/AI attribution and limitations.

License

MIT