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vle-mcp

by miguelju
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vle-mcp

vle-mcp architecture: an AI question passes through a typed MCP gateway to water and steam calculations and returns a structured result

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

Available Tools

1 tool
water_saturation_temperatureA

Calculate water's boiling/saturation temperature at an absolute pressure.

Use this for questions such as “What is the boiling temperature of water at atmospheric pressure?” One standard atmosphere is 1 atm or 101.325 kPa absolute. Do not use gauge pressure.

ParametersJSON Schema
NameRequiredDescriptionDefault
pressureYes
pressure_unitNokPa

Output Schema

ParametersJSON Schema
NameRequiredDescription
pressureYes
substanceNo
calculationNo
temperatureYes

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of behavioral disclosure. It correctly emphasizes absolute pressure over gauge, which is a key behavioral caveat. However, it does not mention any limitations such as temperature ranges, precision, or what the output looks like beyond the implicit calculation. The output schema may cover the return format, but the description itself is somewhat sparse on behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the main purpose. It uses a short example and a clear warning about gauge pressure, with no extraneous content. Every sentence serves a purpose, making it easy for an agent to quickly grasp what the tool does and how to use it correctly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, and the output schema likely defines the return value, so the description doesn't need to explain that. It covers the essential usage context: the pressure type and a common unit example. It could be slightly more complete by mentioning the range of pressures supported or any assumptions about water purity, but for a straightforward thermodynamic calculation, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema itself documents both parameters with types, defaults, and an enum for pressure_unit. Since schema description coverage is 0%, the description must add meaning beyond the schema. It does by explaining the absolute pressure requirement and providing the conversion factor for atm to kPa, which helps users correctly set the pressure parameter. This is meaningful value added beyond the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the specific verb 'Calculate' and the resource 'water's boiling/saturation temperature', and clearly specifies the input as absolute pressure. It provides a concrete example question and clarifies that standard atmosphere is 1 atm or 101.325 kPa absolute, leaving no ambiguity about the tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this for questions such as...' and gives a clear example. It also warns against using gauge pressure, which is a critical usage constraint. Although there are no sibling tools to differentiate from, the guidance is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedwater_saturation_temperature
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TDQS

A4.1/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is no possibility of confusion or overlap.

Naming Consistency5/5

The single tool uses a clear lowercase_with_underscores naming convention, consistent with common practices.

Tool Count3/5

The server has only one tool, which is minimal. While it may serve a specific narrow purpose, it lacks the typical breadth expected of a functional server.

Completeness3/5

The tool covers a single calculation function. For a dedicated water properties server, this might be sufficient, but it offers no related utilities or additional thermodynamic properties, leaving likely gaps.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

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