📖 Overview

As AI-assisted development tools become more common, a recurring challenge is how to provide large language models with accurate, structured, and up-to-date context about complex software systems.

The Model Context Protocol (MCP) proposes a standardized way to expose curated project knowledge—such as documentation, schemas, and examples—to external tools via an explicit, query-driven interface, rather than relying on ad-hoc prompts or implicit embeddings.

In this walkthrough, MCP is considered as a local, opt-in, read-only bridge that allows tools to retrieve authoritative context on demand, without modifying or executing project logic.

The focus here is not on deployment or governance, but on understanding how such a pattern could complement Pyxel’s existing documentation and validation workflows if adopted in the future.

Exploration Context

• Domain: AI-assisted scientific tooling
• Focus: Model Context Protocol (MCP)
• System studied: Pyxel (hypothetical integration)
• Type: Theoretical walkthrough / design exploration

🧠 Motivation

Pyxel is a complex scientific simulation framework with a growing ecosystem of detectors, models, and configuration parameters. Today, Pyxel already provides rich authoritative artifacts—JSON Schemas, minimal YAML examples, and structured model descriptions—that support correctness and reproducibility.

However, this information is primarily optimized for human readers and purpose-built tooling. As users increasingly work across IDEs, GUIs, and AI-assisted environments, the same authoritative knowledge must be reinterpreted repeatedly across interfaces.

A theoretical MCP server offers one way to expose this existing knowledge in a structured, machine-consumable form, enabling consistent validation and guidance without introducing a new source of truth.

In this framing, Pyxel remains authoritative, while MCP acts only as an advisory interface layer.

🧱 Design Principles (Theoretical)

1. Pyxel remains the single source of truth
   All exposed information is derived directly from Pyxel’s existing artifacts.
2. Read-only by default
   The walkthrough assumes inspection, validation, and explanation only.
3. Schema-first design
   JSON Schema is treated as a primary interface contract.
4. Interface-agnostic access
   No single client (GUI, IDE, AI) is privileged.
5. Local-first execution
   The MCP server is assumed to run locally alongside Pyxel.

📦 What Pyxel Already Provides

• JSON Schemas defining required fields, constraints, and valid ranges.
• Minimal YAML examples that are schema-valid and canonical.
• Model and detector descriptions written in structured documentation formats.
• Programmatic APIs already consumed by existing tooling.

The walkthrough assumes that an MCP layer would expose these artifacts as-is, without reinterpreting or duplicating logic.

🧩 Hypothetical MCP Capabilities

1. Schema Exposure

An MCP server could expose full JSON Schemas, including field descriptions, constraints, and required parameters.

2. Configuration Validation

Schema-based validation services could accept YAML or JSON input and return structured, human-readable error reports.

3. Reference Configuration Examples

Minimal, canonical YAML fragments could be exposed as authoritative starting points for configuration.

4. Model & Detector Metadata

Short descriptive metadata could support orientation-level questions such as model purpose, parameter relevance, and relationships.

5. Reference-Oriented Documentation Search

Lightweight lookup across schemas, examples, and reference text could support fast orientation without deep semantic inference.

6. Optional Visualization Hooks (Exploratory)

Read-only visualization or preview hooks could complement existing tooling, without embedding execution logic in MCP itself.

🔐 Security and Boundary Considerations

Even in a read-only scenario, AI-facing tooling must treat AI agents as untrusted intermediaries.

• Explicit capability scoping
• Schema-validated inputs only
• No implicit filesystem or shell access
• Clear separation between suggestion and execution
• Human confirmation for any destructive future extensions

❓ Open Questions

• What level of granularity is most useful to expose?
• How should schema and documentation updates stay synchronized?
• How can outdated or misleading AI guidance be prevented?

📌 Next Steps (Exploratory)

Future exploration may include small prototypes, deeper study of MCP server internals, and evaluation of how such an approach could complement existing documentation workflows—without implying adoption or direction.

✍️ Authored by: Doby Baxter
🧠 Category: AI & MCP Exploration Notes (WIP)