d365fo-agent-developer 0.6.1

Local D365 F&O knowledge toolkit: corpus indexing (SQLite FTS5), deterministic generation, and an MCP server that grounds coding agents (Claude Code, Codex) in real AOT facts.

d365fo-agent-developer — D365 F&O X++ knowledge for Claude Code & Codex

A local MCP server that gives an AI coding agent (Claude Code, Codex, any MCP host) a grounded knowledge base of Dynamics 365 Finance & Operations X++ — so you can develop for D365 quickly without re-feeding a whole repo for analysis every time.

It grounds the agent in real AOT facts: it verifies that a class/table/EDT/enum/entity actually exists (anti-hallucination), searches the corpus, walks extension/security relationships, serves an X++ engineering methodology, validates generated XML against learned per-type structure, scaffolds or deterministically generates artifacts, and (on a Windows D365 host) compiles with the real X++ compiler.

• Pure Python standard library — zero runtime dependencies, runs anywhere Python 3.11+ runs.
• Vendor-neutral — Claude Code, Codex, Gemini CLI, or any MCP-speaking client over stdio.
• The standard D365 corpus is the same for everyone, so it is indexed once and used as a portable knowledge base; your own custom code is optional.

Install

pip install d365fo-agent-developer
# or, isolated:  pipx install d365fo-agent-developer

This installs two commands: d365fo-agent-developer (the MCP server; d365fo-mcp is a short alias) and d365fo-agent (the CLI).

Get the knowledge base (once)

Pick one. Both produce a local index at ~/.d365fo-agent/d365fo.db that the server uses by default.

A. Download the prebuilt standard-D365 index (fastest, no D365 install needed):

d365fo-agent fetch-knowledge          # downloads + caches the standard knowledge index

B. Build it from your own D365 dev box (no download; uses metadata you already have):

d365fo-agent build-index \
  --db ~/.d365fo-agent/d365fo.db \
  --packages-root "C:/AOSService/PackagesLocalDirectory" \
  --rebuild

The methodology, default lint rules, and a default learned type-profile ship inside the package, so validation and guidance work out of the box even before the index is built.

Wire it into your agent

Claude Code — add to .mcp.json (project) or ~/.claude.json (global):

{
  "mcpServers": {
    "d365fo-agent-developer": { "command": "d365fo-agent-developer", "args": [] }
  }
}

…or one command: claude mcp add d365fo-agent-developer d365fo-agent-developer

Codex — add to ~/.codex/config.toml:

[mcp_servers.d365fo-agent-developer]
command = "d365fo-agent-developer"
args = []

With no --db, the server uses the cached knowledge index automatically. That's it — ask the agent to build something for D365 and it will verify elements, follow the methodology, and validate its output instead of guessing.

Add your custom code (optional)

Point the server at your D365 source repo so your custom classes/tables/EDTs/enums/extensions are indexed too, and so the rich tools can read real signatures and clone real examples:

[mcp_servers.d365fo-agent-developer]
command = "d365fo-agent-developer"
args = ["--repo-root", "C:/path/to/your/D365Repo",
        "--rules", "C:/path/to/your/rules.json",
        "--packages-root", "C:/AOSService/PackagesLocalDirectory"]

Capability	Knowledge index only	+ a PackagesLocalDirectory / repo
Verify an element exists, search, relations, methodology, validation, scaffolding by template	✅	✅
Read a real signature, clone a real example (get_signature, find_similar_examples, scaffold_object)	needs source files	✅
Compile with the real X++ compiler (compile_model)	—	✅ (Windows D365 host)

What the agent gets (MCP tools)

element_exists, find_element, search_corpus, get_signature, get_extension_chain, get_security_links, get_entity_exposure, find_similar_examples, scaffold_object, find_references, find_reverse_references, analyze_spec, generate_from_spec, validate_xml, lint_artifact, derive_entity, wire_security, compile_model, compile_generated, get_sql_model, explore_functional_unit, find_relations, get_methodology, index_stats.

See docs/mcp-server.md for the verify-driven workflow and docs/x++-methodology.md for the behavioural contract.

Maintainer: publish the knowledge index

The wheel stays tiny; the ~100 MB standard index is distributed as a downloadable asset.

# 1. Build a STANDARD-only index from a PackagesLocalDirectory (no custom repo)
d365fo-agent build-index --db d365fo-standard.db --packages-root <PLD> --rebuild
# 2. (optional) learn type profiles to ship as the default
d365fo-agent build-type-profiles --db d365fo-standard.db --packages-root <PLD> \
  --out src/d365fo_agent/data/aot-type-profiles.json
# 3. Compress and attach to a GitHub release
python -c "import gzip,shutil; shutil.copyfileobj(open('d365fo-standard.db','rb'), gzip.open('d365fo-standard.db.gz','wb'))"
# 4. Point users at it: set DEFAULT_KNOWLEDGE_URL in knowledge_fetch.py (or pass --url)

Note: the index holds factual AOT metadata (element names, types, packages, labels, relations) — not Microsoft source. Confirm your redistribution position before publishing a prebuilt standard index; option B above lets each user build their own with zero redistribution.

To publish the package itself: python -m build then python -m twine upload dist/* (PyPI account required).

Develop / contribute

pip install -e ".[dev]"
PYTHONPATH=src python -m unittest discover -s tests   # full test suite
ruff check src/d365fo_agent tests

Docs: Architecture · MCP Server · X++ Methodology · Specification Contract · Metadata Schema · Tool Catalog

License

MIT — see LICENSE.