Reqif

An MCP server for requirements engineering. Load ReqIF / .reqifz baselines (the OMG open standard exported by IBM DOORS, Polarion, Codebeamer, Jama…) and let any MCP client — Claude Desktop, Claude Code, Cursor — query requirements, follow trace links, detect orphans, and diff baselines in natural language.

Requirements management is where automotive, aerospace, rail and defense engineering actually lives, yet the MCP ecosystem has servers for Slack and GitHub and almost nothing for it. This fills that gap.

What it does

Ask your MCP client things like:

• "Which ASIL-D requirements have no verifying test case?"
• "Trace REQ-AEB-001 downstream — what tests cover it, directly or transitively?"
• "Diff baseline v3 against v4: what changed, was added, was removed?"
• "Show me the outline of the AEB specification."

Tools exposed

Install & run

pip install reqif-mcp        # or: pip install -e . from a clone
reqif-mcp path/to/baseline.reqifz

Register in Claude Desktop / Claude Code (.mcp.json) — no arguments needed; load files at runtime with the load_reqif tool:

{
  "mcpServers": {
    "reqif": { "command": "reqif-mcp" }
  }
}

Then, in your MCP client: "Load /path/to/baseline.reqifz and show me the document stats."

CLI arguments are an optional shortcut for small files (reqif-mcp fixtures/small.reqif); large baselines are better loaded at runtime so the stdio connection comes up instantly. Unloadable startup paths log a warning instead of killing the server.

Architecture

.reqif / .reqifz ──▶ parser.py (lxml, namespace-agnostic) ──▶ model.py (dataclasses)
                                                                    │
MCP client ◀── server.py (FastMCP, stdio) ◀── store.py (in-memory DuckDB: SQL over
                                               requirements / attributes / relations)

Design choices:

• Namespace-agnostic parsing. Real-world ReqIF exports disagree on namespace prefixes and even URI revisions; matching on local-name() makes the parser tool-vendor tolerant.
• Enum values resolved to labels. ATTRIBUTE-VALUE-ENUMERATION refs are resolved through DATATYPE-DEFINITION-ENUMERATION, so a status reads Approved, not ev-4f2a….
• Type-level default values applied. An ATTRIBUTE-DEFINITION with a DEFAULT-VALUE is materialized on every spec-object of that type that doesn't override it — otherwise baseline diffs silently miss defaulted attributes.
• DuckDB as the query engine. Orphan detection, transitive tracing and baseline diffs are set operations — SQL expresses them cleanly, and it scales to large baselines without an external service.
• Hardened XML parsing. Entity resolution and network access disabled (XXE-safe).
• XHTML flattened to text. MCP tools serve search and trace; formatting is noise for that job.

Development

pip install -e ".[dev]"
pytest

Tests run against a synthetic ADAS emergency-braking fixture (tests/fixtures/) — no proprietary data anywhere in this repo.

Known limitations

Deliberate scope cuts for v0.1 — the parser extracts all spec-object attributes dynamically (no fixed schema), but:

• Attributes carried by SPEC-RELATIONs and SPECIFICATIONs themselves are not extracted (only id, type, source/target).
• RELATION-GROUPs, ALTERNATIVE-IDs and vendor TOOL-EXTENSIONS blocks are ignored.
• Datatype constraints (min/max, string length) are not enforced; every value is a string.
• XHTML values are flattened to plain text; embedded objects and formatting are dropped.

Roadmap

• Semantic search over requirement text (embeddings)
• MCP resources (expose specifications as browsable resources)
• Coverage report tool: requirement type A → verifying type B matrix
• Write-back: export a filtered subset as valid ReqIF

License

MIT