mcp 2.0.0 (unpinned in CI and in the ifcmcp[mcp] extra) renamed
mcp.server.fastmcp.FastMCP to mcp.server.mcpserver.MCPServer, which
ifcmcp does not support yet. server.py caught the resulting
ModuleNotFoundError with a bare except Exception and silently
reported it as FastMCP not installed, masking the real breakage
until the ifcmcp test suite failed in CI.
Pinned mcp to >=1.0,<2 in both ci.yml and ifcmcp's pyproject.toml
mcp extra, confirmed the full ifcmcp test suite (70 tests) passes
against mcp 1.29.0, and confirmed the genuinely-not-installed path
still raises the expected ImportError. Also narrowed the except
clause to ImportError only so an unrelated future bug in that
import block surfaces instead of being swallowed as "not installed".
Generated with the assistance of an AI coding tool.
IfcSpace is not a subtype of IfcElement, so quantify.run_quantify()'s
default selector silently skipped all spaces, reporting
elements_quantified: 0 with no error or warning.
Generated with the assistance of an AI coding tool.
Add ifcquery, ifcedit and ifcmcp to the README contents table, the
Sphinx docs toctree and introduction utilities table. Add new .rst
pages for each package documenting subcommands, installation, usage,
and parameter types. Fix plot and render CLI examples in ifcquery
README to use -o/--out-format flags. Update ifcmcp README to use the
installed ifcmcp command rather than python3 -m ifcmcp.
Generated with the assistance of an AI coding tool.
These three packages were added to src/ but lacked the Makefile needed
by common.mk to build distribution wheels, and the GitHub Actions
workflow to publish them to PyPI.
Adds make dist / make test / make qa targets and ci-*-pypi.yaml
workflows matching the pattern used by ifcpatch, ifcclash, etc.
mcp is an optional dependency so that the embedded API (embedded.py) can
be used from Pyodide without pulling in pydantic-core and the rest of the
MCP protocol stack, which may not be available in all WASM environments.
ifcmcp is a new Model Context Protocol server that wraps ifcquery and ifcedit, holding an IFC model in memory across tool calls. It is the preferred way to interact with IFC models from AI assistants and MCP-compatible clients.
Setup:
claude mcp add --transport stdio ifc -- python3 -m ifcmcp
Session tools: ifc_load, ifc_save
Query tools: ifc_summary, ifc_tree, ifc_info, ifc_select, ifc_relations, ifc_clash, ifc_validate, ifc_schedule, ifc_cost, ifc_schema, ifc_contexts, ifc_materials, ifc_plot, ifc_render, ifc_shape, ifc_shape_list, ifc_shape_docs
Edit discovery: ifc_list, ifc_docs
Edit execution: ifc_edit, ifc_quantify
The model stays in memory between calls - ifc_edit does not auto-save; call ifc_save explicitly when done.
Depends on both ifcquery and ifcedit
Generated with the assistance of an AI coding tool.
Petru Conduraru