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Validate an IFC file and get its Health Score

validate_ifc
Read-only

Checks a local IFC/BIM model against schema, spatial structure, GlobalId, ISO 19650, classification and MEP rules, returning a 0–100 Health Score, issue counts and top fixes. Files stay on this machine.

Instructions

Check an IFC (BIM) model against the IFC Viewer Online rule set — schema, spatial structure, names, GlobalIds, ISO 19650, classification, MEP and more — and return its Health Score (0–100, higher is better), issue counts, the rules that cost the most points and the first issues with links to fix guides. Runs locally on this machine; the file is not uploaded anywhere. Use it when the user asks whether a model is OK, what is wrong with it, or how good it is.

Input Schema

NameRequiredDescriptionDefault
rulesNoRun only these rule ids (see list_validation_rules). Default: the viewer's default rules.
sourceYesPath to a local file (absolute, or relative to where the server runs) or a public http(s) URL.
maxIssuesNoHow many issues to list (the counts always cover all of them).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond readOnlyHint/openWorldHint by declaring local execution and that 'the file is not uploaded anywhere' — a material privacy/behavior trait an agent would otherwise have to guess. It also previews the response shape (0–100 score, issue counts, highest-cost rules, first issues with fix-guide links), so the agent knows what comes back.

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

Conciseness4/5

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

Front-loaded with the action and the return value, then privacy, then the trigger condition — a sensible order. Slightly long due to the rule-category enumeration, but each clause carries information rather than filler.

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?

With no output schema, the description carries the return-value burden and does so adequately (score, counts, costly rules, sample issues, fix links). It is nearly complete for a read-only validation tool; missing only edge behavior such as unparsable files or runtime expectations.

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

Parameters3/5

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

Schema description coverage is 100%, so all three parameters are already documented, including the list_validation_rules cross-reference and the 'counts always cover all of them' note for maxIssues. The description adds little parameter-level meaning beyond restating that issues are truncated to 'the first issues'; baseline 3 applies.

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?

States a specific verb and resource ('Check an IFC (BIM) model against the ... rule set') plus the distinguishing output, a Health Score. Adjacent siblings such as list_validation_rules and check_ids are clearly different activities, and the enumerated rule categories (schema, spatial structure, GlobalIds, ISO 19650) make the scope concrete.

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?

Gives explicit triggering conditions: 'Use it when the user asks whether a model is OK, what is wrong with it, or how good it is.' It also implicitly routes rule discovery to list_validation_rules via the 'rules' parameter. It stops short of naming when-not-to-use or contrasting itself with check_ids, so it is clear context rather than full routing guidance.

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