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List Face Roles

list_face_roles
Read-only

Check semantic face roles on a part and identify tags that no longer resolve on the current geometry.

Instructions

Read back the semantic face roles declared on a part (see annotate_face).

Each entry re-resolves its stored tag against the CURRENT geometry, so a drifted or deleted face is reported rather than silently resolving wrong.

Returns a list (sorted by name) of dicts: name (str) the annotation key role (str) inlet | outlet | sealing | wetted | ambient | mating tag (str) the f_* face tag the role is bound to present (bool) whether that tag still resolves on the current shape index (str) 'FaceN' on the current shape (only when present) meta (dict) the verbatim metadata (only when set)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
handleYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses a key behavioral trait: each entry re-resolves its stored tag against the current geometry, so drifted or deleted faces are reported via a 'present' flag rather than silently misresolving. It also documents the exact output fields, giving the agent full transparency about what to expect.

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

Conciseness5/5

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

The description is structured with a purpose sentence, a behavioral note, and a tidy bulleted return-format list. Every sentence carries information; there is no filler. The most important facts are front-loaded, and the return fields are laid out clearly.

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?

The description provides behavioral details and a complete return schema, which is commendable given there is no output schema. The only gap is the implicit mapping of the 'handle' parameter to the part, which could have been stated in one short clause. Otherwise, an agent has everything needed to call the tool correctly.

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

Parameters2/5

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

The schema has zero description coverage for the only parameter 'handle'. The description mentions 'a part' but does not explicitly state that the handle parameter identifies that part. This is an important gap because the agent needs to know what value to supply. The description does not compensate for the missing schema documentation.

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?

The description starts with a clear verb and resource: 'Read back the semantic face roles declared on a part'. It distinguishes itself from geometry-related siblings by focusing on semantic roles and explicitly referencing annotate_face as the corresponding write operation. The purpose is unambiguous and specific.

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?

The description states the tool's context well: it is the read counterpart to annotate_face. However, it does not explicitly name alternative tools like list_faces or query_faces, nor does it provide when-not-to-use guidance. The context is clear enough for an agent to infer when to call this tool, but explicit exclusions are missing.

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

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