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nx-ai-bridge

nx-ai-bridge

by qiuyekai
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edge_blend

Rounds model edges with a constant radius to create fillets. Select edges by ID or nearest point via list_edges, then set the radius in millimetres.

Instructions

Round (fillet) edges with a constant radius. Use list_edges to find edges. Units: millimetres (mm) and degrees.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "radius": "<radius>"
    },
    "name": "edge_blend"
  }
}

Input Schema

NameRequiredDescriptionDefault
edgesNoEdges to modify. Each item is either an edge id from list_edges (e.g. "E:B1:3") or an object {"near": [x, y, z], "body": "B1"?} that picks the edge closest to that point.
radiusYesBlend radius (mm).
all_edges_of_bodyNoShortcut: blend every edge of this body.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It discloses the operation is a geometric mutation producing a uniform-radius blend and states units, but says nothing about whether the result becomes a persistent feature in the model tree, reversibility, or failure conditions (e.g., radius too large for the edge).

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?

Two sentences, front-loaded with the operation and its defining constraint, followed by the one piece of input-sourcing guidance an agent needs. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a three-parameter mutation tool with no annotations and no output schema, the description covers purpose, units, and how to source edge ids. It is silent on precedence between 'edges' and 'all_edges_of_body', on failure behavior, and on what the call returns, which leaves real gaps for a modeling operation.

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%, including the radius unit and the edge-id-or-near-point format, so the schema does the heavy lifting. The description's 'Units: millimetres (mm) and degrees' adds nothing new for radius and mentions degrees, which maps to no parameter and is mildly confusing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource pair ('Round (fillet) edges') and a key constraint ('constant radius'), so the agent knows exactly what geometry operation this performs. It does not explicitly distinguish itself from the sibling 'chamfer', which also modifies edges, leaving that distinction to domain knowledge.

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?

'Use list_edges to find edges' gives concrete prerequisite guidance for obtaining valid inputs, which is the main selection hurdle for this tool. There are no exclusions or when-not-to-use notes (e.g., when to prefer chamfer or how it interacts with existing features).

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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