hcl-mcp

Overview Schema Source Related Servers Discussions Admin

sensors

Manage named daylight sensor sets for reuse in lighting simulations. Create, list, retrieve, or delete sensor point sets by name.

Instructions

Manage named, persistent daylight sensor sets (reused across annual_daylight / daylight_factor / daylight_savings via sensor_set='name').

action: 'create' | 'list' | 'get' | 'delete'. create: name + (points=[[x,y,z,dx,dy,dz],...] OR grid={interior:[xmin,xmax,ymin,ymax], floor_z, workplane_h?, max_spacing?}); overwrite=True to replace an existing set. list: all saved sets (name, path, count, source). get: name -> the set's points + .pts path. delete: name -> remove the set.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`grid`	No
`name`	No
`action`	Yes
`points`	No
`work_dir`	No
`overwrite`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 9, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does well: it explains overwrite semantics, delete removes the set, and get returns points plus the .pts path. It doesn't cover error cases or side effects beyond overwrite, but core behavior is disclosed.

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?

The description is well-organized with a clear purpose line and a bulleted action list. It front-loads the core concept and each action is described succinctly. Slightly dense but efficient, with no 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?

Given the tool's complexity (6 parameters, 4 actions) and no annotations, the description is reasonably complete. It details points and grid structures, overwrite behavior, and return expectations for get and list. It doesn't specify return values for create/delete, but an output schema exists, which may cover that. Overall, sufficient for an agent to call correctly.

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

Parameters4/5

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

Schema coverage is 0%, so the description is the sole documentation for parameters. It explains the points array format, grid object structure, and overwrite flag. Work_dir is not described, but it is a common auxiliary parameter. The description compensates well for the schema gap.

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 clearly states the tool manages named, persistent daylight sensor sets, listing four concrete actions (create, list, get, delete). It distinguishes itself by noting these sets are reused by other tools via sensor_set='name', making the resource and scope unambiguous.

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 implies when to use it (when managing sensor sets for daylight simulations) but does not explicitly contrast with siblings like workplane_grid or observer_points. However, the CRUD nature and mention of reuse across specific tools provide adequate context.

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