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bbruhn91

Aedifion MCP Server

by bbruhn91

get_datapoint_labels

Retrieve all labels assigned to a specific datapoint in an Aedifion project to identify metadata and categorization for building performance data.

Instructions

Get all labels assigned to a datapoint.

Args: project_id: The project's numeric ID. datapoint_id: The datapoint identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
datapoint_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get' implies a read-only operation, it doesn't specify whether this requires authentication, returns paginated results, has rate limits, or what format the labels come in. The description is too minimal for a tool that likely interacts with a labeling system.

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 efficiently structured with a clear purpose statement followed by parameter documentation. Every sentence serves a purpose, though the parameter explanations could be slightly more detailed given the lack of schema descriptions.

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 read operation with 2 parameters and an output schema (which handles return values), the description is minimally adequate. However, with no annotations and 0% schema description coverage, it should provide more behavioral context about authentication needs, error conditions, or response structure to be truly complete.

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?

The description explicitly documents both parameters (project_id and datapoint_id) with brief explanations, which is valuable since the schema has 0% description coverage. While it doesn't elaborate on parameter formats or constraints, it provides essential semantic context that the schema lacks entirely.

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?

The description clearly states the action ('Get all labels') and resource ('assigned to a datapoint'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar sibling tools like 'get_project_labels' or 'get_company_labels', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, related tools, or scenarios where this specific label retrieval is appropriate versus other label-related tools in the sibling list.

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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