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roynertr

COBie MCP

by roynertr

search_entities

Search COBie entities by type and optional text query in an Excel file, returning matching items with metadata and configurable limit.

Instructions

Search COBie entities (contact, space, floor, type, component, document) by type and optional query.

For filtered lists by floor/space use list_floors, list_spaces, list_components.

Args:
    entity_type: contact|space|floor|type|component|document (plural accepted).
    excel_path: Path to COBie Excel file.
    query: Optional text to match in any cell (case-insensitive substring).
    limit: Max items to return (default 50).

Returns:
    items (list of {id, label, key, sheet, rowIndex, fields}), executed_at, provenance.

Example:
    search_entities("component", "project.xlsx", query="AHU", limit=10)
    # items[].id = "component:AHU-1", items[].fields = {Name, TypeName, ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
excel_pathYes
entity_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, but the verb 'Search' strongly implies a non-mutating, read-only operation. The description does not explicitly address side effects (e.g., rate limits, caching), but the absence of any mutation language and the presence of a plain return structure make the behavior clear enough.

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 concise and well-structured: a purpose line, an alternative-guidance line, a compact parameter list, a return type summary, and an example. No redundant or vague wording.

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

Completeness5/5

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

The return structure is explicitly shown (items with id, label, etc.), an example is provided, and the alternative tools are named. Given the simple parameters and the output schema referenced, the description is complete for an agent to invoke this tool correctly.

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

Parameters5/5

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

All four parameters are described in the description with their roles (entity_type, excel_path, query, limit) and allowed values for entity_type. Even though the schema lacks enums, the description covers the full semantics, making the tool self-explanatory.

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 a specific action ('Search') on a specific resource ('COBie entities') and enumerates the allowed types, distinguishing it from sibling tools that operate on individual entity types or graph structures.

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?

It explicitly names alternatives for filtered lists (list_floors, list_spaces, list_components) with conditions, telling when NOT to use this tool. Does not fully state when to use it (e.g., 'for cross-type searches'), but the intent is inferable from the listed types and optional query.

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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