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where_is

Find where a data or document type is stored by describing it in plain language. Resolves targets like 'documents' or 'memory' to the appropriate data stores or scopes.

Instructions

Ask the Company Brain where a data or document type lives (data stores / doc scopes).

Resolves a plain-language target (e.g. "documents", "memory", "company scope", "vectors") to the anatomy nodes that store or serve it. Backed by the generated, versioned anatomy catalog. Read-only.

Args: target: What to locate — a data type, document type, or scope name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.1

TDQS

A4.7/5.0
Behavior5/5

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

The description explicitly states 'Read-only', so an agent can infer no mutations or side effects. With no annotations present, this is a clear and sufficient behavioral disclosure.

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 compact and well-structured with a short summary and an Args section. No redundant or irrelevant information is included.

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 covers purpose, usage, parameters, and read-only behavior. It does not explicitly describe the return shape, but the resolved-node concept is sufficient for this simple tool.

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?

The only parameter, target, is fully described with the type of values accepted (data type, document type, or scope name). Examples further clarify the expected input.

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 identifies the tool's function: resolving plain-language data/document/scope names to anatomy nodes. It states the resource (Company Brain) and the output (anatomy nodes) unambiguously.

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 provides concrete example targets ('documents', 'memory', 'company scope', 'vectors') and explains the underlying catalog. It does not explicitly name alternatives, but the usage context is clear enough for this simple lookup tool.

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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