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gwmage

Rootr MCP Server

Create a typed LOG datastore

rootr_create_log_store

Create a typed log store to define fields with various data types and relations, enabling structured data entry and querying.

Instructions

Create a new typed LOG store (a structured, queryable table-like node) in a Rootr (루터) workspace. Each field has a name/type; type is one of string|int|float|bool|datetime|json|enum|level|relation. Relation fields additionally need target (a node id to relate to) and optional many/relation flags. Use rootr_add_log_entries afterward to insert rows, and rootr_query_log_entries / rootr_log_stats to read them back.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the LOG store
fieldsYesField definitions: [{name, type, ...}]. type=string|int|float|bool|datetime|json|enum|level|relation. relation fields add target (node id), many?, relation?.
parentIdNoParent node id to nest the LOG store under
workspaceNoWorkspace id; defaults to ROOTR_WORKSPACE/config if omitted
Behavior3/5

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

Annotations already indicate readOnlyHint=false and destructiveHint=false. The description adds context about field types but does not disclose other behavioral traits like side effects or permissions. It does not contradict annotations.

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?

Three efficient sentences front-loading the main purpose, with no wasted words.

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 the tool's function, field specification, and subsequent steps. However, it does not mention the return value or prerequisites like workspace existence. Given the absence of an output schema, this is a minor gap.

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 coverage is 100% with descriptions for all parameters. The description reiterates some schema info but adds minor detail about relation fields. No significant new semantic value beyond the schema.

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 it creates a typed LOG store, a structured table-like node. It specifies field types and distinguishes from related tools like rootr_add_log_entries.

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 explicitly directs the agent to use rootr_add_log_entries afterward and rootr_query_log_entries/rootr_log_stats for reading. It provides clear context but lacks explicit when-not or alternatives to other create tools.

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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