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rush-db

RushDB MCP Server

Official
by rush-db

getSchema

Read-only

Retrieve the RushDB graph schema as structured JSON to inspect labels, properties, relationships, and vector indexes, enabling targeted queries and deeper property drill-down.

Instructions

Returns the same graph schema as getSchemaMarkdown but as structured JSON. Each item has: label (string), count (number), properties (array with id, name, type, optional isArray, recordsCount (number of records that carry this property), min/max for numbers/datetimes, values[] for strings/booleans, and an optional vectorIndexes array), and relationships (array with label, type, direction: in|out, count, and optional edge properties). vectorIndexes is non-empty when one or more embedding indexes exist for that property; each entry has: id, sourceType (managed|external), similarityFunction (cosine|euclidean), dimensions (number), status (pending|indexing|ready|error), modelKey. A non-empty vectorIndexes means the property is queryable with aiSemanticSearch. Use this when you need property id values to pass to propertyValues for deeper drill-down. For initial schema orientation, getSchemaMarkdown uses fewer tokens. Pass force: true to bypass the 1-hour schema cache and force a fresh recalculation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoPass true to bypass the 1-hour schema cache and force a fresh recalculation.
labelsNoOptional: scope schema to specific labels only. Leave empty to get all labels.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds beyond annotations by explaining the 1-hour schema cache, the force=true bypass behavior, and that non-empty vectorIndexes means a property is queryable with aiSemanticSearch. It does not add auth or rate-limit context, but for a read-only schema introspection tool the added cache and semantic-query behavior are substantial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and structured, starting with what the tool returns and then giving usage guidance. However, a large middle portion exhaustively describes output fields even though an output schema exists, and the force behavior is repeated from the input schema. This is information-dense but not optimally concise.

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 gives enough context for an agent to select and invoke the tool: it explains the JSON vs Markdown distinction, the drill-down use case with propertyValues, and the cache-bypass option. Because an output schema exists, it need not explain return values, and the input schema covers the labels parameter. The main minor gap is that label scoping is not mentioned in the description itself.

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 description coverage is 100%, so both parameters are already documented in the input schema. The description repeats the force parameter's cache-bypass meaning but adds no new semantics for force or for the labels scoping parameter. The baseline of 3 is appropriate when the schema already does the heavy lifting.

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 first sentence states a specific verb and resource: 'Returns the same graph schema as getSchemaMarkdown but as structured JSON.' It distinguishes this tool from its closest sibling, getSchemaMarkdown, by format and token cost, and it names another related tool, propertyValues.

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

Usage Guidelines5/5

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

It explicitly says when to use this tool: 'Use this when you need property `id` values to pass to propertyValues for deeper drill-down.' It also states when to prefer the alternative: 'For initial schema orientation, getSchemaMarkdown uses fewer tokens.' This is clear routing guidance with no inference required.

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