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Rubrik MCP

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by rubrikinc

rsc_search_schema

Read-only

Search RSC GraphQL schema operations by natural language to find relevant queries or mutations when you don't know their names.

Instructions

Search the full RSC GraphQL schema to find relevant operations.

Searches operation names/descriptions, field semantics, and type-level vocabulary in one call and returns the best candidate operations ranked by relevance. Use this whenever you need to find an operation and don't already know its name.

The search runs three complementary indexes:

  • Operation index: matches operation names and descriptions directly

  • Field index: finds concepts buried in nested type fields (e.g. "who is logged in" → Group.activeUsers → operations returning Group)

  • Type index: matches domain concepts to operations via aggregate type vocabulary (e.g. "cluster storage runway" → Cluster type → listing ops)

Results are deduplicated and merged; the same operation may be surfaced by multiple indexes and will appear once with the highest score.

Args: search: Natural-language query or keywords describing what you want. Must be non-empty. Use descriptive terms, not operation names. operation_type: Filter results to "query", "mutation", or "all" (default). Use "query" for read-only intent, "mutation" for write intent.

Returns: Dict with: - operations: list of dicts with name, type, description, return_type, score, source (ops/fields/types) - search: the search string used

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchYes
operation_typeNoall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description goes further by disclosing that three indexes (operation, field, type) are queried, that results are deduplicated and merged, and that duplicates surface once with the highest score — behavior an agent cannot get from annotations or schema.

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?

Front-loaded with the one-line purpose, then progressively more detail in the index and Args/Returns sections. The index bullets carry real information about matching semantics rather than filler, so the length is earned.

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?

With no output schema, the description correctly supplies the return shape (operations list with name, type, description, return_type, score, source, plus the echoed search string). Combined with full parameter documentation and the usage trigger, an agent has everything needed to call and interpret it; only the absence of a stated result limit/pagination keeps it from being exhaustive.

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?

Schema description coverage is 0%, so the description must carry the load, and it does: 'search' is constrained to non-empty natural language with the caveat to use descriptive terms rather than operation names, and 'operation_type' is enumerated as query/mutation/all with the default stated. Both parameters are fully specified.

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?

States a specific verb ('Search') and resource (the full RSC GraphQL schema) with clear scope ('to find relevant operations'). The line 'Use this whenever you need to find an operation and don't already know its name' implicitly separates it from naming-based siblings like rsc_describe_operation_full.

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?

Gives an explicit trigger condition ('when you need to find an operation and don't already know its name') and routes read-only vs write intent via operation_type ('query' for read-only intent, 'mutation' for write intent). It stops short of naming the alternative tools to use instead when the operation name is already known.

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