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YARlabs

HyperspaceDB MCP Server

Official
by YARlabs

hyperspace_graph_traverse

Traverse knowledge graphs from a start node in a collection to find logical paths between concepts for complex reasoning and cross-referencing, bounded by max depth and node limits.

Instructions

Deep graph exploration starting from node start_id inside collection. Finds logical paths between concept A and context B for complex reasoning/cross-referencing. Returns a JSON object {paths: [...], nodes: [...], edges: [...]} bounded by max_depth and max_nodes.

Input Schema

NameRequiredDescriptionDefault
start_idYes
max_depthNo
max_nodesNo
collectionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.2.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the burden. It does disclose output shape and that results are bounded by max_depth/max_nodes, which is useful. However, it omits whether traversal is read-only, cost/latency implications of deep exploration, or permission requirements.

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

Conciseness4/5

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

Three tight sentences, front-loaded with the core action, then purpose, then return contract. Little waste; the return-shape sentence is valuable rather than filler.

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

Completeness3/5

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

No output schema, so documenting the return object is a good call, and bounds are explained. But for an unannotated deep-traversal tool, gaps remain around performance cost, read-only safety, and behavior when limits are hit.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates well by explaining start_id (start node), collection, and that max_depth/max_nodes bound the result. It doesn't spell out what the numeric defaults mean semantically, but all four parameters are addressed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Deep graph exploration starting from node `start_id` inside `collection`'. The word 'deep' implicitly distinguishes it from shallow siblings like hyperspace_get_neighbors, but no sibling is named directly.

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

Usage Guidelines3/5

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

It gives a use case ('complex reasoning/cross-referencing') implying when to use it, but offers no explicit when-not conditions and never routes the agent to alternatives such as hyperspace_explore_graph or hyperspace_get_neighbors.

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