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mcp-hyperspace-memory

mcp-hyperspace-memory

Official
by YARlabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

hyperspace_query_planner

Evaluates raw queries before memory retrieval, flags ambiguous intent with multiple-choice clarification, and splits compound questions into parallel sub-queries for focused search.

Instructions

Enterprise Query Planner & Intent Disambiguator. Evaluates raw user queries before issuing memory retrieval. Detects ambiguous intent, returns multiple-choice questions for the user, or splits compound questions into parallel sub-queries.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "raw_user_query": "<raw_user_query>"
    },
    "name": "hyperspace_query_planner"
  }
}

Input Schema

NameRequiredDescriptionDefault
contextNoOptional previous conversation context or chat history to resolve coreferences.
collectionsNoOptional list of available HyperspaceDB collections.
raw_user_queryYesThe raw user query or compliance condition submitted to the agent.
max_sub_queriesNoMaximum number of parallel proof obligations to generate.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.2.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the two possible outcomes (multiple-choice clarifying questions, or parallel sub-queries) and that it produces proof obligations rather than answers, but says nothing about cost/latency of the planning step, failure modes, or how the returned sub-queries are shaped for downstream use.

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 short sentences, front-loaded with the core action and followed by the branching behavior. The "Enterprise Query Planner & Intent Disambiguator" label is slightly promotional filler, but nothing else is wasted.

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?

For a 4-parameter, no-output-schema, no-annotation tool, the description covers purpose and output modes but leaves the return contract under-specified: it does not indicate how sub-queries or clarifying questions are delivered, which matters since there is no output schema to fall back on.

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%, so all four parameters are already documented, and the description adds no syntax or format detail for raw_user_query, context, collections, or max_sub_queries. Baseline 3 applies because the schema does the heavy lifting here.

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?

The description names specific actions (evaluate raw queries, detect ambiguous intent, split compound questions) on a specific resource (user queries before memory retrieval), so the agent knows it is a pre-retrieval planning step. It does not distinguish itself from the very similar sibling memory_query_planner, which is the main clarity gap.

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?

"Evaluates raw user queries before issuing memory retrieval" implies the ordering constraint (call this before recall), which is genuine usage guidance. However, no alternative is named (memory_recall, memory_query_planner), no exclusion conditions are given, and it never says when to skip planning and query directly.

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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