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mcp-hyperspace-memory

mcp-hyperspace-memory

Official
by YARlabs
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TypeScript
Hybrid

memory_verify_claim

Verify a logical claim against a premise in 129D MRL hybrid space to prevent hallucinations. Returns trust score, geodesic distances, and geometric explanation for VERIFIED or REJECTED status.

Instructions

Verify whether a logical claim (conclusion) is geometrically consistent with a premise in 129D MRL hybrid space (Lorentz + Cosine). Returns a JSON object with status ('VERIFIED'|'REJECTED'), trust_score (0.0-1.0), geodesic distances, and geometric explanation to prevent hallucinations.

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": {
      "premise": "<premise>",
      "conclusion": "<conclusion>"
    },
    "name": "memory_verify_claim"
  }
}

Input Schema

NameRequiredDescriptionDefault
premiseYesThe known fact or context.
thresholdNoTrust score threshold (default: 0.36).
conclusionYesThe claim to verify against the premise.

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?

With no annotations, the description carries the full burden, and it does disclose the response shape (status VERIFIED/REJECTED, trust_score 0.0-1.0, geodesic distances, geometric explanation). It does not clarify that the operation is non-mutating, how the threshold changes the verdict, or what happens when the premise is unrelated to the conclusion.

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?

Two sentences with the purpose stated first and the return contract second; no filler. The density of internal jargon ('MRL hybrid space', 'geodesic distances') is justified only if the calling agent already knows the system's vocabulary.

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?

There is no output schema, but the description compensates by enumerating the returned fields, and the tool's surface is small (two required strings plus an optional threshold). Missing pieces are secondary: threshold semantics and failure behavior for mismatched premises.

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 premise, conclusion, and threshold are already documented with the default value. The description adds no meaning beyond the schema, such as what a trust_score near the 0.36 threshold implies, so the baseline 3 applies.

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 a specific verb and object ('verify whether a logical claim ... is geometrically consistent with a premise') plus the mechanism (129D MRL hybrid space, Lorentz + Cosine), so the operation is unambiguous. It does not distinguish itself from the similarly named sibling memory_verify_generation, leaving an agent to infer the split.

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?

Usage is only implied by the closing phrase 'to prevent hallucinations', which suggests fact-checking a claim against a known premise. There is no explicit when-to-use, when-not-to-use, or comparison against memory_verify_generation, memory_recall, or memory_query_planner.

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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