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contradicciones_hipocampo

Detect semantic contradictions in stored memories by analyzing negation-probe embeddings. Provide a memory ID to identify conflicting information.

Instructions

Detecta contradicciones semánticas de una memoria existente.

Usa negation-probe embedding: embedea "AFIRMACIÓN: {contenido} — ¿Esto es FALSO según: ..." y compara con el embedding original. Si la distancia de la sonda de negación es MENOR que la del contenido original → señal de contradicción.

Args: memory_id: ID numérico de la memoria en memoria_vectorial.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv6.0.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden, and it explains the negation-probe embedding method, the comparison, and the distance-based contradiction signal. The algorithm strongly implies a read-only detection operation, though side effects are not explicitly addressed.

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?

The description is compact and front-loaded: purpose, method, decision criterion, then arguments. Every sentence contributes useful information without redundancy.

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

Completeness5/5

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

Given the simple one-parameter interface, the existence of an output schema, and no annotations, the description covers the tool's purpose, invocation parameter, and behavioral mechanism. Nothing is missing for an agent to select and call it correctly.

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 coverage is 0%, but the description compensates by documenting memory_id as the numeric ID of the memory in memoria_vectorial. This adds real meaning beyond the integer type in the schema and is sufficient for the single required parameter.

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 description opens with a specific verb and resource: 'Detecta contradicciones semánticas de una memoria existente.' This clearly identifies a unique detection task separate from the sibling search, update, and maintenance tools.

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?

The context is clear: use this tool when you have a memory_id and need to check an existing memory for semantic contradictions. It does not explicitly name when-not-to-use alternatives, but the unique purpose and required memory_id make the intended usage evident.

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