Skip to main content
Glama

save_hipocampo

Save a memory with auto-generated embedding for future semantic retrieval, skipping duplicates with high similarity.

Instructions

Guarda un recuerdo en el Hipocampo (memoria_vectorial).

Genera embedding automáticamente y persiste el contenido para que sea encontrable por búsqueda semántica futura.

Si ya existe un recuerdo con similitud semántica >0.9, se advierte y se omite el guardado a menos que force=True.

Args: content: Texto del recuerdo a guardar. memory_type: Tipo de memoria. Valores comunes: "event" (evento/experiencia), "decision" (decisión tomada), "profile" (dato personal). Por defecto: "event". code: Código o etiqueta corta para agrupar recuerdos (opcional). Ej: "documentacion", "bugfix", "feature", "setup". categories: Lista de categorías (opcional). Ej: ["python", "mcp", "infraestructura"]. session_id: Opcional. Identificador de sesión para aislar memorias. force: Si True, guarda incluso si existe un recuerdo muy similar. auto_link: Si True, busca recuerdos semánticamente similares (>0.75) y crea enlaces "similar" automáticamente. nivel: Nivel de memoria jerárquica: "episodica" (default) — detalle completo, comprimible, "semantica" — conocimiento consolidado, protegido, "automatica" — regla permanente, nunca se comprime.

Returns: Confirmación con el ID asignado.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
forceNo
nivelNoepisodica
contentYes
auto_linkNo
categoriesNo
session_idNo
memory_typeNoevent

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It details automatic embedding, duplicate detection threshold (>0.9) with warning and skip behavior, force override, auto-linking (>0.75), hierarchical memory levels (episodica, semantica, automatica) and their retention implications, plus the return format. This is rich, non-obvious behavioral information.

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 well-organized: a clear lead sentence, a brief functional overview, a note on duplicate handling, then a structured Args list with examples, and a Returns line. Every sentence adds value, and the length is appropriate for the tool's complexity.

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 tool has 8 parameters and several special behaviors (duplicate detection, auto_link, hierarchical levels), the description covers all essential aspects: the core action, parameter semantics, edge cases (force, similarity thresholds), and return value. No critical gaps remain.

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?

The input schema has 0% description coverage (only titles/defaults), so the description's Args section must compensate. It does so thoroughly, explaining all 8 parameters with types, defaults, and examples, including hierarchical memory levels and category examples. This fully covers the schema's lack of detail.

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 clearly states the tool's purpose: 'Guarda un recuerdo en el Hipocampo' (saves a memory in the hippocampus). It specifies the resource and adds context about automatic embedding and persistence for semantic search, distinguishing it from sibling tools like search_hipocampo or delete_hipocampo.

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 description provides clear context for when to use the tool (saving memories) and describes key behaviors like duplicate detection and force override. However, it does not explicitly contrast with alternatives or state when not to use it, so it stops short of a 5.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/carrasquelalex1/hipocampo'

If you have feedback or need assistance with the MCP directory API, please join our Discord server