La capa de inteligencia para la memoria de la IA.
Motor de puntuación + grafo causal + gestor de ciclo de vida para la memoria de agentes de IA. Habla MCP de forma nativa.
Genesys es un motor de puntuación, un grafo causal y un gestor de ciclo de vida para la memoria de la IA. Las memorias se puntúan mediante una fórmula multiplicativa (relevancia × conectividad × reactivación), se conectan en un grafo causal y se olvidan activamente cuando dejan de ser relevantes. Se conecta a cualquier backend de almacenamiento y habla MCP de forma nativa.
Related MCP server: tentra
La memoria plana no escala. Volcar todo en un almacén vectorial te da recuperación sin comprensión. La memoria número 500 entierra a las 5 que importan.
Sin olvido no hay inteligencia. Los sistemas de memoria reales olvidan. Sin una poda activa, tu IA se ahoga en un contexto obsoleto.
Sin razonamiento causal. La similitud vectorial no puede responder "¿por qué elegí X?": necesitas un grafo.
Tu IA recuerda todo pero no entiende nada. Genesys soluciona eso.
La mayoría de la gente debería empezar con la Opción 1 (en memoria). Si quieres algo totalmente local sin claves de API, salta a la Opción 3: Obsidian + local.
La forma más rápida de probar Genesys. No requiere base de datos: el estado se mantiene en memoria y, opcionalmente, se persiste en un archivo JSON.
pip install genesys-memory
cp .env.example .env
# Set OPENAI_API_KEY in .env
uvicorn genesys.api:app --port 8000Para persistir entre reinicios, establece GENESYS_PERSIST_PATH en .env:
GENESYS_PERSIST_PATH=.genesys_state.jsonDale esto a Claude para que lo configure por ti: "Instala genesys-memory, crea un .env con mi clave de OpenAI, inicia el servidor en el puerto 8000 con el backend en memoria y conéctalo como un servidor MCP."
Almacenamiento persistente y escalable con búsqueda vectorial mediante pgvector.
pip install 'genesys-memory[postgres]'
cp .env.example .envEdita .env:
OPENAI_API_KEY=sk-...
GENESYS_BACKEND=postgres
DATABASE_URL=postgresql://genesys:genesys@localhost:5432/genesysInicia Postgres y ejecuta las migraciones:
docker compose up -d postgres
alembic upgrade head
GENESYS_BACKEND=postgres uvicorn genesys.api:app --port 8000Dale esto a Claude para que lo configure por ti: "Instala genesys-memory[postgres], inicia un contenedor de Postgres con pgvector usando docker compose, ejecuta las migraciones de alembic, crea un .env con mi clave de OpenAI y DATABASE_URL, inicia el servidor con GENESYS_BACKEND=postgres y conéctalo como un servidor MCP."
Convierte tu bóveda de Obsidian en un almacén de memoria de Genesys. Los archivos Markdown se convierten en nodos de memoria, los [[wikilinks]] se convierten en aristas causales. Un archivo auxiliar de SQLite (.genesys/index.db) gestiona la indexación.
pip install 'genesys-memory[obsidian]'
cp .env.example .envEdita .env:
OPENAI_API_KEY=sk-...
GENESYS_BACKEND=obsidian
OBSIDIAN_VAULT_PATH=/path/to/your/vaultInicia el servidor:
uvicorn genesys.api:app --port 8000En el primer inicio, Genesys indexa todos los archivos .md en la bóveda y genera incrustaciones (embeddings). Un observador de archivos vuelve a indexar de forma incremental cuando editas notas.
Si no se establece
OBSIDIAN_VAULT_PATH, Genesys lo detecta automáticamente buscando.obsidian/en~/Documents/personal,~/Documents/Obsidiany~/obsidian.
Utiliza el proveedor de incrustaciones local para ejecutar el modo Obsidian sin dependencias externas:
pip install 'genesys-memory[obsidian,local]'GENESYS_BACKEND=obsidian
GENESYS_EMBEDDER=local
OBSIDIAN_VAULT_PATH=/path/to/your/vault
# No OPENAI_API_KEY neededuvicorn genesys.api:app --port 8000Esto utiliza all-MiniLM-L6-v2 (384 dimensiones) a través de sentence-transformers para las incrustaciones. El modelo se descarga en el primer uso (~80 MB).
Conecta Claude Desktop: añádelo a tu claude_desktop_config.json:
{
"mcpServers": {
"genesys": {
"url": "http://localhost:8000/mcp"
}
}
}O para Claude Code:
claude mcp add --transport http genesys http://localhost:8000/mcpDale esto a Claude para que lo configure por ti: "Instala genesys-memory[obsidian,local], crea un .env con GENESYS_BACKEND=obsidian, GENESYS_EMBEDDER=local y OBSIDIAN_VAULT_PATH a mi bóveda en [YOUR_VAULT_PATH], inicia el servidor en el puerto 8000 y conéctalo como un servidor MCP. No se necesitan claves de API."
Utiliza FalkorDB (base de datos de grafos basada en Redis) para el recorrido nativo de grafos.
pip install 'genesys-memory[falkordb]'
cp .env.example .envEdita .env:
OPENAI_API_KEY=sk-...
GENESYS_BACKEND=falkordb
FALKORDB_HOST=localhostInicia FalkorDB y el servidor:
docker compose up -d falkordb
uvicorn genesys.api:app --port 8000Dale esto a Claude para que lo configure por ti: "Instala genesys-memory[falkordb], inicia un contenedor de FalkorDB usando docker compose, crea un .env con mi clave de OpenAI y GENESYS_BACKEND=falkordb, inicia el servidor en el puerto 8000 y conéctalo como un servidor MCP."
git clone https://github.com/rishimeka/genesys.git
cd genesys
pip install -e '.[dev]'Dos scripts de utilidad pueblan una instancia de Genesys en ejecución con datos de demostración a través de la API REST. Requieren un servidor en ejecución con la autenticación de Clerk configurada.
cp .env.example .env
# Set CLERK_SECRET_KEY and CLERK_USER_ID in .env
python seed_demo.py # Creates 25 memories with causal edges and runs recall queries
python seed_recalls.py # Runs 5 rounds of recall queries to build reactivation historyAmbos scripts leen las credenciales de las variables de entorno (a través de .env). Consulta .env.example para ver todas las variables requeridas.
claude mcp add --transport http genesys http://localhost:8000/mcpAñádelo a tu claude_desktop_config.json:
{
"mcpServers": {
"genesys": {
"url": "http://localhost:8000/mcp"
}
}
}Apunta tu cliente al endpoint MCP:
http://localhost:8000/mcpHerramienta | Descripción |
| Almacena una nueva memoria, vinculándola opcionalmente a memorias relacionadas |
| Recupera memorias mediante una consulta en lenguaje natural (vector + grafo) |
| Busca memorias con filtros (estado, rango de fechas, palabra clave) |
| Recorre el grafo causal desde un nodo de memoria dado |
| Explica por qué existe una memoria y su cadena causal |
| Obtiene estadísticas del sistema de memoria |
| Fija una memoria para que nunca se olvide |
| Desfija una memoria previamente fijada |
| Elimina permanentemente una memoria |
| Lista las memorias principales, filtradas opcionalmente por categoría |
| Establece las preferencias del usuario para las categorías de memoria principal |
Cada memoria se puntúa mediante tres fuerzas multiplicadas entre sí:
decay_score = relevance × connectivity × reactivationLa relevancia decae con el tiempo. Las memorias antiguas se desvanecen a menos que se refuercen.
La conectividad recompensa a las memorias con muchos enlaces causales. Las memorias "hub" sobreviven.
La reactivación impulsa las memorias que se siguen recordando. La frecuencia importa.
Debido a que la fórmula es multiplicativa, una memoria debe puntuar en los tres ejes para sobrevivir. Una memoria altamente conectada pero nunca accedida sigue decayendo. Una memoria frecuentemente recordada pero causalmente huérfana sigue desvaneciéndose.
STORE → ACTIVE → DORMANT → FADING → PRUNED
↑ │
└── reactivation ────┘
(only if score=0, orphan, not pinned)Las memorias también pueden ser promovidas al estado principal (core): memorias estructuralmente importantes que se fijan automáticamente y nunca se podan.
Probado en el benchmark de memoria de conversaciones largas LoCoMo (1540 preguntas en 10 conversaciones, categoría 5 excluida: preguntas adversarias donde la verdad fundamental contiene errores factuales, por ejemplo, fechas incorrectas y atribuciones de eventos):
Categoría | Puntuación J |
Un salto | 94.3% |
Temporal | 87.5% |
Saltos múltiples | 69.8% |
Dominio abierto | 91.7% |
General | 89.9% |
Modelo de respuesta: gpt-4o-mini | Modelo de juicio: gpt-4o-mini | Recuperación k=20
Como contexto, Mem0 obtuvo un 67.1% y Zep un 75.1% en el mismo benchmark. Los scripts de reproducción completos están en benchmarks/.
Backend | Instalación | Caso de uso |
| Integrado | Cero dependencias, pruébalo |
|
| Persistente, escalable |
Bóveda de Obsidian |
| Base de conocimiento local-first |
FalkorDB |
| Recorrido nativo de grafos |
Personalizado | Trae el tuyo | Implementa |
Copia .env.example a .env y establece:
Variable | Requerido | Descripción |
| A menos que | Incrustaciones |
| No | Procesamiento de memoria LLM (consolidación, detección de contradicciones) |
| No |
|
| No |
|
| Si es postgres | Cadena de conexión de Postgres |
| Si es obsidian | Ruta a tu bóveda de Obsidian |
| Si es falkordb | Host de FalkorDB (predeterminado: |
| No | ID de usuario predeterminado para modo de inquilino único |
Consulta .env.example para ver todas las opciones.
Genesys ha sido creado por Rishi Meka en Astrix Labs. Surgió de la frustración de tener que volver a explicar el contexto del proyecto a Claude en cada sesión. El objetivo es la capa de inteligencia entre tu LLM y tu memoria, totalmente de código abierto.
Consulta CONTRIBUTING.md.
Nota: Las versiones de Genesys anteriores a la v0.3.6 estaban documentadas erróneamente como Apache 2.0. El archivo LICENSE siempre ha contenido el texto de la AGPLv3. Desde la v0.3.6 en adelante, toda la documentación hace referencia correctamente a AGPL-3.0-or-later con un Acuerdo de Licencia de Colaborador.
Permanently delete a memory node and all its edges.
| Name | Required | Description | Default |
|---|---|---|---|
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It reveals permanence and that edges are removed, but does not mention authentication or rate limits. For a simple delete operation, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that directly states the action and scope. There is no unnecessary detail or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive operation, the description conveys the essential information: what is deleted (node and edges) and that it is permanent. No output schema is needed for such a simple action, and the description is complete enough for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not elaborate on node_id. Although the parameter name is self-explanatory, the description could have added more context (e.g., format or example) to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (permanently delete) and the resource (memory node and all its edges). It is specific and distinguishes itself from sibling tools like memory_store or memory_amend.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains what the tool does but does not explicitly contrast with alternatives (e.g., when to delete vs. amend). However, the action is unambiguous and the sibling context implies usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all core memories, optionally filtered by category.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'List' unambiguously indicates a read-only operation with no side effects, providing full behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundant information or extra clutter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides sufficient context for an agent to decide to use it: it lists all core memories with an optional category filter, which is complete for a listing operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The 'category' parameter is described as an optional filter, giving it clear meaning beyond the bare schema type. It does not enumerate possible values, but the purpose is well understood.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list) and the resource (core memories) with an optional filter, distinguishing it from other memory operations such as search or recall.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit comparison is made with sibling tools like memory_search or memory_recall, so an agent may not know when to prefer listing over searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Record a correction: creates a new memory that supersedes an existing one. The old memory is kept (decayed in recall results), not deleted.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | No | ||
| content | Yes | ||
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the old memory is kept but decayed in recall results, which is a behavioral detail beyond the basic schema. It does not mention error cases or effects on nodes, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence that packs the purpose and key behavior without unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool, the description covers the primary purpose and effect, and the parameter names plus context allow an agent to infer usage. Missing explicit parameter meanings are a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions, but the description implies node_id identifies the existing memory. The fields 'reason' and 'content' are self-explanatory in context, but the tool description does not explicitly explain each parameter's role.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool records a correction and creates a new memory that supersedes an existing one, distinguishing it from the sibling memory_store tool which likely creates new memories without superseding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It implies usage for correcting existing memories by saying 'supersedes an existing one', but does not explicitly contrast with memory_store or other siblings. However, the intent is fairly clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain a memory's score breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations (e.g., readOnlyHint, destructiveHint) are provided, and the description does not mention side effects, permissions, or return behavior. It is unclear whether this tool only reads data or modifies anything.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundant words. It directly states the tool's purpose without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool this description is minimally adequate, but it lacks context about the output format or any additional behavior. The missing parameter details and return expectations reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter node_id has no schema description and is not elaborated in the tool description. This leaves ambiguity about what node_id refers to and how it should be supplied.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (explain) and the resource (a memory's score breakdown), distinguishing it from sibling tools like memory_store or memory_search. It is specific enough to understand the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given on when to use this tool versus alternatives. The description implies use when wanting to understand a memory's score, but does not state conditions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recall memories using hybrid search (vector + keyword + graph spreading activation).
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes | ||
| verbosity | No | concise = id/summary/status/score/activation/is_core only, no causal chains. | full |
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of explaining behavior, but it only mentions the hybrid search approach. It does not disclose return format, side effects, or how results are sorted/ranked.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that conveys core functionality without unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (hybrid search with multiple parameters) and the existence of closely related sibling tools, the description lacks detail about expected output, use cases, or how it differs from alternatives. It feels incomplete for an agent to choose confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 25% (only 'verbosity' has a description). The description does not clarify the meaning or effect of 'query', 'k', or 'max_results' beyond what the schema implies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool recalls memories using hybrid search, specifying the resource (memories) and the method (vector + keyword + graph spreading activation). It is distinguishable from siblings like memory_search, though not explicitly contrasted.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as memory_search or memory_traverse. No criteria or context for selection is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filtered vector search by status, category, date, or entity. Pass an EMPTY query to enumerate by recency instead (no vector search, no embedder needed): with since/active_since this answers 'what's new/changed since ' without knowing what to query for.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes | Search text. Empty string switches to enumeration mode: nodes listed by last_reactivated_at descending, honoring the same filters. | |
| filters | No | Supported keys: status (list), category, entity, since (ISO date — created_at >= since; tz-naive treated as UTC), active_since (ISO date — last_reactivated_at >= active_since; tz-naive treated as UTC). With a non-empty query, results are vector-seeded and k-limited; use an empty query for enumeration. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not mention whether the operation is read-only, whether it has side effects, or any auth or rate-limit considerations. Since it is a search tool, it likely does not modify data, but the description does not confirm this, leaving behavior partly unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and mixes the core purpose with the special enumeration mode efficiently. The punctuation and parenthetical asides are slightly complex, but the overall structure is clear and not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main usage modes and filter details, but it does not mention what the return values look like (no output schema) or how the `k` parameter affects the result set. For a search tool, this leaves some context about expected output incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The `filters` parameter receives a detailed explanation covering its keys and semantics, and the `query` parameter's empty-string behavior is well described. However, the `k` parameter is only given a type and default without any explanation of its meaning (e.g., maximum number of results), so parameter semantics are only partially covered.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the core function as a filtered vector search by status, category, date, or entity, and explicitly introduces the enumeration mode with an empty query. This makes the purpose immediately understandable and distinguishable from typical search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides practical guidance on when to use the empty-query enumeration mode, especially for answering 'what's new/changed since <ts>' without needing a query. It does not explicitly compare with sibling tools, but the use cases are well enough defined for the primary scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get graph statistics.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description carries the full burden. It implies a read-only operation via 'Get', but does not explicitly state that it is non-destructive, what data it returns, or any side effects or permissions. The behavior is only minimally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that directly states the purpose. It adheres to the principle of brevity and clarity, with no extraneous words or structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema, the description is incomplete in explaining what the agent should expect from the tool. It does not say what kind of statistics are returned (e.g., counts, sizes, metadata) or how they might be used. An agent would need additional context to correctly interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema coverage is 100%. Per the baseline, a score of 3 is given when the schema fully documents all parameters. The description does not add any additional meaning about parameters, but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action 'Get' and an object 'graph statistics', but the object is vague. It does not specify what kind of statistics, which graph, or how they are presented. It is better than a tautology but lacks specificity to fully distinguish from similar tools like memory_explain or memory_recall.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings. It does not mention any conditions, prerequisites, or alternatives. An agent would have to infer from the name alone that it is for retrieving statistics, with no clear differentiation from other memory tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Store a new memory in the causal memory graph. Use related for writer-specified typed edges (each {id, type}); related_to is legacy and always creates caused_by edges. May return possible_conflicts — heuristic hints, not verified contradictions.
| Name | Required | Description | Default |
|---|---|---|---|
| org_id | No | Required when visibility is 'org'. Must be an org the caller belongs to. | |
| content | Yes | ||
| related | No | Typed explicit edges. Direction: new_node --type--> target. | |
| category | No | Free-form classification (suggested: professional, educational, family, location). | |
| related_to | No | Legacy: ids of nodes to link via caused_by. Prefer `related`. | |
| visibility | No | private | |
| source_session | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses the primary side effect (storing a new memory), the edge-creation behavior, legacy behavior of 'related_to', and notes that 'possible_conflicts' may be returned as heuristic hints. It does not mention authentication or permission side effects, but the core behaviors are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using only two sentences to cover purpose, edge semantics, legacy behavior, and return hints. There is no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential purpose, key parameter distinctions, legacy behavior, and return hints. It omits some details about fields like 'content' and 'visibility', but the overall context is sufficient for a typical agent to correctly invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds value by explaining 'related' edges, the legacy nature of 'related_to', and the direction semantics. However, schema coverage is only 57%, and the description does not compensate for undocumented parameters like 'content', 'visibility', or 'source_session'. It partially clarifies parameters but not comprehensively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's primary action: storing a new memory in the causal memory graph. It distinguishes itself from sibling tools like memory_amend (existing memories), memory_recall, and memory_search by emphasizing 'new memory'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for using the tool, including guidance on preferring the 'related' parameter over the legacy 'related_to' and clarifying that 'related_to' always creates caused_by edges. It does not explicitly say 'use this instead of memory_amend for existing memories,' but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Traverse the memory graph from a starting node. Returns reachable nodes AND the edges of the induced subgraph among them (source/target/type/weight/created_by) — a superset of the BFS tree, so paths can be reconstructed.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| node_id | Yes | ||
| edge_types | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does add behavioral context: it states the return contains both reachable nodes and the edges of the induced subgraph, and explains it is a superset of the BFS tree. However, with no annotations present, it doesn't disclose whether traversal is read-only, whether there are cycle risks, or any side effects, so the full burden is not met.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description uses two sentences with no wasted words. It front-loads the main action, then immediately explains the output's structure and why the superset property matters. Very effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters and no output schema or annotations, the description is incomplete. It leaves depth and edge_types undefined, and it doesn't explain what happens with empty reachability or missing node_id. This is not enough for a confident call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0%, so the description must compensate for node_id, depth, and edge_types. It only mentions a starting node, which maps to node_id; it doesn't explain what depth controls or how edge_types filters traversal. This is a clear gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
This sentence is a precise action: the verb 'Traverse' with resource 'memory graph' and the starting node. It also clarifies the output is a superset of the BFS tree, which sets it apart from sibling tools like memory_recall or memory_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not say when to use this tool instead of alternatives, and it never names a sibling or a use case that would select this over memory_recall or memory_search. There is only an implicit purpose, no explicit when/when-not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pin a memory to core status.
| Name | Required | Description | Default |
|---|---|---|---|
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations accompany the tool, the description carries the full responsibility of explaining behavior. It only says 'Pin a memory to core status' and does not specify what 'core status' implies, whether the operation is reversible, what side effects might occur, or any permission-related constraints. The description effectively relies on the tool name itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise, with a single sentence and no filler. However, this conciseness is achieved by omitting nearly all the information an agent would need; it earns its place as a short purpose statement but does not go beyond that, making the brevity a trade-off against completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and a bare schema with a single undocumented parameter, the description leaves central concepts undefined. An agent cannot infer what 'core status' means, how the memory is located via 'node_id', or what the tool actually does beyond the name. The description is not complete enough to support correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has only one parameter, 'node_id', but its description coverage is 0%. The description does nothing to explain what 'node_id' represents, how to obtain it, or how it relates to the 'memory' being pinned. This is a critical gap because neither the schema nor the description supplies this required meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a clear verb ('Pin'), a specific resource ('a memory'), and the intended outcome ('core status'), and it also implicitly distinguishes the tool from its sibling 'unpin_memory' by describing the opposite action. An agent can confidently connect the tool to its core function without consulting other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use 'pin_memory' versus related tools such as 'memory_store', 'memory_amend', or 'unpin_memory'. It does not mention prerequisites, use cases, or exclusion conditions, so the agent receives no direction about when this tool is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Promote a private memory to org visibility. Caller must own the node and belong to the target org.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | keep_private | |
| org_id | Yes | ||
| dry_run | No | ||
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The main behavior is disclosed (changing a memory from private to org visibility) and the required permissions are stated. However, key behavioral controls in the schema—especially the 'action' enum values and 'dry_run' flag—are unexplained, and no annotations exist to fill that gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two brief sentences with no redundant words or filler. It front-loads the core purpose and then states the key precondition, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is not complete enough for reliable invocation. It omits the semantics of the 'action' enum, the behavior of 'dry_run', expected outcomes, and any edge cases or error conditions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero schema descriptions, the description must compensate. It implicitly covers node_id and org_id ('own the node', 'target org'), but it does not explain the meaning or effects of 'action' or 'dry_run'. Coverage is partial at best.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the primary action: promoting a private memory to org visibility. It also names the resource ('private memory') and the target state ('org visibility'), and it is distinct from sibling tools like pin_memory or memory_store.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a precondition ('Caller must own the node and belong to the target org') but does not say when to use this tool versus alternatives like pin_memory or memory_store. No explicit usage guidance or comparison to sibling tools is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Configure core memory category preferences.
| Name | Required | Description | Default |
|---|---|---|---|
| auto | No | ||
| approval | No | ||
| excluded | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a generic 'configure' verb, the description does not disclose side effects, persistence, permissions, or whether changes are reversible. It is unclear if this tool modifies global settings or per-category rules.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundancy. However, it sacrifices clarity for brevity, leaving out essential details, so it does not fully earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of parameter explanations and usage context, the description is incomplete for an agent to safely and effectively invoke the tool. It does not cover return values, errors, or interactions with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides zero descriptions for parameters (auto, approval, excluded), and the description does not explain their meaning or expected values. The agent cannot determine what these array parameters control or how to populate them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a verb ('Configure') and a resource ('core memory category preferences'), but it is vague about what 'preferences' entails. It does not clarify whether it sets auto-approval, exclusions, or other specific behaviors, making it only partially clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus siblings like memory_store or memory_amend. The description does not indicate scenarios where configuring preferences is appropriate, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Unpin a memory and re-evaluate core eligibility.
| Name | Required | Description | Default |
|---|---|---|---|
| node_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations or output schema. The description mentions side effects vaguely ('re-evaluate core eligibility') but does not explain what happens to the memory or return value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise and direct; no filler or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema or return details, and the side effects of unpinning are under-specified, leaving the agent unsure about the outcome.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter node_id has no description in the schema, and the tool description does not clarify its format or role beyond the obvious identifier meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'unpin' and object 'memory', with an explicit consequence (re-evaluate core eligibility). Distinct from sibling tools like pin_memory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as pin_memory or delete_memory, nor any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0delete_memorylist_core_memoriesmemory_amendmemory_explainmemory_recallmemory_searchmemory_statsmemory_storememory_traversepin_memorypromote_to_orgset_core_preferencesunpin_memoryEach tool targets a distinct operation: store, amend, recall, filtered search, graph traversal, explanation, pinning, unpinning, deletion, stats, preferences, and visibility promotion. memory_recall and memory_search are clearly differentiated as hybrid retrieval versus structured filtering/enumeration.
The memory_* prefix is used consistently for several core operations, but other tools switch to verb_memory forms (pin_memory, delete_memory), noun-like names (memory_stats), or unrelated forms (list_core_memories, set_core_preferences, promote_to_org). The naming is readable but not a uniform verb_noun pattern.
Thirteen tools is well within the ideal range for a memory graph server. Each tool covers a meaningful lifecycle or administrative function without unnecessary redundancy.
The tool set covers the full memory lifecycle: create, read via multiple retrieval modes, amend/supersede, pin/unpin, delete, and administrative operations like stats and preferences. Graph traversal and explanation tools add strong domain coverage with no obvious dead ends.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Graph-native persistent memory for AI agents — 33 MCP tools, zero-LLM writes.
Graph memory for AI agents: entities, cause-effect links, cross-session recall, time travel.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
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