La base de conocimientos de piloto automático para tu agente de codificación.
Instálalo una vez. A partir de ahí, tu agente construye por sí mismo una base de conocimientos estructurada de tu proyecto, mientras programas. Sin prompts que recordar, sin llamadas remember(), sin API que aprender.
Binario único de Go. SQLite integrado. Cero nube. Sin Docker. Sin Python. Sin tiempo de ejecución de Node.
Agent (Claude Code / Cursor / Kiro / Gemini CLI / ...)
↓ MCP stdio
mnemos serve
↓
Auto-compiled knowledge base (~/.mnemos/mnemos.db)Cada servidor de memoria almacena texto. Mnemos compila una base de conocimientos.
Mientras que otros servidores esperan que tú (o un prompt cuidadosamente ajustado) decidas cuándo almacenar y cuándo recuperar, mnemos ejecuta una tubería completa en segundo plano:
Agent action → mnemos auto-pipeline:
├── Quality gate (reject/rewrite low-value content)
├── 3-tier dedup (hash → fuzzy → semantic)
├── Auto-summarize (extractive, fast; LLM if available)
├── File linking (extract identifiers, link to code)
├── Type classification (episodic / long_term / semantic / working)
├── Quality scoring (for retrieval ranking)
└── Decay scheduling (so knowledge base stays relevant)
Retrieval:
├── Hybrid search (FTS5 + optional semantic + RRF)
├── File-overlap boost (memories about active files rank higher)
├── MMR diversity (kill redundant results)
├── Adaptive packing (full content or summary based on budget)
└── Token-budget cap (always fits in context)No llamas a nada de esto. Tu agente no llama a nada de esto. Los hooks lo activan automáticamente al inicio de la sesión, al enviar un prompt y al finalizar la sesión.
Related MCP server: Gingugu
Capa 1 — Transporte MCP. Servidor MCP estándar, stdio, funciona con cualquier cliente MCP.
Capa 2 — Hooks de piloto automático. Un comando (mnemos setup claude) conecta los hooks + dirección + configuración de MCP. El inicio de sesión inyecta automáticamente el contexto relevante. El envío de prompts realiza búsquedas automáticas al cambiar de tema. El fin de sesión verifica la cobertura.
Capa 3 — Base de conocimientos autocompilada. Puerta de calidad, desduplicación de 3 niveles, resumen automático, vinculación de archivos, ensamblaje de contexto MMR: todo automático. Nunca los activas tú. Incluye un demonio pasivo en segundo plano que detecta continuamente la obsolescencia, contradicciones y relaciones faltantes en tu base de memoria.
Mem0 | Zep/Graphiti | engram | OMEGA | mnemos | |
Nativo de MCP | ✓ | ✓ | ✓ | ✓ | ✓ |
Binario único, sin dependencias de ejecución | — | — | ✓ | — | ✓ |
Cero nube / local-first | parcial | — | ✓ | ✓ | ✓ |
Configuración de piloto automático con 1 comando | — | — | — | — | ✓ |
Puerta de calidad automática | — | — | — | — | ✓ |
Resumen automático | — | — | — | — | ✓ |
Vinculación automática de archivos (consciente de git) | — | — | — | — | ✓ |
Ensamblaje de contexto MMR | — | — | — | — | ✓ |
Demonio pasivo en segundo plano | — | — | — | — | ✓ |
Grafo de conocimiento temporal | — | ✓ | — | — | parcial (decaimiento + reemplazo) |
Costo de autoalojamiento | $0-nube | ~$50/mes (Neo4j) | $0 | $0 | $0 |
Mnemos no intenta ser Zep; es una apuesta diferente. Zep es la mejor respuesta si necesitas razonamiento temporal sobre hechos de negocio y tienes infraestructura empresarial. Mnemos es la mejor respuesta si eres un usuario de agentes de codificación que desea una base de conocimientos de piloto automático que se ejecute sola en tu portátil.
# Homebrew (macOS / Linux)
brew install s60yucca/tap/mnemos && mnemos setup claude
# curl (verify mnemos.dev is live before using)
curl -fsSL https://mnemos.dev/install.sh | bash && mnemos setup claude
# npm (coming in v1.2)
# npx mnemos setup claude
# Build from source (requires Go 1.23+)
git clone https://github.com/s60yucca/mnemos
cd mnemos && make buildCambia claude por cursor, kiro o gemini-cli. Reinicia tu cliente. El piloto automático se ejecuta desde aquí.
mnemos setup <cliente> escribe:
Archivo de dirección (CLAUDE.md, .cursorrules, .kiro/steering/mnemos.md) — le dice al agente qué vale la pena almacenar
Configuración de hooks (.claude/hooks.json o equivalente) — conecta los eventos del ciclo de vida
Configuración de MCP (.mcp.json) — registra mnemos serve como proveedor de herramientas
Tres hooks se ejecutan automáticamente:
Inicio de sesión → mnemos hook session-start
Ensambla memorias relevantes dentro de un presupuesto de tokens (diversificado por MMR, potenciado por archivos). Inyecta en el contexto. Inicio en frío < 200 ms.
Envío de prompt → mnemos hook prompt-submit
Detecta cambios de tema + intención. Busca automáticamente en la base de conocimientos cuando el cambio es significativo. Respeta el tiempo de enfriamiento para evitar ruido.
Fin de sesión → mnemos hook session-end
Verifica si se capturó memoria duradera. Opcionalmente almacena un rastro mínimo. Limpia el estado de la sesión.
La dirección le dice al agente qué vale la pena recordar. Los hooks manejan la recuperación, desduplicación, resumen y vinculación, para que el agente no desperdicie tokens pensando en la logística de la memoria.
Más allá de los hooks, mnemos ejecuta un demonio en segundo plano que mejora continuamente tu base de conocimientos:
Detección de obsolescencia — marca memorias que hacen referencia a archivos eliminados o patrones desactualizados
Detección de contradicciones — encuentra memorias que entran en conflicto entre sí
Inferencia de relaciones — vincula automáticamente memorias relacionadas
Relleno (Backfill) — genera retroactivamente resúmenes para memorias que carecen de ellos
mnemos autopilot status # check daemon state
mnemos autopilot run # trigger immediate run
mnemos autopilot run --dry-run # preview findings without writing
mnemos autopilot report # view latest findingsOperación | 350 memorias | 1,500 memorias |
| 57 ms | 24 ms |
| 55 ms | 22 ms |
| 42 ms | 39 ms |
| 27 ms | 108 ms |
hook session-start (frío) | < 200 ms | — |
tamaño del binario | ~12 MB | — |
Hardware: M1 Pro, 16GB RAM, SQLite en SSD. Tu latencia puede variar.
La mayoría de las operaciones se mantienen por debajo de 60 ms independientemente del tamaño del conjunto de datos. Los subcomandos de hook utilizan el modo InitLight: sin trabajadores en segundo plano, sin interrupción de sesión.
El benchmark de valor (ahorro de tokens, precisión, evitación de errores) está en progreso. Consulta DOGFOODING_RUNBOOK.md para conocer la metodología. Los números reales reemplazarán este marcador de posición antes del lanzamiento público.
Herramienta | Qué hace |
| Almacena una memoria (la tubería automática completa se ejecuta de forma transparente) |
| Búsqueda híbrida FTS + semántica + superposición de archivos con MMR |
| Ensambla contexto diversificado y consciente del presupuesto para el inicio de sesión |
| Obtiene por ID |
| Actualiza contenido, resumen o etiquetas |
| Eliminación lógica (recuperable mediante maintain) |
| Vincula dos memorias (reemplaza, causado_por, depende_de) |
| Ejecuta decaimiento, archivado, GC, detección de obsolescencia |
# Agents call these automatically via MCP. You can also use directly:
mnemos store "JWT uses RS256, 1h expiry, config in auth/config.go"
mnemos search "token expiry"
mnemos stats
mnemos maintainLa mayoría de los usuarios nunca tocan esto. Pero si quieres:
# ~/.mnemos/config.yaml
embeddings:
provider: noop # noop (default) | ollama | openai
# Pure FTS works fine. Enable semantic for meaning-based search.
quality_gate:
min_words: 5
max_words: 200
min_density: 0.3
require_specific: true # long_term memories need project identifiers
duplicate_threshold: 0.8
summarization:
extractive: true # always on, fast, offline
file_linking:
enabled: true # auto-disables outside git
hook:
enabled: true
search_cooldown: 5m
session_start_max_tokens: 2000
mmr_lambda: 0.7 # 0=max diversity, 1=max relevance
file_boost: 0.3
autopilot:
enabled: true
interval: 15m
contradiction_enabled: falseMnemos clasifica automáticamente. Puedes sobrescribir manualmente mediante el flag --type.
Tipo | Tasa de decaimiento | Uso para |
| rápida (~1 día) | tareas pendientes, notas temporales, WIP |
| media (~1 mes) | eventos de sesión, corrección de errores |
| lenta (~6 meses) | decisiones de arquitectura |
| muy lenta | hechos, definiciones, conocimiento |
| rápida | contexto de tarea activa |
Me cansé de volver a explicar mi propio proyecto a Claude Code cada mañana.
Probé los servidores de memoria existentes. La mayoría almacenaba texto bien. Pero todos esperaban que yo —o un prompt cuidadosamente ajustado— decidiera cuándo almacenar y cuándo recuperar. Eso no es una base de conocimientos. Eso es una base de datos con un envoltorio MCP.
Mnemos es lo que construí para hacerlo realmente automático. mnemos setup claude, reinicia el editor y la base de conocimientos se compila sola.
mnemos setup claude # writes CLAUDE.md, .claude/hooks.json, .mcp.json
mnemos setup cursor # writes .cursorrules, .mcp.json
mnemos setup kiro # writes .kiro/steering/mnemos.md, .kiro/mcp.json
mnemos setup gemini-cli # writes GEMINI.md, .gemini/settings.json, .mcp.jsonFlags: --global (instalar para todos los proyectos), --force (sobrescribir existente).
mnemos init # first-time setup
mnemos store "..." # store (auto-pipeline)
mnemos search "auth" # hybrid search
mnemos list --project myapp # list memories
mnemos get <id> # fetch by id
mnemos update <id> --content "..." # update
mnemos delete <id> # soft delete
mnemos relate <src> <tgt> --type supersedes # typed relation
mnemos stats # storage + quality stats
mnemos maintain # decay + stale + GC
mnemos serve # MCP server (stdio)
mnemos version
# Autopilot setup
mnemos setup claude | cursor | kiro | gemini-cli [--global] [--force]
# Passive autopilot daemon
mnemos autopilot status
mnemos autopilot run [--dry-run] [--project <id>]
mnemos autopilot report [--project <id>]
# Backfill
mnemos backfill summaries --project <id> [--dry-run] [--limit N]
# Hook subcommands (called by clients, not manually)
mnemos hook session-start
mnemos hook prompt-submit
mnemos hook session-endNo es un SaaS de memoria para chatbots. Para recordar preferencias de usuario en bots de atención al cliente, usa Mem0.
No es una base de datos de grafos de conocimiento temporal. Para razonamiento de válido-en/inválido-en sobre hechos de negocio, usa Zep.
No es un producto en la nube. No existe la nube de mnemos. Nunca existirá.
No es específico de un framework. Nativo de MCP. Funciona con cualquier cosa que hable MCP.
Mnemos hace una sola cosa: dar a los agentes una base de conocimientos que se compila sola.
Consulta ROADMAP.md. Versión corta:
v1.1.1 (enviado): tubería automática completa, ensamblaje de contexto MMR, recuperación consciente de archivos, demonio de piloto automático pasivo, marco de benchmark
v1.2 (próximo): benchmark de valor público (dogfooding), envoltorio npm, GIF de demostración, lanzamiento en HN
v1.3 (planificado): memoria de equipo a través de git — .mnemos/shared/ compartido para el conocimiento de los compañeros de equipo
v2.0+ (por determinar): alcances de memoria entre proyectos, compactación de memoria, impulsado por los comentarios de los usuarios
MIT
Distill knowledge into a compiled article
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Title/subject of the compiled article | |
| content | Yes | The compiled text | |
| project_id | No | Project scope | |
| source_ids | No | Comma-separated source memory IDs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive behavior, but the description does not explain what gets destroyed or any side effects. It adds no behavioral context beyond the annotations.
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 phrase with no wasted words. However, it may be too brief to be fully informative.
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 4 parameters, no output schema, and annotations indicating destruction, the description is too minimal. It fails to explain how parameters affect the compilation, what the return value is, or side effects.
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 description coverage is 100%, so the schema already defines parameters. The description does not add any additional meaning or usage hints beyond the field names and types.
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 verb 'distill' is specific and the resource 'knowledge into a compiled article' is clear, but it does not differentiate from siblings like mnemos_store or mnemos_update, leaving ambiguity about what 'compile' entails compared to 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 this tool versus alternatives, nor does it mention prerequisites or when not to use it. Siblings are listed but not compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assemble relevant context for a query within token budget
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Context query | |
| max_tokens | No | Token budget (default 4000) | |
| project_id | No | Project scope | |
| include_relations | No | Include related memories |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'assemble context', implying a benign read operation, but annotations indicate destructiveHint=true. The description fails to disclose that this tool may alter or delete state, creating a misleading impression.
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 front-loads the primary action. It is appropriately sized for a simple tool, though it could include a bit more context without becoming 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?
With no output schema and 4 parameters, the description is too sparse. It does not explain what 'context' means, how relations are included, or what the return format is. The behavior around token budget and destructive side effects is not elaborated.
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 description coverage is 100%, so parameters are already well-documented. The description adds no new semantic information beyond echoing 'query' and 'token budget'. Baseline score of 3 applies.
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 uses the verb 'assemble' and resource 'relevant context', which clearly indicates the tool's purpose is to compile context for a query. It distinguishes from siblings like mnemos_search (which likely searches for specific items) and mnemos_store (which saves), but does not explicitly contrast them.
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 like mnemos_search or mnemos_get. The description does not mention prerequisites or conditions under which this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Soft-delete a memory
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The term 'soft-delete' adds value beyond annotations by indicating the operation marks data as deleted without immediate removal. However, no further behavioral details (e.g., reversibility, permission requirements, or state changes) are disclosed.
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 phrase with no wasted words. It is front-loaded and efficient for its length.
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 one-parameter tool with a soft-delete behavior and no output schema, the description is adequate but lacks depth—e.g., it does not clarify how a soft-deleted memory can be recovered or whether this affects search results.
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 description coverage is 100%, so 'id' is already documented. The description adds no additional meaning beyond what the schema provides, earning a baseline score of 3.
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 'Soft-delete a memory' uses a specific verb ('soft-delete') and a clear resource ('memory'), making its purpose distinct from sibling tools like mnemos_store or mnemos_get. No ambiguity.
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 soft-delete versus alternatives (e.g., permanent deletion, updates, or other operations). The description lacks context for choosing this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a memory by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read operation ('Get'), but annotations set destructiveHint=true, implying mutation or deletion. This is a clear contradiction, and the description does not disclose any behavioral traits beyond what annotations provide.
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 fluff. However, it could be slightly more informative without losing conciseness, hence a 4 rather than 5.
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 is provided, and the description does not explain the return format or structure of the memory object. For a retrieval tool, this is a significant omission, leaving the agent uncertain about what data will be returned.
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 100% for the single required parameter 'id', with description 'Memory ID' already present in the schema. The description adds no extra semantic meaning beyond the schema, meeting the baseline but not exceeding it.
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 'Get a memory by ID' clearly states the verb (Get), resource (memory), and method (by ID). This distinctly separates it from sibling tools like mnemos_delete or mnemos_store, serving as a straightforward retrieval operation.
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 provided on when to use this tool versus alternatives (e.g., mnemos_search). The description lacks any context about prerequisites, exclusions, or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run decay, archival, and GC maintenance
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Project scope (empty = all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate destructiveHint=true, so the description adds some context by naming the specific maintenance operations (decay, archival, GC). However, it does not disclose what gets destroyed, whether changes are reversible, or other behavioral implications beyond the annotation.
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, front-loaded sentence with no wasted words. Every part contributes to conveying the tool's purpose.
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 basic purpose but lacks details about side effects, return values, or how the parameter affects execution. Given the absence of an output schema and the destructive nature, more context would be beneficial for safe and effective use.
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 description coverage is 100% for the single parameter, so baseline is 3. The description does not add any additional meaning to the parameter beyond what the schema already provides.
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 verb 'Run' and specifies the resources 'decay, archival, and GC maintenance', which distinguishes it from sibling tools like mnemos_delete or mnemos_store. However, it could be more specific about what each maintenance operation entails.
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 when to run maintenance instead of using mnemos_delete or mnemos_update. No context about prerequisites or typical scenarios is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a relation between two memories
| Name | Required | Description | Default |
|---|---|---|---|
| strength | No | Relation strength [0.0, 1.0] | |
| source_id | Yes | Source memory ID | |
| target_id | Yes | Target memory ID | |
| relation_type | Yes | Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=true and readOnlyHint=false, but the description does not explain whether creating a relation overwrites existing ones or has side effects. The description adds minimal behavioral context beyond what annotations already provide.
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 sentence with no wasted words, delivering the core purpose efficiently.
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?
Despite having 4 parameters and no output schema, the description is very brief. It does not explain the effect of the relation, uniqueness constraints, or behavior on duplicates, leaving the agent with significant gaps.
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 100%, so the schema fully documents all parameters. The description adds no additional meaning to the parameters, meeting the baseline but not exceeding it.
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 creates a relation between two memories, which distinguishes it from siblings like mnemos_store (store a memory) or mnemos_delete (delete). However, it could be more specific about what a relation entails in the memory graph.
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 alternatives, such as mnemos_context for contextual links. The description lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report the live MCP server runtime identity: version, host, pid, executable, uptime, data dir, and project scope
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description indicates read-only behavior ('Report'), but annotations set destructiveHint: true, a direct contradiction. The description fails to disclose any actual destructive behavior.
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?
Single sentence, front-loaded with the action and resource, no wasted 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?
Lists all expected return fields, adequate for a simple info tool. No output schema, so description covers main content, though format or example could strengthen it.
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?
No parameters, so baseline 4. Description adds context about what the tool reports (version, host, etc.), compensating for the lack of parameters.
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?
Description clearly states it reports runtime identity and lists specific items (version, host, pid, etc.). Distinct from sibling tools like mnemos_compile, mnemos_delete, etc., which have different purposes.
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?
Implied usage as a diagnostic/info tool, but no explicit guidance on when to use it vs alternatives. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search memories using hybrid text+semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode: text|semantic|hybrid (default hybrid) | |
| limit | No | Max results (default 10) | |
| query | Yes | Search query | |
| project_id | No | Filter by project |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read-only search operation, but annotations set readOnlyHint=false and destructiveHint=true, creating a contradiction. The description fails to clarify the actual behavioral traits.
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 immediately states the purpose, with no wasted 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 search tool with no output schema, the description is brief and lacks details about return format, pagination, or behavior, though it covers the core functionality.
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 description coverage is 100%, so the description adds no extra meaning beyond the schema. Baseline of 3 is appropriate.
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 searches memories and specifies the method (hybrid text+semantic search), distinguishing it from siblings like mnemos_get or mnemos_delete.
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 implies usage for searching but does not explicitly compare to siblings like mnemos_get or mnemos_context, nor does it provide when-not-to-use guidance.
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 Mnemos
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated tags | |
| type | No | Memory type: short_term|long_term|episodic|semantic|skill|compiled | |
| source | No | Source identifier | |
| content | Yes | Memory content (1 byte to 100KB) | |
| summary | No | Optional summary | |
| category | No | Memory category | |
| project_id | No | Project scope |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Store a new memory' but annotations indicate destructiveHint=true, implying potential data destruction. The description does not address this contradiction or provide any side-effect context, such as overwriting behavior or resource implications.
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?
At 6 words, it is very concise but may be too brief for a tool with 7 parameters and no output schema. It front-loads the purpose but lacks supporting detail, making it adequately concise but not optimally informative.
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 (7 params, no output schema), the description fails to mention return values, confirmation behavior, or the consequences of the 'destructive' annotation. It is incomplete for an agent to use effectively without additional context.
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?
All 7 parameters have schema descriptions (100% coverage), so the description adds no additional meaning. It does not elaborate on how parameters like 'type' or 'tags' affect storage behavior beyond the schema.
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 verb 'store' and resource 'memory' in 'Mnemos', indicating a creation operation. However, it does not differentiate from sibling tools like mnemos_update or mnemos_relate, leaving ambiguity about when to use this tool over others.
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 alternatives. There is no mention of prerequisites, exclusions, or context for choosing store over related tools like compile or update.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update a memory (PATCH semantics)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID | |
| tags | No | New comma-separated tags | |
| content | No | New content | |
| summary | No | New summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, which aligns with 'Update'. The description adds 'PATCH semantics' but no extra behavioral details (e.g., what happens to unspecified fields, authorization needs). It is minimally 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?
A single sentence that is perfectly concise and front-loaded. Every word contributes essential information (action, resource, update semantics).
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 tool with no output schema, the description lacks context about return values, side effects, or behavior on error. More detail would be needed for safe 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?
Input schema covers all 4 parameters with descriptions (100% coverage). The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.
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 'Update a memory (PATCH semantics)' clearly states the action (update) and the resource (memory). The parenthetical note adds specificity about partial updates, distinguishing it from siblings like mnemos_store (create) or mnemos_delete.
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 vs alternatives like mnemos_get or mnemos_search. No prerequisites or exclusions are mentioned, leaving the agent without decision context.
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.
v1.2.1mnemos_runtimev1.1.14mnemos_compilemnemos_store1 field changedInput schema / properties / type / descriptionPrevious value: -"Memory type: short_term|long_term|episodic|semantic"New value: +"Memory type: short_term|long_term|episodic|semantic|skill|compiled"v0.1.2mnemos_relate1 field changedInput schema / properties / relation_type / descriptionPrevious value: -"Relation type"New value: +"Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by"v0.1.0mnemos_contextmnemos_deletemnemos_getmnemos_maintainmnemos_relatemnemos_searchmnemos_storemnemos_updateEach tool has a clearly distinct purpose: CRUD operations (store, get, update, delete), search, maintenance, relation creation, context assembly, compilation, and runtime info. No two tools overlap in functionality, ensuring an agent can easily select the correct tool.
All tools follow a consistent 'mnemos_' prefix with an underscore-separated verb or noun. Most use imperative verbs (compile, delete, get, maintain, relate, search, store, update), while 'context' and 'runtime' are nouns. This minor inconsistency prevents a perfect score.
With 10 tools, the surface is well-scoped for a memory/knowledge server. It covers essential CRUD, search, maintenance, relations, and advanced features like compilation and context assembly without being overwhelming or sparse.
The tool set covers the full memory lifecycle (create, read, update, soft-delete) plus advanced operations (compile, context, relate, maintain, runtime). Minor gaps include missing batch operations or explicit undo for soft-delete, but the core domain is well-served.
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Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
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Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
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