mcp-light-memory
¿Qué es esto?
MCP Light Memory es un sistema de memoria persistente ligero, local-first, para agentes de codificación y clientes MCP (Warp, OpenCode, JetBrains AI Assistant / PyCharm, Claude Code, Cursor). Actúa como una capa de checkpoint + recuperación — almacena el estado durable mínimo necesario para reanudar trabajo complejo entre sesiones, sin mantener la conversación completa en la ventana de contexto del modelo.
Cuando tu agente inicia una tarea, llama a context y recibe decisiones pasadas relevantes, trampas, restricciones e hipótesis — clasificadas, deduplicadas y limitadas por confianza. Cuando termina, guarda un checkpoint del estado de trabajo. En la siguiente sesión, incluso después de un reinicio, la memoria está ahí.
Related MCP server: mcp-chest-memory
¿Por qué usarlo?
Problema | Cómo lo resuelve MCP Light Memory |
Los agentes olvidan todo entre sesiones | Los archivos Markdown persisten en disco; el agente los recupera mediante BM25 + embeddings opcionales |
El historial completo de la sesión es demasiado grande para el contexto | Solo se recuperan las memorias relevantes (con presupuesto de tokens, diversificadas con MMR) |
Dependencia de la nube / preocupaciones de privacidad | 100% local, sin conexión, cero llamadas de red, sin demonio |
Configuración pesada / dependencias | Cero dependencias de ejecución requeridas (stdlib puro de Python 3.8+); |
Inyección de prompts mediante memoria almacenada | Cada memoria recuperada es explícitamente evidencia |
Aislamiento multi-proyecto | Router con lista de permitidos del registro, límite estricto |
Deriva del protocolo MCP | Soporte de doble era: moderna |
Cómo funciona (mecanismos)
Markdown es la fuente de verdad. Cada memoria es un archivo
.mdcon frontmatter YAML (id,type,status,tags,sources,links,valid_from,valid_to,supersedes). Legible por humanos, diferenciable, durable.SQLite es una caché reconstruible. Índice BM25/FTS5 + vectores de embeddings opcionales + seguimiento de uso. Bórralo y todo se reconstruye desde Markdown.
Recuperación: BM25 en Python puro + embeddings densos opcionales → fusión RRF → diversificación MMR → refuerzos de política (tipo/estado/temporal) → corte por presupuesto de tokens. Modo adaptativo: primero disperso, denso solo si es débil.
Ciclo de vida:
remember→update→supersede(enlaza en ambas direcciones, nunca elimina historial) →forget(archiva, nunca elimina) →timeline(vista temporal).search --at YYYY-MM-DDpara consultas históricas.Límite de confianza: el contenido recuperado se envuelve en
=== BEGIN/END INTERNAL_RAG MEMORY ===con un encabezadoSECURITY NOTICE. El JSON/MCP estructurado llevatrust: untrusted+security_flags: ["instruction_like_content"]opcional.Actualidad de la evidencia: cada resultado incluye
evidence_state(present/missing/unverifiable) para evidencia local tipo ruta — derivado en el momento de la recuperación, nunca persistido.Router multi-proyecto: un servidor MCP stdio frente a muchos proyectos mediante un registro JSON.
write:falsebloquea las herramientas de mutación antes de lanzar un subproceso hijo. Aislamiento por subproceso por llamada (sin estado compartido).
Configuración
Requisitos previos
Python 3.8+ (usa el lanzador
py,pythonopython3— el instalador detecta automáticamente el intérprete real y rechaza el stub de WindowsApps)Git (el proyecto de destino debe ser un repositorio git)
Opcional:
pip install sentence-transformers numpypara una mejor recuperación semántica
La versión actual está definida por el archivo VERSION — consúltalo (o ejecuta mlm.py --version) en lugar de codificar un número esperado.
Inicio rápido
Clona este repositorio una vez y luego instala en cualquier proyecto:
# Windows (PowerShell)
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python ~/mcp-light-memory/install.py . --client warp# Linux/macOS
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python3 ~/mcp-light-memory/install.py . --client warpEl instalador:
copia los archivos de habilidades + crea
INTERNAL_RAG/+AGENTS.mdejecuta
init+checkpoint+validate(para queguardestéOKinmediatamente)registra automáticamente el servidor MCP en la configuración del cliente cuando puede hacerlo de forma segura (o informa
MANUAL_REQUIRED/ imprime instrucciones para JetBrains)escribe la ruta absoluta al intérprete de Python verificado (sobrevive a problemas de PATH en Windows)
python .agents\skills\internal-rag\mlm.py --version # reports the installed version
python .agents\skills\internal-rag\mlm.py status # expect: INTERNAL_RAG ready
python .agents\skills\internal-rag\mlm.py guard # expect: GUARD OKMatriz de instalación
Un instalador, cuatro clientes, dos ámbitos de configuración. Guía completa: docs/INSTALLATION.md.
Cliente | Ámbito de proyecto | Ámbito global |
Warp (escritura de configuración automática; la activación del proyecto puede requerir aprobación) |
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OpenCode stable (V1) (automático para escrituras seguras de configuración JSON) |
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OpenCode 2 (V2, beta) (automático para escrituras seguras de configuración JSON) |
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JetBrains AI / PyCharm (manual en la interfaz del IDE) |
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--globalcambia el ámbito de la CONFIGURACIÓN DEL CLIENTE (~/.warp/.mcp.jsonvs{repo}/.warp/.mcp.json,~/.config/opencode/opencode.jsonvsopencode.jsondel proyecto). El servidor sigue apuntando al proyecto de destino en el que instalaste.¿Necesitas un endpoint MCP global para muchos repositorios? Usa el router multi-proyecto — docs/MCP-MULTI-PROJECT.md.
JetBrains/PyCharm es asistido, no totalmente automático: el instalador prepara el JSON + el Directorio de Trabajo; tú añades el servidor en Settings → Tools → AI Assistant → MCP y eliges Nivel de servidor = Proyecto o Global.
Configuración manual (sin instalador) por cliente: docs/INSTALLATION.md + páginas de clientes (Warp · OpenCode).
Zero-shot: prompts de copiar y pegar para Warp y OpenCode
Puedes pegar uno de estos directamente en el agente del cliente. Reemplaza C:\Projects\App con la ruta real del repositorio de destino.
Warp — instalar para un proyecto:
Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in Warp, using project scope. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, update it with git pull --ff-only. Apply the canonical installation contract from the repository and run install.py with TARGET_PROJECT=C:\Projects\App and --client warp without --global. Do not force-overwrite an existing configuration. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification. If Warp requires an additional project activation/toggle/approval, state the exact client-side step and do not claim the server is active before it is completed.Warp — configuración global del cliente para un proyecto:
Install and configure MCP Light Memory (mcp-light-memory) in Warp globally for project C:\Projects\App. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Apply the canonical installation contract and run install.py with TARGET_PROJECT=C:\Projects\App, --client warp, and --global. Remember: --global means the global Warp client configuration, while the server must still be bound to C:\Projects\App; do not use the multi-project router. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification.OpenCode — instalar para un proyecto (stable/V1):
Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in OpenCode. By "OpenCode" I mean stable/V1, so use --client opencode, not opencode2. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App and --client opencode without --global. Do not force-overwrite an existing configuration. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED (for example because opencode.jsonc exists), do not report success: safely edit the JSONC while preserving comments and unrelated settings if you have appropriate file-editing tools; otherwise report the exact manual action required. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the OpenCode configuration contains mcp-light-memory and C:\Projects\App.OpenCode — configuración global del cliente para un proyecto (stable/V1):
Install and configure MCP Light Memory (mcp-light-memory) globally in OpenCode for project C:\Projects\App. By "OpenCode" I mean stable/V1, so use --client opencode. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App, --client opencode, and --global. --global means the global OpenCode client configuration, while the server must still be bound only to C:\Projects\App; do not use the multi-project router. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED, do not report success and follow the safe JSONC instructions. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global OpenCode configuration contains mcp-light-memory and the C:\Projects\App path.Para OpenCode 2 / V2, usa los mismos prompts pero indica explícitamente OpenCode 2 / V2 y exige --client opencode2. Más variantes: docs/ZERO-SHOT-SETUP-PROMPTS.md.
Detalles de configuración
Warp
Warp lee las configuraciones del servidor MCP desde ~/.warp/.mcp.json (global, auto-inicio) o
{repo}/.warp/.mcp.json (proyecto, requiere un interruptor manual según la documentación de Warp).
Forma: mcpServers.<name> con command, args, working_directory (configúralo siempre — el almacén de memoria se resuelve a partir de él). Consulta examples/warp.example.json y docs/WARP-SETUP.md.
OpenCode stable (V1)
OpenCode lee opencode.json/.jsonc en la raíz del proyecto, o
~/.config/opencode/opencode.json globalmente. Los servidores V1 son planos bajo
mcp.<name> (sin subclave servers) con enabled: true y command como
array — consulta examples/opencode-legacy.example.json y docs/OPENCODE.md.
OpenCode 2 (V2, beta)
Mismos archivos de configuración, forma diferente: mcp.servers.<name>, command como
array, y sin campo enabled (V2 desactiva mediante disabled: true) —
consulta examples/opencode-v2.example.jsonc y docs/OPENCODE.md.
JetBrains AI Assistant / PyCharm
PyCharm NO lee automáticamente ningún archivo de configuración MCP. El instalador imprime
el JSON listo para pegar + el Directorio de Trabajo; tú añades el servidor en
Settings → Tools → AI Assistant → MCP (STDIO) y eliges Nivel de servidor =
Proyecto o Global. Consulta examples/jetbrains.example.json.
Router multi-proyecto
Una conexión MCP frente a muchos proyectos — lista de permitidos del registro, límite estricto write:false, aislamiento por subproceso por llamada.
Archivo de registro (projects.json)
{
"projects": {
"backend": { "root": "/abs/path/backend", "write": true },
"shared-lib": { "root": "/abs/path/shared-lib", "write": false }
}
}Configuración de Warp para el router
{
"mcpServers": {
"mcp-light-memory-router": {
"command": "python3",
"args": ["/abs/path/mcp-light-memory/.agents/skills/internal-rag/irag_mcp_router.py", "--registry", "/abs/path/projects.json"],
"working_directory": "/abs/path/mcp-light-memory"
}
}
}Consulta docs/MCP-MULTI-PROJECT.md para más detalles.
Flujo de trabajo
context --task "current task"
↓
recovery, if required (RECOVERY REQUIRED)
↓
checkpoint before first change
↓
implementation
↓
checkpoint after each milestone
↓
guard before finishingComandos principales (alias CLI: mlm.py o irag.py heredado):
mlm.py context --task "..."
mlm.py checkpoint --reason "..."
mlm.py search --query "..." --limit 8
mlm.py remember --type decision --title "..." --body "..."
mlm.py show <ref>
mlm.py update <ref> --status superseded
mlm.py status
mlm.py guard
mlm.py validate
mlm.py doctorMapeo de rutas (rebranding: internal-rag → MCP Light Memory)
Nuevo nombre | Ruta heredada (conservada por compatibilidad) |
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La carpeta en disco INTERNAL_RAG/ y el directorio de habilidades .agents/skills/internal-rag/ se mantienen intencionalmente con sus nombres heredados para compatibilidad hacia atrás de migración cero. Consulta docs/MIGRATION-TO-MCP-LIGHT-MEMORY.md.
Memoria durable (CRUD)
remember --type decision --title "..." --body "..." --tags "a,b" --evidence "src/x.py:42" --links "decisions/other.md"
show <path-or-id>
show <ref> --section Knowledge
update <ref> --add-tags "new" --append "New evidence: ..."
supersede <ref> --by <new> --reason "..."
forget <ref> # archives, does not delete
link --from <ref> --to <ref>
timeline --limit 20
status
historyTipos: decision, knowledge, constraint, gotcha, failure, hypothesis, session.
Pila de tareas (interrupciones)
mlm.py push --task "interrupted work" --reason "user-priority"
mlm.py tasks
mlm.py resume
mlm.py forget-task <id> # drop a specific task
mlm.py forget-task # clear the whole stackConfiguración (.irag.yml, opcional)
retrieval:
limit: 10
mmr_lambda: 0.4
min_score: 0.3
embeddings: auto # auto | on | off
profile: english-fast # english-fast (default) | multilingual (PL/EN projects)
embeddings_model: null # explicit model overrides the profile
tokens:
context_budget: 5000
checkpoints:
auto_archive_sessions: true
max_task_stack: 24mlm.py config muestra la configuración efectiva. mlm.py config --init escribe una plantilla.
Embeddings opcionales (mejor recuperación)
pip install -r requirements-optional.txtCuando el paquete está disponible y .irag.yml tiene embeddings: auto (predeterminado), la recuperación usa embeddings con respaldo a BM25. Anula en tiempo de ejecución con --embeddings on|off|auto.
Dos perfiles de recuperación (consulta docs/EMBEDDINGS.md):
english-fast(predeterminado,all-MiniLM-L6-v2)multilingual(intfloat/multilingual-e5-small) — para proyectos polaco-inglés
Sin conexión / aislado de red
python pack.py --with-embeddings --profile english-fast
# -> internal-rag-offline-1.8.1.zip (name from pack.py; 1.8.1 = VERSION file)
# On the air-gapped machine:
unzip internal-rag-offline-*.zip -d internal-rag-offline
pip install --no-index --find-links wheels/ -r requirements-optional.txt
python install.py "/path/to/project" --client <warp|opencode|opencode2|jetbrains>Consulta docs/OFFLINE.md para más detalles.
Privacidad y Git
El modo de instalación predeterminado es solo local. El instalador usa .git/info/exclude, no el .gitignore del proyecto, para que la memoria local y los archivos de integración no se confirmen accidentalmente.
Antes de publicar un proyecto:
python .\privacy_check.py "D:\path\to\project"Esperado: RESULT: PASS
Eliminación completa de un proyecto
python .\uninstall.py "D:\path\to\project"El desinstalador crea una copia de seguridad fuera del repositorio y luego elimina INTERNAL_RAG y sus integraciones. Usa --keep-memory para conservar los datos de memoria.
Documentación
Estructura en un proyecto de destino
project/
├── AGENTS.md
├── .irag.yml # optional config
├── INTERNAL_RAG/
│ ├── WORKING_STATE.md
│ ├── INDEX.md
│ ├── .checkpoint.json
│ ├── decisions/ knowledge/ gotchas/ failures/ hypotheses/ sessions/ archive/
│ └── exports/
├── .agents/skills/internal-rag/
│ ├── SKILL.md
│ ├── mlm.py # primary CLI (forwards to irag.py)
│ ├── irag.py # core (legacy alias, still the canonical module)
│ ├── irag_embeddings.py # optional plugin
│ └── irag_hooks.py # optional git hooks
└── .opencode/ # OpenCode integration (optional)Fuente de verdad
instrucciones actuales del usuario, 2. código/pruebas/configuración actuales, 3. especificaciones/ADR, 4. memoria verificada, 5. notas de sesión, 6. hipótesis.
La memoria puede estar desactualizada. El código tiene prioridad.
Licencia
MIT.
Registro de cambios
1.8.0 — Configuración manual de JetBrains
--client jetbrainsya no escribe un archivo de configuración falso (PyCharm ignora los archivos de configuración de MCP). En su lugar, imprime JSON listo para pegar e instrucciones del menú del IDE.--unregister --client jetbrainsimprime un recordatorio para eliminar en la interfaz del IDE.
1.7.2 — JetBrains cwd + mensajes específicos del cliente
JetBrains: escribe
working_directorycomo sugerencia e imprimeWARNINGcon la ruta exacta para configurar enSettings → Tools → AI Assistant → MCP.Mensajes de reinicio específicos del cliente (Restart PyCharm / Restart Warp / Restart OpenCode).
Memory store: <path>se imprime en la salida de instalación para verificación inmediata.
1.7.1 — Corrección del stub de Python en Windows
detect_python()rechaza el stub de 0 bytes de WindowsApps; prefierepy -0p; verifica cada candidato con--version.Verificación posterior al registro: ejecuta
--versioninmediatamente después de escribir la configuración e informaPASS/FAIL.--unregisterelimina archivos de configuración vacíos y directorios principales (corrige el esqueleto muerto.warp/.mcp.json→GUARD STALE).
1.7.0 — Reforma a MCP Light Memory
Reforma total de
internal-raga MCP Light Memory (mcp-light-memory). Nuevo alias de CLImlm(mlm.py). Recursos de logotipo/icono. Documento de migración. Lista de verificación de reforma de GitHub.Compatible con versiones anteriores:
irag.py,INTERNAL_RAG/, nombres antiguos del servidor MCP conservados como alias obsoletos.18 pruebas de consistencia de reforma.
1.6.1 — Endurecimiento posterior a v1.6
Benchmark de mutación/ciclo de vida (11 escenarios). Límite de confianza (ADR-015):
trust: untrusted+security_flags. Frescura de evidencia (ADR-016):evidence_state. Benchmark de escala (100/1k/10k). Regresiones de seguridad del router (+12 pruebas). Prueba de consistencia de documentación. 249 pruebas superadas.
1.6.0 — Calidad de recuperación + MCP 2026-07-28
Benchmark de calidad de memoria (37 casos). MCP
2026-07-28de doble era (server/discover,_meta,structuredContent,outputSchema).writeestricto del registro. Fuentes en el prefijo del fragmento. Recuperación adaptativa. Contexto consciente de enlaces.consolidate --prepare. Benchmark de latencia del router. ADR-010…016.
1.5.0 — Puerta de abstención + router multiproyecto
Puerta de relevancia/abstención (
--meta). Prefiltro de candidatos FTS5. Router MCP multiproyecto. Endurecimiento del protocolo MCP (stdout puro, verificado con SDK). 168 pruebas.
1.4.0 — Fragmentación + deduplicación + ciclo de vida temporal
Fragmentación consciente de secciones (esquema v3). Deduplicación SimHash. Perfil multilingüe PL/EN. Ciclo de vida temporal (
valid_from/valid_to/supersedes/--at).consolidate --dry-run.
1.3.0 — Caché de embeddings persistente
BLOBs float32 a nivel de fragmento en SQLite. Múltiples modelos coexisten.
index --vacuum/--embed-missing.
1.0.2 — Presupuesto de tokens + privacidad
Aplicación del presupuesto de tokens. Detección de memoria obsoleta. Detección de duplicados. Escaneo de privacidad al escribir. Temporizador de checkpoint automático. Paquete sin conexión/aislado.
1.0.0 — Lanzamiento inicial
Recuperación BM25 + MMR. CRUD completo de memoria. Pila de tareas. Servidor MCP (JSON-RPC stdio). Git hooks. Diagnósticos. Exportación/importación. Presupuesto de tokens.
Available Tools
8 toolscheckpointSave Task CheckpointA
Persist current operational task state so work can be resumed after interruption or context loss. Use at meaningful milestones and before ending a work session; use remember for reusable knowledge. This writes local checkpoint/task state, does not delete durable memories, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| next | No | Recommended next action when work resumes. | |
| phase | No | Current task phase or milestone name. | |
| reason | Yes | Why this checkpoint is being created. | |
| blockers | No | Known blockers or unresolved issues. | |
| completed | No | Work completed since the previous checkpoint. | |
| in_progress | No | Work currently underway. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the safety profile (readOnly=false, destructive=false, openWorld=false), and the description adds the local-write scope, confirms no durable-memory deletion, and states no network access. It does not clarify whether checkpoints overwrite prior state or how the non-idempotent behavior manifests, so it is strong but not fully exhaustive.
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?
Two sentences, front-loaded with the core action, then usage and behavioral caveats. Every clause earns its place with no redundancy.
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, the description adequately covers when to call it, what state it writes, and what it does not touch, which is sufficient for a checkpoint write. It could be slightly more complete on resume interaction with the sibling 'resume' 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?
Schema description coverage is 100%, so all six parameters (reason, next, phase, blockers, completed, in_progress) are already documented in the schema. The description adds no parameter-level detail beyond what the structured fields provide, making the baseline 3 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?
States a specific verb and resource ('Persist current operational task state') with the added purpose of resuming after interruption or context loss. It explicitly distinguishes itself from the sibling 'remember' by noting that tool is for reusable knowledge, so an agent can tell them apart without opening schemas.
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?
Gives explicit when-to-use guidance ('at meaningful milestones and before ending a work session') and names the alternative ('use remember for reusable knowledge') with the condition that selects it. Nothing is left to inference about timing or routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
contextLoad Task ContextA
Start or resume a task by retrieving relevant durable project memories and current working state. Use before project changes or after a context reset; use search for a focused lookup. This call may update task or usage state, is non-idempotent, does not delete durable memories, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Short description of the task being started or resumed; used to retrieve relevant project context. | |
| limit | No | Maximum number of relevant memories to include in the context packet. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, idempotentHint=false, openWorldHint=false, and destructiveHint=false, so part of the description ('non-idempotent', 'no network access') restates structured data. It does add value beyond them by disclosing what state is touched ('may update task or usage state') and a non-obvious negative guarantee ('does not delete durable memories').
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?
Two sentences, no waste, with the core action front-loaded and usage/behavioral caveats packed into the second sentence. Every clause carries 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?
For a 2-parameter, non-destructive mutation with full annotation coverage and no output schema, the description covers trigger, alternative, state mutation, idempotency, and network behavior. It stops short of describing what the returned 'context packet' contains, a minor gap given no output schema is declared.
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 both 'task' and 'limit' are already documented in the schema. The description adds no syntax, format, or defaulting detail beyond that, so the baseline 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?
States a specific verb ('Start or resume a task') and resource ('durable project memories and current working state'), and explicitly routes the focused-lookup case to the sibling 'search'. An agent can distinguish it from search, resume, and checkpoint without opening any schema.
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?
Gives concrete triggers ('before project changes or after a context reset') and names the alternative tool and the condition that selects it ('use search for a focused lookup'). Nothing about when-to-use is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guardCheck Checkpoint FreshnessARead-onlyIdempotent
Check whether project state changed since the last checkpoint. Use before finishing a task to detect uncheckpointed work, then checkpoint if stale. This operation is read-only and idempotent, does not modify memory or task state, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | True when project state is consistent with the last checkpoint. |
| fingerprint | No | Fingerprint representing the current project state used by the guard check. |
| changed_files | No | Project files detected as changed since the checkpoint baseline. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, and the description largely restates these facts ('read-only and idempotent', 'no network access'). It adds only marginal new context via 'does not modify memory or task state', which is implied by readOnlyHint. With annotations carrying the safety profile, this is adequate but adds little.
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?
Three short sentences, with the core purpose and the usage trigger front-loaded before the safety caveat. The final sentence is largely redundant with the annotations, which slightly dilutes the otherwise tight 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?
For a zero-parameter, read-only check with an output schema present, the description covers purpose, when to call it, what to do with the result, and the safety profile. Return-value semantics are left to the output schema, which is appropriate.
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 takes zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. No parameter-related claims are made or 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?
States a specific verb (check) and resource (whether project state changed since the last checkpoint), which is a distinct operation from the sibling 'checkpoint'. The name 'guard' is opaque on its own, but the description resolves it immediately.
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?
Explicitly names the trigger ('Use before finishing a task to detect uncheckpointed work') and the conditional follow-up action ('then checkpoint if stale'), routing the agent to the sibling tool under a stated condition. Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberStore Durable MemoryA
Store durable project knowledge that should survive across sessions. Use for stable decisions, constraints, gotchas, failures, hypotheses, or reusable knowledge; use checkpoint for temporary task progress. This writes local durable memory, does not delete existing memories, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | Durable content to preserve: a decision, fact, constraint, gotcha, failure, hypothesis, or reusable session knowledge. | |
| tags | No | Optional comma-separated tags used to organize or retrieve the memory. | |
| type | Yes | Memory category describing the kind of durable knowledge being stored. | |
| scope | No | Optional scope describing where this memory applies. | |
| title | Yes | Short, specific title for the memory. | |
| status | No | Memory confidence state: active for established knowledge or tentative for information that still needs confirmation. | |
| evidence | No | Optional source or project-relative evidence reference supporting the memory. | |
| consequence | No | Optional impact or consequence of this memory for future work. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description's behavioral claims ('does not delete existing memories', 'performs no network access') largely restate destructiveHint=false and openWorldHint=false from the annotations, and it adds the useful locality framing of 'local durable memory'. It does not disclose what the annotations cannot: whether repeated calls with identical content create duplicates (idempotentHint=false implies they do) or what the write returns. With annotations already carrying the safety profile, this is adequate but adds limited new context.
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?
Three short sentences, front-loaded with purpose, then usage routing, then behavioral constraints. No filler or repetition; every clause carries 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?
For an 8-parameter write tool with full schema coverage, no output schema, and annotations present, the description covers purpose, usage routing, and side-effect profile. The only gap is duplicate/return behavior on a non-idempotent write, which is minor.
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% with per-field descriptions and enum values, so the schema does the heavy lifting. The description's list of knowledge kinds loosely maps to the type enum but adds no format, syntax, or selection guidance beyond it. Baseline 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?
Specific verb+resource (store durable project knowledge that survives across sessions) with the durable/temporary distinction made explicit against the checkpoint sibling. An agent can tell immediately what this tool produces and how it differs from checkpoint without opening either schema.
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?
States exactly when to use it (stable decisions, constraints, gotchas, failures, hypotheses, reusable knowledge) and names the alternative with its own condition (checkpoint for temporary task progress). Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resumeResume Saved TaskA
Resume and remove the top saved task from the task stack. Use after tasks shows resumable work; use tasks when you only need to inspect the stack. This changes local task state, is non-idempotent, does not delete durable memories, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and idempotentHint=false, but the description adds genuinely new context: it removes an entry from a stack (pop semantics), leaves durable memories intact, and performs no network access. The non-destructive clarification is valuable since 'remove' could otherwise read as data loss, though it restates non-idempotency already in 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?
Two sentences, both front-loaded: the operation first, then routing guidance and behavioral guarantees. No filler, and every clause carries information an agent needs.
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 zero-param mutation with no output schema, the description covers the operation, the routing decision, state side effects, and network behavior. The only gap is what 'resume' returns (the resumed task content), which an agent might reasonably want to know, but annotations and description together are otherwise sufficient.
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 takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-related claims are needed or missing.
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?
States a precise verb pair and resource: 'Resume and remove the top saved task from the task stack.' This distinguishes it from sibling 'tasks' (inspect) and from 'checkpoint'/'remember' by naming the exact stack operation performed.
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?
Explicitly names the precondition ('Use after tasks shows resumable work') and the alternative ('use tasks when you only need to inspect the stack'). Both when-to-use and when-not-to-use are covered with the sibling named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchSearch Project MemoryARead-only
Search durable project memories and return ranked results with confidence and abstention metadata. Use for focused fact retrieval without starting a task; use context when beginning or resuming work. It does not alter durable memory content or the task stack; repeated calls may update retrieval or usage metadata, so it is not marked idempotent. It performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
| at | No | Optional YYYY-MM-DD date used to filter memories by temporal validity. | |
| limit | No | Maximum number of ranked memories to return. | |
| query | Yes | Natural-language query describing the fact, decision, constraint, or prior work to retrieve. | |
| types | No | Optional memory types to include; omit to search all supported types. | |
| explain | No | Include per-channel retrieval scoring details for diagnostics. | |
| statuses | No | Optional memory statuses to include; omit to use the server default. |
Output Schema
| Name | Required | Description |
|---|---|---|
| reason | No | Human-readable explanation for the retrieval or abstention decision. |
| results | Yes | Ranked durable memory records returned by the search. |
| admitted | No | Number of candidate memories admitted to the result set. |
| rejected | No | Number of candidate memories rejected by retrieval admission rules. |
| abstained | Yes | True when retrieval intentionally returns no admitted result set. |
| confidence_kind | No | Whether retrieval confidence is heuristic or calibrated. |
| retrieval_confidence | No | Server confidence score for the retrieval result set. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/destructive/openWorld/idempotent hints, but the description goes further by explaining the reason for the non-idempotent hint (repeated calls may update retrieval or usage metadata) and clarifying that durable memory content and the task stack are not altered. The no-network-access statement also adds context beyond the openWorldHint.
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?
Three sentences, each load-bearing: purpose/output first, routing guidance second, behavioral caveats third. No repetition of schema content and no filler.
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 an output schema present, the description need not explain return values, and it still flags that results carry confidence and abstention metadata. Combined with full schema coverage and annotations, an agent has everything needed to call 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 description coverage is 100%, so all six parameters are already documented in the schema. The description adds no syntax, format, or interaction details for query, types, statuses, at, limit, or explain, so the baseline 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?
States a specific verb and resource ('search durable project memories') plus the output character ('ranked results with confidence and abstention metadata'). It explicitly contrasts itself with the sibling 'context' tool, so an agent can route between them without opening either schema.
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?
Gives an explicit when ('focused fact retrieval without starting a task') and a when-not with the named alternative ('use context when beginning or resuming work'). Nothing about tool selection is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statusInspect Memory StatusARead-onlyIdempotent
Return memory, checkpoint, index, and recovery status for the current project. Use for diagnostics and health checks. This operation is read-only and idempotent, does not modify memory or task state, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| memories | No | Total number of durable memories in the current project. |
| checkpoints | No | Total number of checkpoints in the current project. |
| index_status | No | Current retrieval index health or synchronization status. |
| last_checkpoint | No | Most recent checkpoint recorded for the current project. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, and the description largely restates these (read-only, idempotent, no state modification, no network access). It adds no information beyond the structured hints, but it also contradicts nothing, so a 3 is appropriate.
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?
Three short sentences, front-loaded with the core action and scope, followed by usage and safety context. No filler and nothing repeated needlessly.
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 operation is parameterless, an output schema exists to describe the return shape, and annotations cover the safety profile; nothing an agent needs in order to call this correctly is missing.
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 takes zero parameters, so there is nothing for the description to disambiguate; per the rubric, a 0-parameter tool has a 4 baseline. The description correctly avoids inventing inputs.
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?
States a specific verb (Return) and enumerates the exact resources covered (memory, checkpoint, index, recovery status for the current project), which is well beyond a restatement of the name "status". It stops short of explicitly distinguishing itself from the operational siblings like resume or checkpoint, so it lands just under a 5.
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?
"Use for diagnostics and health checks" gives a clear context for invoking it, implicitly steering the agent away from action-oriented siblings such as remember or resume. There are no explicit when-not conditions or named alternatives, which keeps it from a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tasksList Pending TasksARead-onlyIdempotent
List the current task stack and resumable task state. Use before resume when you need to inspect pending work without changing the stack. This operation is read-only and idempotent, does not modify task state, and performs no network access.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| tasks | No | Saved or resumable tasks in the current task stack, in server-defined order. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description's 'read-only and idempotent, does not modify task state' largely restates structured data rather than adding new behavior. 'Performs no network access' is a marginal addition but is already implied by openWorldHint=false.
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?
Front-loads the purpose in the first sentence and stays to three short sentences, though the safety restatement ('read-only and idempotent, does not modify task state') is somewhat redundant with annotations and could be trimmed.
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 an output schema present, the description need not explain return values, and it covers purpose, timing, and the safe-read nature adequately for a zero-parameter read 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 tool takes zero parameters, so the baseline is 4; there is no parameter semantics to document, and the description correctly says nothing about inputs.
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?
States a specific verb+resource ('List the current task stack and resumable task state') that an agent can distinguish from sibling tools like resume or status without opening a schema.
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?
Gives clear timing guidance ('Use before resume when you need to inspect pending work') and implicitly contrasts with resume by noting it leaves the stack unchanged, but does not explicitly name alternatives or when-not-to-use conditions beyond that.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
8 tool updates
v0.1.0- First observed
checkpoint - First observed
context - First observed
guard - First observed
remember - First observed
resume - First observed
search - First observed
status - First observed
tasks
TDQS
Scored across 8 tools
Descriptions carefully cross-reference each other (checkpoint vs remember, context vs search, resume vs tasks) so boundaries are mostly clear. Minor potential overlap between checkpoint/remember and guard/status, but the descriptions resolve these distinctions well.
All names are single lowercase words with no camelCase/snake_case mixing, giving a uniform visual style. However, it mixes verbs (checkpoint, resume, search, guard, remember) with nouns (context, status, tasks), so the convention is not fully predictable.
Eight tools is well-scoped for a lightweight memory server, with each tool mapping to a distinct operation (write memory, write checkpoint, search, inspect, resume, diagnose). No tool feels redundant or bolted on.
Covers the core lifecycle: durable memory (remember/search), task state (checkpoint/context/resume/tasks), and diagnostics (status/guard). The one gap is the absence of an update/delete/forget operation for durable memories, which descriptions explicitly note they never remove, limiting correction of stale knowledge.
Maintenance
Related MCP Connectors
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
Long-term memory for AI coding agents: durable project facts, recalled by every MCP client.
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.
Shared memory for coding agents. Stop re-explaining your codebase every session.
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