Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Descripción del creador (original en inglés)
Private-by-default, local-first memory/context/task orchestrator for MCP apps and agents.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Funciona en Claude para web, escritorio y móvil:
https://contextlattice.io/mcpEjecuta esto en tu terminal
claude mcp add --transport http contextlattice https://contextlattice.io/mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"contextlattice": {
"url": "https://contextlattice.io/mcp"
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"contextlattice": {
"type": "http",
"url": "https://contextlattice.io/mcp"
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
ChatGPT solo se conecta a servidores en internet:
https://contextlattice.io/mcpLos nombres de los menús pueden variar un poco según el plan y con el tiempo.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Todavía no hemos leído la lista de herramientas de este servidor; revisamos los endpoints por turnos.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub sheawinkler, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; leemos la lista de herramientas cuando revisamos el endpoint
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en la nube del creador. Te conectas por internet sin instalar nada, pero lo que envías pasa por los servidores del creador.
El repo de GitHub cambió por última vez –, y la última versión (1.0.0) llegó al registro hace 9 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
En la misma categoría: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
Open an agent. Already there.
The local-first intelligence layer that gives AI agents durable continuity, explainable retrieval, portable context, and verified learning across harnesses.
Quickstart · How it works · Agent setup · Architecture · Docs · Updates
Models can reason. Harnesses can act. Neither reliably retains the mission when a chat, model, tool, account, or computer changes.
ContextLattice gives that work a durable, inspectable context layer. It reconstructs the active objective, selects the evidence that matters, carries it safely, and records what actually worked—without turning every prompt into a transcript dump or making cloud storage mandatory.
| Capability | What changes |
|---|---|
| Durable continuity | Reopen the objective, decisions, repository state, risks, proof, and next move as one bounded packet. |
| Explainable retrieval | Rank evidence by impact per token and expose source coverage, omissions, opposition, degradation, and receipts. |
| Portable context | Move signed, least-privilege continuation across agents and machines while keeping execution and transport caller-owned. |
| Verified skill evolution | Discover skills without loading every file, evaluate repeated wins on holdouts, and require review before promotion. |
| Privacy-bounded Aggregate Signal | Learn from explicitly opted-in, clipped statistics while raw memory remains local; production activation stays hard-blocked pending independent privacy and utility review. |
The CLI is the primary interface. The dashboard makes behavior and proof visible. HTTP and MCP are companion integration surfaces for applications and harnesses.
| Stage | ContextLattice does |
|---|---|
| 01 · Reopen | Reconstructs the one active mission from durable checkpoints and current state. |
| 02 · Select | Retrieves high-signal evidence into a compact Context Pack with provenance. |
| 03 · Move | Carries signed, bounded context through Agent Packets, Passports, and encrypted continuation envelopes. |
| 04 · Earn | Records outcomes and promotes reusable behavior only after deterministic proof and human approval. |
| 05 · Compound | Improves future retrieval while preserving corrections, contradictions, freshness, and retirement semantics. |
ContextLattice does not replace your agent harness, choose goals from retrieved text, or execute imported context. Local tools remain execution surfaces; memory and remote content remain evidence.
Requirements: macOS, Linux, or Windows through WSL2; a Compose v2-compatible container runtime; and gmake, jq, rg, python3, and curl. The tested macOS baseline uses OrbStack through its explicit Docker context; see the container runtime decision.
git clone https://github.com/sheawinkler/ContextLattice.git
cd ContextLattice cp .env.example .env gmake quickstart
gmake quickstart is the prescribed technical install path; installers are bootstrap alternatives. The command prepares environment wiring, asks for a runtime profile, launches the selected local stack, and validates initial readiness.
curl -fsS http://127.0.0.1:8075/health | jq
contextlattice doctor --pretty contextlattice state status --pretty contextlattice context "verify this ContextLattice installation" \ --project contextlattice \ --pretty
Healthy containers are only the first check. The state command verifies the canonical gateway-owned storage inventory; the final command exercises the actual context path and reports source coverage, degradation, evidence, and next actions. Existing installs can use the explicit, reversible procedure in gateway state migration.
For a fuller lifecycle proof from the repository:
scripts/agent/agent-runtime-proof-pack --pretty
scripts/agent/agent-adoption-proof-matrix \ --skip-provider-smoke \ --progress \ --pretty
Run integration from the repository that should use ContextLattice:
cd /path/to/your/project
contextlattice_adopt integrate \ --repo . \ --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid \ --pretty
contextlattice_adopt integrate \ --repo . \ --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid \ --check \ --pretty
The integration command writes bounded managed blocks while preserving existing instruction text. It does not install Codex, Claude Code, OpenCode, Hermes, OMP, Mercury, Pi, Droid, or another third-party agent harness.
External provider discovery is network-free. Provider execution remains explicit and caller-authorized; see the external-provider boundary.
If an agent is performing the installation, it should follow the quickstart directly, avoid cloning a second checkout when already inside one, report the exact failing command and path, and rerun the deterministic check after any repair.
# Confirm readiness when the environment is uncertain.
contextlattice doctor --pretty
contextlattice context "debug the current release regression" \ --project contextlattice \ --pretty
contextlattice remember \ "Root cause verified; regression test added; focused checks pass." \ --project contextlattice \ --pretty
contextlattice resume --project contextlattice --pretty
contextlattice correct \ "The prior deployment record is stale." \ --category stale \ --project contextlattice \ --pretty
contextlattice finish \ "Regression fixed and verified." \ --success \ --project contextlattice \ --pretty
contextlattice_continuous_cognition status "prepare the next verified move" \ --project contextlattice --session-id --agent-id codex_gpt5 \ --task-id --objective-id --as-of --pretty contextlattice_continuous_cognition evaluate "verify the completed response" \ --project contextlattice --session-id --agent-id codex_gpt5 \ --task-id --task-identity-id --as-of --pretty
contextlattice agent-fit context-prep-schedule --project contextlattice \ --session-id --agent-id codex_gpt5 --payload-file prep-request.json --raw contextlattice agent-fit context-prep-claim --project contextlattice \ --session-id --agent-id codex_gpt5 --prep-id \ --worker-id --claim-token-file prep.claim --raw contextlattice agent-fit context-prep-complete --project contextlattice \ --session-id --agent-id codex_gpt5 --prep-id \ --claim-token-file prep.claim --payload-file prep-artifact.json --raw contextlattice agent-fit context-prep-use --project contextlattice \ --session-id --agent-id codex_gpt5 --prep-id \ --task-id --effective-profile-digest \ --source-generation --raw
Continuous Cognition is advisory-only: each invocation makes one bounded request, returns opaque evidence references, and never dispatches a runner or performs an external mutation. Context-preparation claims stay in an owner-only file and cross the completion/failure boundary only through the protected header; successful explicit use consumes the artifact once.
Find a capability without loading every skill body:
contextlattice_skills_index search "browser automation" --pretty
The active Skills Index scans configured Codex, Hermes, Hermes Ultra, and shared agent roots read-only. It reports each harness and root inventory separately, collapses byte-identical SKILL.md files by SHA-256 digest while retaining every source path as provenance, and requires discriminating query-term coverage instead of ranking generic words such as skill, index, or agent. Quarantine discovery remains separate, read-only by default, and never auto-promotes retrieved content.
The default local control path is:
Agent or application
│ ▼ ContextLattice CLI / HTTP / MCP │ ▼ Gateway :8075 ├── durable write and outbox fanout ├── scoped retrieval and source receipts ├── session, objective, graph, and outcome state └── dashboard-visible proof and operations
Writes are validated and durably persisted before fanout. Retrieval merges the available sources, ranks bounded evidence, and reports missing or degraded coverage instead of hiding it.
The active application path is Go and Rust. Python remains in build, development, migration, and audit tooling rather than the live request path. The exact runtime and toolset choices are recorded in the v4 runtime decision and container decision.
The public local lane is account-free and useful on its own. It includes the CLI-first memory lifecycle, Context Packs, sessions, graph and claim surfaces, Skills Index discovery, Agent Packets, public Passport and Mesh contracts, and local proof tooling.
Paid artifacts add governed collaboration, protected activation, workspace operations, advanced analytics, and hosted distribution. They do not turn local memory into a mandatory cloud dependency.
See plans and distribution boundaries for the current contract.
macOS technical preview: unsigned DMG bootstrap launcher; expect Gatekeeper warnings until Developer ID notarization is configured, and prefer the source/CLI path.
| Path |
Solo se muestra el principio; lee el resto en el repo