Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Let a planning agent hand bounded language work to a local Ollama model.
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.
SEZZLEE_LLM_MODEL, SEZZLEE_LLM_BASE_URL, SEZZLEE_LLM_NUM_CTX, SEZZLEE_LLM_TIMEOUT_MS, SEZZLEE_LLM_KEEP_ALIVE, SEZZLEE_LLM_ROOT, SEZZLEE_LLM_OUTPUT_DIRNecesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"llm": {
"command": "npx",
"args": [
"-y",
"@sezzlee/llm-mcp"
],
"env": {
"SEZZLEE_LLM_MODEL": "your-value",
"SEZZLEE_LLM_BASE_URL": "your-value",
"SEZZLEE_LLM_NUM_CTX": "your-value",
"SEZZLEE_LLM_TIMEOUT_MS": "your-value",
"SEZZLEE_LLM_KEEP_ALIVE": "your-value",
"SEZZLEE_LLM_ROOT": "your-value",
"SEZZLEE_LLM_OUTPUT_DIR": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add llm --env SEZZLEE_LLM_MODEL=your-value --env SEZZLEE_LLM_BASE_URL=your-value --env SEZZLEE_LLM_NUM_CTX=your-value --env SEZZLEE_LLM_TIMEOUT_MS=your-value --env SEZZLEE_LLM_KEEP_ALIVE=your-value --env SEZZLEE_LLM_ROOT=your-value --env SEZZLEE_LLM_OUTPUT_DIR=your-value -- npx -y @sezzlee/llm-mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"llm": {
"command": "npx",
"args": [
"-y",
"@sezzlee/llm-mcp"
],
"env": {
"SEZZLEE_LLM_MODEL": "your-value",
"SEZZLEE_LLM_BASE_URL": "your-value",
"SEZZLEE_LLM_NUM_CTX": "your-value",
"SEZZLEE_LLM_TIMEOUT_MS": "your-value",
"SEZZLEE_LLM_KEEP_ALIVE": "your-value",
"SEZZLEE_LLM_ROOT": "your-value",
"SEZZLEE_LLM_OUTPUT_DIR": "your-value"
}
}
}
}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": {
"llm": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@sezzlee/llm-mcp"
],
"env": {
"SEZZLEE_LLM_MODEL": "your-value",
"SEZZLEE_LLM_BASE_URL": "your-value",
"SEZZLEE_LLM_NUM_CTX": "your-value",
"SEZZLEE_LLM_TIMEOUT_MS": "your-value",
"SEZZLEE_LLM_KEEP_ALIVE": "your-value",
"SEZZLEE_LLM_ROOT": "your-value",
"SEZZLEE_LLM_OUTPUT_DIR": "your-value"
}
}
}
}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.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
SEZZLEE_LLM_BASE_URL envSEZZLEE_LLM_NUM_CTX envSEZZLEE_LLM_TIMEOUT_MS envSEZZLEE_LLM_KEEP_ALIVE envSEZZLEE_LLM_ROOT envSEZZLEE_LLM_OUTPUT_DIR envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
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 sezzlee, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
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.
Debes completar: SEZZLEE_LLM_MODEL
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.1.3) llegó al registro hace 26 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
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Connect AI agents to Exa for web search, content fetching, and multi-step research.
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.
MCP server that lets a planning agent (Codex, Claude Code, Cursor) hand off bounded language work to a local model. It builds on @sezzlee/mcp-core and names no other @sezzlee/* package.
{
"mcpServers": { "local": { "command": "npx", "args": ["-y", "@sezzlee/llm-mcp"], "env": { "SEZZLEE_LLM_BASE_URL": "http://127.0.0.1:11434", "SEZZLEE_LLM_MODEL": "qwen3:8b" } } } }
In Codex, the same block goes under mcp_servers.local. If it runs with approval_policy = "never", default_tools_approval_mode = "auto" is also required, or Codex refuses the call.
Three tools. Two are read-only; local_map only adds new files to the server's own output directory.
| Tool | What it does | Key arguments |
|---|---|---|
local_task | Runs one bounded language task on the local model: classify, extract, summarize, transform, or free. The server reads any file arguments itself — their contents never enter the calling agent's context. With jsonSchema the answer is JSON matching that schema (returned as result); otherwise it is text (returned as answer). summarize and extract split an oversized input into chunks and read all of it (up to 32 chunks, 1 MiB total); every other kind refuses an oversized input as input_too_large. Nothing is ever silently truncated. | kind, instruction, text?, files? (up to 8, paths relative to the working directory), jsonSchema? |
local_map | Labels every row of a CSV file with one of the given labels. The agent never sees the rows: it passes a path, and the server writes a new file (the original columns plus one label column) under the output directory, returning that file's path, the per-label counts, and up to 4 sample rows per label. Up to 2,000 rows. | file, instruction, labels (2–50), labelColumn? (defaults to label) |
local_status | Reports whether the local model is reachable and loaded, its context window, one call's input budget, and how many calls are queued. Call it when unsure the local model is available before delegating to it. | none |
SEZZLEE_LLM_MODEL=qwen3:8b
SEZZLEE_LLM_ROOT=. # default: the working directory SEZZLEE_LLM_OUTPUT_DIR=.llm-mcp/out # default; relative to root, must stay inside it SEZZLEE_LLM_BASE_URL=http://127.0.0.1:11434 # default SEZZLEE_LLM_NUM_CTX=16384 # default; at least 4096 SEZZLEE_LLM_KEEP_ALIVE=30m # default SEZZLEE_LLM_TIMEOUT_MS=300000 # default; one request
npx -y @sezzlee/llm-mcp
| Variable | Default | Meaning |
|---|---|---|
SEZZLEE_LLM_MODEL | none — required | The Ollama model name. There is no built-in default; it must be set explicitly (see Model choice). |
SEZZLEE_LLM_ROOT | working directory | Workspace root. Every file argument a tool accepts is resolved against it. |
SEZZLEE_LLM_OUTPUT_DIR | .llm-mcp/out | Where local_map writes its output. Relative to the root, and must stay inside it. |
SEZZLEE_LLM_BASE_URL | http://127.0.0.1:11434 | Base URL of the Ollama host. |
SEZZLEE_LLM_NUM_CTX | 16384 | Context window in tokens. Must be an integer of at least 4096. |
SEZZLEE_LLM_KEEP_ALIVE | 30m | Value forwarded to Ollama's keep_alive. |
SEZZLEE_LLM_TIMEOUT_MS | 300000 | Timeout for a single request to the model. |
Only the model name is required. Parsing is fatal, reaching the host is not: the server still starts and answers tools/list with the host down, and local_status reports reachable: false. The model is warmed up right after the server starts serving.
Set SEZZLEE_LLM_NUM_CTX to what the GPU actually delivers. Ollama does not refuse an oversized input — it silently drops the head — and the input budget is derived from this value.
| Limit | Value | Why |
|---|---|---|
| Input budget | 45% of the context window | Ollama silently drops the head of an oversized prompt instead of refusing it, so the server refuses first. |
| Output budget | 40% of the context window | Caps a runaway completion; without an id-bearing schema the model was measured looping into 12,600 tokens of garbage. |
| Chars-per-token estimate | ~1.8 | Measured on Turkish text; English runs closer to 4 characters per token, so the estimate errs toward refusing rather than truncating. |
local_task files | up to 8 | Per-call file argument cap. |
local_task long input (summarize/extract) | up to 32 chunks, 1 MiB total | Other kinds refuse an over-budget input outright as input_too_large. |
| Chunk merge rounds | up to 3 | A merge that still does not fit after 3 rounds fails as input_too_large. |
local_map rows | up to 2,000 | Labelling was measured at ~270 ms per row — about nine minutes for 2,000 rows, inside the 900 s tool timeout Codex allows. |
local_map file size | up to 8 MiB | Refused up front rather than timed out halfway with nothing written. |
src splits into platform/ (the mcp-core and Node boundary), backend/ (the model host), and tools/ (the MCP surface). tools/ sees the host only through backend/port.ts; the concrete backend is wired in cli.ts.fetch. That global and the node:http/net/tls family are lint-banned everywhere else.realpath are checked separately, so a symlink pointing outside the workspace is refused too. readText accepts only a WorkspacePath-branded path that has passed both checks. In error envelopes, the workspace's absolute path appears as ..num_ctx × 0.45 never reaches the host; an input that could not fit under any circumstances is refused before it is even read. The answer is capped at num_ctx × 0.4.createOutput: it re-checks the output directory's realpath, the server itself picks the file name, lowers it to a safe alphabet and fixes the extension to .csv, and opens it wx — so it never overwrites an existing file or a symlink, and nothing is ever deleted (mcp-core).input_too_large.createSerialBackend queues requests: the GPU does not run them in parallel, while Codex fires calls concurrently. probe bypasses the queue.The measurements below are what the rules above are built on.
Four ways of getting Codex to a local model were tried; only the MCP server worked.
| Approach | Result |
|---|
Solo se muestra el principio; lee el resto en el repo