Sezzlee Local LLM

Name: Sezzlee Local LLM
Author: sezzlee

by sezzleeversion 0.1.3i★ –iRuns on your computer

Registry nameio.github.sezzlee/llm

Install

Overview

Description from the maker (original English)

Let a planning agent hand bounded language work to a local Ollama model.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: SEZZLEE_LLM_MODEL

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: 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_DIR

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      }
    }
  }
}

Run this in your 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-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

SEZZLEE_LLM_MODEL required env
Ollama model to call
SEZZLEE_LLM_BASE_URL env
Ollama base URL
SEZZLEE_LLM_NUM_CTX env
Context window in tokens
SEZZLEE_LLM_TIMEOUT_MS env
Per-call timeout in milliseconds
SEZZLEE_LLM_KEEP_ALIVE env
How long Ollama keeps the model loaded
SEZZLEE_LLM_ROOT env
Workspace folder paths resolve against
SEZZLEE_LLM_OUTPUT_DIR env
Folder local_map writes its CSV into

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account sezzlee, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5662 servers in this category
Published to the registry more recently than 59% of the other 5662 servers in this category
Basic checks equal to or better than 100% of the other 5662 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Sezzlee Local LLM safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: SEZZLEE_LLM_MODEL

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.3) reached the registry 27 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Codebase Memory

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

@sezzlee/llm-mcp

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.

Quick start

"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.

Tools

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

Configuration

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.

Limits

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.

Rules

Three lint-enforced layers, three root entrypoints. 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.
Only `src/backend/ollama.ts` reaches the network, and only through the global fetch. That global and the node:http/net/tls family are lint-banned everywhere else.
`process.env` is read only in `cli.ts`, each variable accessed by name.
File paths are resolved against the workspace. The requested path and its 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 ..
The server counts tokens, not the model. An input over 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.
Only `src/platform/workspace.ts` touches the filesystem, lint-enforced. The single writer is 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).
`local_map` carries the row number to the model and back. An id-less format lost the row order. A skipped row is asked again once; if it is still missing, it is left unlabeled and counted — never guessed.
A chunked input is not truncated either. Chunks split on paragraph, then line, boundaries; each chunk yields notes, the notes are merged and the instruction applied one last time. If the merged notes still do not fit the budget, the merge round repeats (up to 3); if that still fails, the call is refused as input_too_large.
One request per host at a time. createSerialBackend queues requests: the GPU does not run them in parallel, while Codex fires calls concurrently. probe bypasses the queue.
A new backend kind is just another implementation of the `Backend` interface. The rest of the server does not change.

Design notes

The measurements below are what the rules above are built on.

Why an MCP server instead of pointing Codex at Ollama directly

Four ways of getting Codex to a local model were tried; only the MCP server worked.

Approach	Result

Only the beginning is shown; read the rest in the repo

Sezzlee Local LLM

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

@sezzlee/llm-mcp

Quick start

Tools

Configuration

Limits

Rules

Design notes

Why an MCP server instead of pointing Codex at Ollama directly