Clef MCP

Name: Clef MCP
Author: HighlyLoadedEgo

by HighlyLoadedEgoversion 0.4.0i★ –iRuns on your computer

Registry nameio.github.HighlyLoadedEgo/clef-mcp

Install

Overview

Description from the maker (original English)

Local MCP server for the Cloudflare Clef-Flash decision model: structured decisions, fully offline

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: Developer tools

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).
The registry lists no key to fill in.

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": {
    "clef": {
      "command": "npx",
      "args": [
        "-y",
        "clef-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add clef -- npx -y clef-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "clef": {
      "command": "npx",
      "args": [
        "-y",
        "clef-mcp"
      ]
    }
  }
}

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": {
    "clef": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "clef-mcp"
      ]
    }
  }
}

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.

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 HighlyLoadedEgo, 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 Developer toolsi

More stars than 0% of the other 1396 servers in this category
Published to the registry more recently than 90% of the other 1396 servers in this category
Basic checks equal to or better than 100% of the other 1396 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 Clef MCP 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 (0.4.0) reached the registry 4 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: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

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

GitHub

by github

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

Computer or cloud★ 34,245MITUpdated 4 days ago

Install

Desktop Commander

by wonderwhy-er

MCP server for terminal commands, file operations, and process management

Runs on your computer★ 10,225MITUpdated 16 days ago

Install

Cloudflare MCP

by cloudflare

Cloudflare MCP servers

Runs in the cloud★ 4,446Apache-2.0Updated 14 months ago

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.

clef-mcp

Local decisions for AI agents. Reusable classification for your datasets.

Run Clef-Flash (9B, Apache-2.0 by Cloudflare) locally. Give coding agents a structured MCP decision tool, or classify JSONL/CSV datasets with YAML profiles. Get probabilities and explicit review results instead of generated prose. Inference stays on your machine.

Quick start

Requires Node.js 20+ and enough memory for the selected model (16 GB recommended for Q4_K_M). The default model download is approximately 6 GB; subsequent inference runs offline.

npm install -g clef-mcp

clef-mcp install

Choose your workflow:

Workflow	Start here
Decisions inside Claude Code, Codex, Cursor or ZCode	`clef-mcp setup`, then restart your client; client configuration
Classify tickets, documents or other records	YAML profiles and dataset classification
Hooks, CI or shell scripts	`clef-mcp decide`

The MCP server starts with clef-mcp (stdio). Only install downloads model/runtime files. You can also use npx -y clef-mcp instead of a global install; npx -y clef-mcp install --setup combines model installation and client registration.

Classify JSONL/CSV with a YAML profile

Define a task once, then apply it to separate records. The model loads once for the whole batch; a separate daemon is not required.

Save this as support-routing.yaml:

schema_version: 1

name: support-routing instructions: Classify the current support request into exactly one department. labels: billing: Payments, refunds, invoices and subscription charges technical: Errors, outages, installation and integration problems other: Requests outside billing and technical support input: id_field: id fields: [subject, body] policy: min_probability: 0.80 min_margin: 0.15

Save tickets.jsonl (one object per line):

{"id":"001","subject":"Duplicate charge","body":"Please refund my duplicate subscription payment."}

{"id":"002","subject":"Checkout crashes","body":"The payment page returns an error."}

Or use tickets.csv with a header row:

id,subject,body

001,Duplicate charge,Please refund my duplicate subscription payment. 002,Checkout crashes,The payment page returns an error.

clef-mcp classify tickets.jsonl --profile support-routing.yaml --output results.jsonl

clef-mcp classify tickets.csv --profile support-routing.yaml --output csv-results.jsonl

stdout carries JSONL; summaries and diagnostics go to stderr

cat tickets.jsonl | clef-mcp classify - --profile support-routing.yaml

Extension override, including CSV on stdin

cat tickets.csv | clef-mcp classify - --format csv --profile support-routing.yaml

Runnable files also ship in examples/classify/.

Profile fields

Field	Meaning
`schema_version`	Required; currently `1`
`name`	Required profile ID, up to 64 letters/digits/underscores/hyphens, starting with a letter or digit
`instructions`	Required classification question
`labels`	Required map of 2–100 mutually exclusive label IDs to descriptions; include a fallback category where appropriate
`model`	Optional model ID; priority is CLI `--model` → profile → `CLEF_MODEL` → `clef-flash`
`input.fields`	Optional exact top-level field names to send to the model; otherwise use the full record
`input.id_field`	Optional string/numeric ID to copy to the result; missing or invalid IDs become row errors
`policy.min_probability`	Minimum winning probability, default `0.80`
`policy.min_margin`	Minimum difference between the two highest probabilities, default `0.15`
`policy.min_confidence`	Optional minimum runtime-reported confidence; missing confidence then requires review
`examples`	Optional list of up to 20 `{input: {...}, label: configured_label}` references

Examples are data supplied alongside the current record; they do not train or modify model weights. Unknown fields, duplicate YAML keys, aliases, merge keys, unsupported tags and non-JSON values are rejected. Label IDs use the same character rules as profile names, with a 128-character limit; prototype-shaped reserved keys are rejected. Missing selected fields produce row errors.

Accepted results and `needs_review`

Illustrative results (actual probabilities depend on your data and runtime):

{"row":1,"id":"001","profile":"support-routing","model":"clef-flash","status":"classified","label":"billing","suggested_label":"billing","probability":0.92,"margin":0.87,"probabilities":{"billing":0.92,"technical":0.05,"other":0.03},"review_reasons":[]}

{"row":2,"id":"002","profile":"support-routing","model":"clef-flash","status":"needs_review","label":null,"suggested_label":"technical","probability":0.48,"margin":0.06,"probabilities":{"billing":0.42,"technical":0.48,"other":0.10},"review_reasons":["low_probability","low_margin"]}

classified means all configured thresholds passed. needs_review keeps the suggestion and full distribution but sets label: null. Reasons are low_probability, low_margin, tie, low_confidence or missing_confidence. Exact ties always require review, including when thresholds are zero. Comparisons are inclusive at the threshold. Results may include model confidence and token usage when available.

Winning probability, probability margin and model-reported confidence are different quantities. These thresholds are routing policy, not a calibrated accuracy guarantee. Check them on representative labeled data before automating decisions. Classification does not grant authorization or execute actions.

Invalid JSONL objects and missing fields produce status: "error" rows and processing continues. Runtime failures stop the run; they are never converted to review decisions. Output includes identifiers and results, not a copy of the original record.

Resume, formats and limits

clef-mcp classify tickets.jsonl --profile support-routing.yaml --output results.jsonl --resume

File output creates private results.jsonl and results.jsonl.meta.json files. The sidecar fingerprints the full input, validated profile, effective model, runtime and format. Resume requires all of these to match. Use a new output path after changing data or thresholds; existing files are never silently overwritten. Input must stay unchanged while a run is active.

Resume skips the contiguous committed prefix, including committed error rows; it does not retry those errors. An incomplete final write is discarded, while corruption in a complete row is rejected. Resume requires regular file input and --output; stdin/stdout resume is unsupported. A local lock prevents simultaneous writers. Dead local writer locks can be reclaimed; active, foreign-host or unverifiable locks are preserved. A crash during lock acquisition may leave .lock.claim, which requires inspection before manual removal.

JSONL accepts objects, ignores blank lines and supports UTF-8 BOM/CRLF. CSV supports quoted commas, escaped quotes and multiline fields; headers must be nonempty and unique, with consistent column counts. CSV cells remain strings, preserving IDs such as 001. Format defaults to .jsonl/.csv, or JSONL for stdin; other extensions need --format.

Profiles and logical records are limited to 1 MiB, YAML nesting to 64 levels, selected/CSV fields to 1000, and examples to 20. Existing instruction/criteria/state limits and the model's 16k-token context also apply; selected fields and examples share that context. Oversized JSONL rows are reported as row errors. Broken/oversized CSV syntax stops the run because record boundaries cannot be recovered reliably.

Exit code	Meaning
`0`	Completed; review results are allowed
`1`	Row errors occurred in this invocation, or inference/output failed
`2`	Invalid options/profile/input structure or incompatible checkpoint
`3` / `4`	Model not installed / runtime missing
`130`	Interrupted; committed file output can be resumed

Update

npm install -g clef-mcp@latest

Restart MCP clients and any existing daemon after updating. An already installed model does not need another download.

Why not just ask the LLM?

	Chat LLM	`clef_decide`
Output	prose, you parse it	strict JSON: probability per option
Determinism	varies per run	single forward pass, no sampling
Latency (1 decision)	seconds of generation	~0.5 s local
Context cost	grows with every decision	fixed, small schema
Privacy	depends on provider	100% on-device, works offline
Calibration	vibes	softmax over trained option scores

Sweet spot: decision points inside agent loops — next action, routing, classification, severity, yes/no judgment — asked dozens of times per task.

The `clef_decide` tool

"state": { "task": "Fix failing tests", "error": "TypeError: Cannot read properties of undefined" }, "questions": { "next_action": { "type": "choice", "instructions": "What should the coding agent do next?", "criteria": { "inspect": "Inspect the code and gather more information", "modify": "Modify the code", "test": "Run additional tests", "ask_user": "Ask the user for clarification" } }, "confidence": { "type": "score", "instructions": "How confident are you in this decision?", "criteria": ["very_low", "low", "medium", "high", "very_high"] }, "is_outage": { "type": "noul", "instructions": "Is a service down?" } } }

Type	Criteria	Answer
`choice`	map `option id → description`, or a plain list	probability per option
`score`	ordered list (index = score)	probability per level
`noul`	optional `{"true": "...", "false": "..."}`	`{"true": p, "false": 1-p}`

Response — strictly structured, never prose. Each decision carries the model-reported confidence (when the runtime sends it), plus token usage for the call:

"model": "clef-flash", "decisions": { "next_action": { "answer": { "inspect": 0.72, "modify": 0.12, "test": 0.14, "ask_user": 0.02 }, "confidence": 0.83 }, "confidence": { "answer": { "very_low": 0.01, "low": 0.04, "medium": 0.18, "high": 0.61, "very_high": 0.16 }, "confidence": 0.61 }, "is_outage": { "answer": { "true": 0.9, "false": 0.1 } } }, "usage": { "input_tokens": 228, "output_tokens": 0, "latency_ms": 512 } }

Act on the argmax only when the distribution is decisive — top p ≥ 0.8 and high confidence for destructive or security-adjacent calls.

Batch up to 64 questions per call — they are scored in one forward pass. state is treated strictly as data: never executed, never interpreted as instructions for the server.

Prompts & resources

The server ships four MCP prompts (canned, decision-shaped asks — your client lists them via prompts/list):

Prompt	Purpose

| incident-triage | action + severity + user-impa

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

Clef MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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 Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

clef-mcp

Quick start

Classify JSONL/CSV with a YAML profile

stdout carries JSONL; summaries and diagnostics go to stderr

Extension override, including CSV on stdin

Profile fields

Accepted results and needs_review

Resume, formats and limits

Update

Why not just ask the LLM?

The clef_decide tool

Prompts & resources

Accepted results and `needs_review`

The `clef_decide` tool