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Fable

by codeChapversion 0.1.0i★ –i–
Registry nameio.github.codeChap/fable

Install

Overview

Description from the maker (original English)

Anthropic Claude Fable 5 MCP server — top-tier planning and code critique for handoff

PopNx MCP’s three pillars

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

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: –
  • Transport:
  • Category: Other

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.

The maker has not listed install details in the registry. See the maker’s README below.

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 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account codeChap, 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 Otheri

  • More stars than 0% of the other 32403 servers in this category
  • Published to the registry more recently than 17% of the other 32403 servers in this category
  • Basic checks equal to or better than 100% of the other 32403 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 Fable 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 (0.1.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, LickityClick

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

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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LickityClick

by lickity.click

Create, edit and track LickityClick short links, campaigns and click stats from your AI assistant.

Computer or cloudUpdated today
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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.

mcp-server-fable

An MCP (Model Context Protocol) server for Anthropic Claude Fable 5.1 — Anthropic's most capable, most expensive model ($10 / $50 per 1M input / output tokens, ~2× Opus). Built in Rust, it exposes Fable as specialized MCP tools (plan, critique, ask) so any MCP client can use them.

Fable is not a day-to-day chat model here. This server is built for the one thing that justifies the price: using Fable to plan and critique, then handing the result to a cheaper model (Sonnet / Haiku / Opus) to execute. It works in Claude Code, Claude Desktop, Cursor, custom agents, and any other environment that supports MCP servers over stdio.

Communicates via stdio using JSON-RPC 2.0. Structurally it mirrors mcp-server-claude-chat, but is deliberately adapted to Fable's API surface and purpose-built for the plan-then-execute pattern.

Why mcp-server-fable (vs. Claude Code's built-in advisor)?

Claude Code has a powerful native advisor feature (/advisor fable or the advisor tool). It lets a fast executor model (typically Sonnet or Haiku) dynamically ask Fable for guidance on hard decisions inside a single session.

This MCP server takes a complementary approach that is useful in more situations:

  • Explicit tools with handoff-optimized prompts — plan and critique use fixed, carefully written system prompts designed so the output can be passed *verbatim* to a cheaper model. Plans are numbered, unambiguous, include exact paths/signatures, edge cases, acceptance criteria, and out-of-scope notes. Critiques are deliberately coverage-first (report everything; filter downstream).
  • Works everywhere MCP works — Not limited to Claude Code. Use it in Claude Desktop, Cursor, Windsurf, custom agent frameworks, VS Code MCP extensions, scripts, or any future tool that supports the Model Context Protocol.
  • You control the orchestration — Call Fable for planning or review exactly when *you* (or your multi-agent system) decide, instead of relying on the model to escalate.
  • Clean composability — Treat Fable as a reusable specialist service alongside your other MCP servers. Perfect for modern "expensive model for judgment, cheap model for execution" workflows.
  • Correct Fable-specific behavior — Proper effort levels, refusal handling (no silent fallback), long timeouts, and accurate per-call cost reporting at Fable rates.

Many teams are converging on the same pattern the community discovered: use Fable narrowly for architecture, planning, and review, then execute with cheaper models. This server gives you first-class, portable tools for the "Fable parts" of that pattern.

See the "Technical details" section below for the specific Fable API differences that also required a dedicated implementation.

Subscription vs. API credits

This server uses the Anthropic API with an API key (API credits) — the only supported, terms-compliant way to drive Claude from a third-party tool. A Claude Pro/Max subscription is not usable here. Point base_url at an Anthropic-compatible gateway if you run one.

Technical details: Fable API differences

Fable's Messages API differs from the Opus-era shape (this is why a dedicated server was needed rather than reusing a general Claude chat wrapper):

  • No sampling parameters — temperature / top_p / top_k are rejected with a 400. There is no temperature tool argument.
  • Adaptive thinking only — reasoning is always on; depth is controlled by `effort` (low / medium / high / xhigh / max), not a token budget. The raw chain of thought is never returned; ask can request a readable *summary* via show_reasoning.
  • Refusals stop, cleanly — this is a *dedicated Fable server*. Fable's safety classifiers (cyber / bio / model-distillation) can decline a request; that comes back as a successful response with stop_reason: "refusal", surfaced with its category and explanation rather than as an answer. It is never silently retried on a different model.
  • 30-day data retention required — Fable is not available to zero-data-retention orgs; such orgs get a 400 on every request.

Every response also prints an estimated USD cost (at Fable's sticker rates), since cost-consciousness is the whole point.

Tools

ToolDescription
planFlagship. Turn a goal (+ optional context) into an executor-ready implementation plan: numbered steps, exact paths/signatures, edge cases, acceptance criteria, out-of-scope notes — written to be handed to a cheaper model and executed verbatim.
critiqueCoverage-first review of code, a diff, or a design. Reports every finding with severity + confidence for downstream filtering. Optional focus.
askRaw one-shot query to Fable. Multi-turn history, system prompt, effort, optional reasoning summary.
plan
NameTypeRequiredDescription
goalstringyesWhat to build or fix
contextstringnoRelevant code, file tree, constraints, error output, prior attempts
effortstringnolow/medium/high/xhigh/max (default high)
max_tokensintegernoMax tokens to generate (server default otherwise)
critique
NameTypeRequiredDescription
contentstringyesCode, diff, or design to review
focusstringnoArea to weight, e.g. security, concurrency
effortstringnolow/medium/high/xhigh/max (default high)
max_tokensintegernoMax tokens to generate
ask
NameTypeRequiredDescription
promptstringyesThe user message
system_promptstringnoSystem prompt
messagesstringnoHistory as a JSON array of {role, content} (roles user/assistant only)
effortstringnolow/medium/high/xhigh/max (default medium)
max_tokensintegernoMax tokens to generate
show_reasoningbooleannoReturn a summary of Fable's reasoning in a [thinking] block

Prerequisites

  • Rust (edition 2024)
  • An Anthropic API key from console.anthropic.com, on an org with ≥30-day data retention

The server expects a config file at ~/.config/mcp-server-fable/config.toml containing at minimum your api_key. See config.toml.example.

api_key = "sk-ant-..."

Optional overrides:

base_url = "https://api.anthropic.com/v1"

default_model = "claude-fable-5-1"

default_max_tokens = 8192

default_effort = "high" # low | medium | high | xhigh | max

The server fails fast at startup if the config is missing, api_key is empty, or default_effort (if set) is invalid.

Build

cargo build --release   # produces target/release/fable

cargo build # debug build cargo run # run in dev mode RUST_LOG=debug cargo run cargo test # unit tests (response formatting, cost, refusal, effort, message building)

Installation & MCP Configuration

1. Build the server
cargo build --release

The binary will be at: target/release/fable

Use the full absolute path to target/release/fable in all configuration below.

2. AI-assisted installation (recommended modern method)

Copy the block below and paste it directly to your AI coding assistant (Claude Code, Cursor, Grok, etc.). The AI will handle cloning (if needed), building, path resolution, and registration for you.

Add the mcp-server-fable MCP server for me.

Repository: https://github.com/ /mcp-server-fable (update this URL if you have a fork)

Steps to perform:

  1. If the repo isn't cloned locally yet, clone it and cd into it.
  2. Build the release binary: cargo build --release
  3. Determine the absolute path to the built binary (target/release/fable).
  4. Set up the config directory and file: mkdir -p ~/.config/mcp-server-fable cp config.toml.example ~/.config/mcp-server-fable/config.toml Then edit the config and add your Anthropic API key (api_key = "sk-ant-...").
  1. Register it as an MCP server named "fable".

For Claude Code, run: claude mcp add fable -- /target/release/fable

For Claude Desktop or other MCP clients, add this under the "mcpServers" key (use the real absolute path): { "fable": { "command": " /target/release/fable" } }

After setup, test that the plan tool is available and working.

3. Manual configuration

Claude Desktop or any MCP client (~/.config/Claude/claude_desktop_config.json or equivalent):

{

"mcpServers": { "fable": { "command": "/absolute/path/to/mcp-server-fable/target/release/fable" } } }

Claude Code (one-liner):

claude mcp add fable -- /absolute/path/to/mcp-server-fable/target/release/fable

Replace the path with your actual absolute path to the release binary.

Usage

Once it's registered, an MCP client calls the tools by name — the flagship is plan.

From an MCP client (e.g. Claude Code)

Ask the model to use it, handing over the goal plus whatever context the executor will need:

Use the fable plan tool. goal: "Add a --json flag to the CLI that prints results as JSON". context: "Rust clap app; output currently goes through println! in src/main.rs". effort: high

Claude Code issues a tools/call for plan; Fable returns a numbered, executor-ready plan — exact paths, signatures, edge cases, acceptance criteria — which you then hand to a cheaper model (Sonnet / Haiku) to implement verbatim.

Raw JSON-RPC over stdio

The same call without a client — a tools/call request the server reads on stdin:

{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{

"name":"plan", "arguments":{ "goal":"Add a --json flag to the CLI that prints results as JSON", "context":"Rust clap app; output currently via println! in src/main.rs", "effort":"high" }}}

plan and critique return their content followed by a token + estimated-cost footer. Here is an actual response (from a tiny ask probe) showing that footer:

BINARY-OK

[stop_reason: end_turn] [tokens: 21 input + 9 output = 30 total] [cost: ≈ $0.0007 (fable rates)]

Because the server only ever calls Fable, that cost line is always at Fable's $10 / $50 per-1M rates — accurate by construction, not by convention.

Project Structure

src/

main.rs - entry point, config loading, stdio transport setup server.rs - MCP tool definitions (plan, critique, ask) + fixed prompts api.rs - Anthropic HTTP client, Effort enum, Messages types, refusal/cost formatter params.rs - tool parameter types with serde + JSON Schema derives config.rs - TOML config loading + effort validation

License

MIT

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