mcpField
A
ask-fable

ask-fable

Local
github.com/baggybinAI & Agents
Repository

About ask-fable

MCP server for Anthropic's Claude Fable, Opus 5, and multi-model reasoning councils

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "baggybin-ask-fable": {
      "command": "uvx",
      "args": [
        "ask-fable"
      ]
    }
  }
}
Run commanduvx ask-fable

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiask-fable0.13.0

Tools

38

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
resetbooleanNoDump+clear this session before asking, starting a fresh conversation.
contextstringNoOptional code snippets, file paths, or structural context.
sessionstringNoConversation key. Reuse it to ask follow-ups (Fable keeps context); use a new key or reset=true to start a fresh topic.
trustedbooleanNoOperator-authorized. When true, the prohibited-use denylist runs in log-only mode: security vocabulary in the question is audited but does not block. Use for legitimate security-engineering work (PoC analysis, CVE research, binary hardening review) where the question genuinely needs security terms. The operator is responsible for authorizing this flag.
questionstringYesA specific question about concrete software code/architecture (structure, functionality, data flow, module/function relationships, routing).
context_refanyNoKey(s) of context previously saved with `context_write` to pull in and prepend to `context` — so you paste a big codebase context ONCE and reference it by key across many asks instead of re-pasting. Missing keys are reported, not fatal.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.