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Add Mcp

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lploc94
add-mcp

Use when connecting an MCP server for Raw, explaining its binding, or diagnosing tools, including dashboard discovery. Select exact original names and verify live calls. Use create-package to share definitions.

Overview

Publisherlploc94
Repositoryraw-cli
Skill nameadd-mcp
Stars
107
Forks
14
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lploc94 on GitHub. Read the source before you install it.

Installation

Install the Add Mcp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lploc94/raw-cli/tree/main/examples/skills/add_mcp
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lploc94/raw-cli.git /tmp/raw-cli
mkdir -p .claude/skills
cp -r /tmp/raw-cli/examples/skills/add_mcp .claude/skills/add-mcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Mcp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add Mcp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add Mcp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add an MCP server to Raw

Use when connecting a new MCP capability to a Raw agent or diagnosing that connection. For a how-to, explain connection schema, exact selection and verification. For an actual setup, use the requested server; creating the optional echo fixture below is not a prerequisite or a replacement for that server.

Read references/packages.md for package bindings and references/dashboard.md for browser discovery with read_file.

Establish the connection

Identify the target config/agent, server command or endpoint, authentication requirements and needed tools. Use the server's actual documentation or tools/list to get original tool names. Ask for a missing endpoint/command or required credential source rather than inventing them. For an existing server, inspect its error and definition before replacing it.

Top-level mcp.servers is a connection catalog. An agent activates only exact entries in its ordered tools.use, e.g. mcp/search/web_search. A server definition alone stays inert. Do not select mcp/server/* or substitute Raw's generated model-facing alias for the original tool name.

Connection contract

TransportRequiredOptional
stdiotransport:"stdio", nonempty string commandargs: string array; env: string-to-string map; timeout_ms
remotetransport:"streamable-http", HTTP(S) string urlheaders: string-to-string map; timeout_ms

timeout_ms (integer 1..2147483647, default the agent's request_timeout_ms) bounds connect, tool discovery and each tool call. A call whose server sends progress notifications restarts the timer on each one, so raise it only for tools that work silently for long.

Those are the two configuration transports; unknown fields fail. Stdio starts in the session cwd. Make command/script paths valid for the target machine; a relative script argument is not automatically config-relative. Stdout must carry MCP protocol messages, with logs on stderr. Unselected catalog entries do not start.

env and headers are literal values: Raw does not expand ${VAR}. A stdio server can read inherited environment variables through its own authentication mechanism. For a remote endpoint needing a header, obtain a valid private value using the supported setup; do not teach nonexistent interpolation or OAuth configuration fields.

For a remote server, place this object at mcp.servers.search; replace the placeholder URL/header, then select exact names returned by that server:

json
{ "transport": "streamable-http", "url": "https://example.test/mcp", "headers": { "Authorization": "Bearer REPLACE_IN_PRIVATE_CONFIG" } }

For example, select mcp/search/web_search only if that is a real listed name. The URL itself is not a tool ID. Keep existing agents, servers, defaults and selected tool ordering intact.

Register and verify the requested server

  1. Obtain server-specific command/arguments or URL, check executable/dependency availability, and discover the actual tools. Verify the tool's input schema before choosing test arguments.
  2. Back up the config, add one server definition, and append only requested mcp/<server>/<original-name> IDs to agents.<name>.tools.use. Keep config mode 0600. Follow the canonical/portable path actually in use.
  3. Run raw config list or raw --config PATH config list. This checks structure and selected IDs' syntax; it does not prove handshake, original tool existence or successful execution.
  4. If the installed @tlelabs/raw library is importable, call loadConfig({configPath,requireModel:false}), then createRuntimeTools({runtime,cwd}). tools.mcp.catalog contains discovered tools; tools.mcp.exposed contains selected ones; tools.warnings must be empty. Find the selected server/original name there, dispatch its alias through tools.registry.dispatch(alias,args,{cwd,maxOutputBytes:8192}), inspect the real result, and close tools.mcp in finally. This does not require a model request. Otherwise run a harmless task with the configured agent when model access is available. Report inability to verify rather than equating config parsing with a live pass.
  5. Use a harmless server-specific call. Do not perform writes, purchases or other unrelated side effects just to test the connection. Unselecting the server's tool IDs should leave the definition inert on the next startup.
  6. Add a requested policy under this agent's tools.rules, matching canonical mcp/server/tool, not the alias. Effects are allow/ask/deny, last matching rule wins, unmatched calls run. Only ask permits when on a schema-bound string path and an RE2 regex. Verify policy with harmless arguments; headless ask without approval fails closed. Policy does not sandbox the server.

The registry/schema view is fixed for a running CLI session. Editing config does not add a callable MCP alias to that same session automatically; verify the changed configuration through a fresh runtime or next run.

Optional complete stdio test fixture

Use only when the user wants a reproducible MCP smoke test without installing a real server. Save as an actual absolute echo.mjs path. It exposes echo_text over newline-delimited JSON-RPC:

js
import { createInterface } from "node:readline";
const lines = createInterface({ input: process.stdin });
for await (const line of lines) {
  let request;
  try { request = JSON.parse(line); } catch { continue; }
  if (request.id === undefined) continue;
  let result;
  if (request.method === "initialize") result = { protocolVersion: request.params?.protocolVersion ?? "2025-11-25", capabilities: { tools: {} }, serverInfo: { name: "raw-echo", version: "1.0.0" } };
  else if (request.method === "tools/list") result = { tools: [{ name: "echo_text", description: "Echo text for connection testing", inputSchema: { type: "object", properties: { text: { type: "string" } }, required: ["text"], additionalProperties: false } }] };
  else if (request.method === "tools/call" && request.params?.name === "echo_text") result = { content: [{ type: "text", text: String(request.params.arguments?.text ?? "") }] };
  else if (request.method === "ping") result = {};
  else { process.stdout.write(JSON.stringify({ jsonrpc: "2.0", id: request.id, error: { code: -32601, message: "Method not found" } }) + "\n"); continue; }
  process.stdout.write(JSON.stringify({ jsonrpc: "2.0", id: request.id, result }) + "\n");
}

Complete config for that fixture; replace both the model ID and script path. Its MCP server needs no credentials:

json
{
  "default_agent": "research",
  "models": { "local": { "provider": "ollama", "method": "openai-chat-completions", "model_id": "YOUR_INSTALLED_MODEL", "base_url": "http://127.0.0.1:11434/v1" } },
  "mcp": { "servers": { "echo": { "transport": "stdio", "command": "node", "args": ["/absolute/path/echo.mjs"] } } },
  "agents": { "research": { "model": "local", "tools": { "use": ["builtin/read_file", "mcp/echo/echo_text"] } } }
}

With this fixture, verify a real echo_text call with {"text":"hello"} returns hello. With a different requested server, use its real tool/schema instead.

Diagnose and report

  • Spawn/handshake failure: check command/PATH, cwd, script arguments, stdout protocol and timeout_ms.
  • Unknown tool/server: check server key and actual original names, including paginated tools/list; do not guess from an alias. Raw skips an unavailable selected server or tool with a stderr warning (unknown MCP tool NAME selected from SERVER; skipped) and still starts; a server upgrade can rename or drop tools, so replace stale IDs with current names.
  • Unsupported selected schema: identify the actual schema error; an unselected unsupported tool is inert.
  • Authentication failure: check the server's real credential mechanism and literal headers/environment, not model-provider credentials.
  • Image content failure: native images need a capable model/adapter; text-only agents can use OCR tools returning text.

Raw validates selected schemas/arguments and closes connections. Selected changes may rotate the generated key once; unselected edits stay inert. Valid changes resume on the same ID. Missing historical aliases leave saved ACP views, while unavailable current selections are skipped with a warning. Report checks and prerequisites.

Raw vars do not interpolate MCP env/headers or tool arguments. Use a server's inherited environment/auth mechanism, or a local tool consuming context.vars. Executable var_providers use a separate one-request JSON protocol, not MCP.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Add Mcp AI skill do?

Use when connecting an MCP server for Raw, explaining its binding, or diagnosing tools, including dashboard discovery. Select exact original names and verify live calls. Use create-package to share definitions.

Why use Add Mcp on TypingMind?

Because you install it once and use it with any model. Add Mcp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add Mcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lploc94/raw-cli/tree/main/examples/skills/add_mcp. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Add Mcp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add Mcp?

As many as you like. As long as a model supports skills, you can use Add Mcp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add Mcp AI skill free?

Yes. It is published on GitHub by lploc94 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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