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Mcp Manager

OrganizationPopular
clacky-ai
mcp-manager

Manage MCP (Model Context Protocol) servers for openclacky: add, list, probe, remove, reconfigure. Edits ~/.clacky/mcp.json so the user never writes JSON by hand. Trigger on: add mcp, install mcp, setup mcp, configure mcp, mcp list, mcp remove, mcp probe, mcp reconfigure.

Overview

Publisherclacky-ai
Repositoryopenclacky
Skill namemcp-manager
Stars
1.2K
Forks
111
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by clacky-ai on GitHub. Read the source before you install it.

Installation

Install the Mcp Manager 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/clacky-ai/openclacky/tree/main/lib/clacky/default_skills/mcp-manager
  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/clacky-ai/openclacky.git /tmp/openclacky
mkdir -p .claude/skills
cp -r /tmp/openclacky/lib/clacky/default_skills/mcp-manager .claude/skills/mcp-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Manager 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 Mcp Manager 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 Mcp Manager 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.

MCP Manager Skill

Manage MCP servers for openclacky. The user's MCP configuration lives at ~/.clacky/mcp.json (the same format Claude Desktop and Cursor use). You never ask the user to edit it by hand — you do it for them through the local clacky HTTP API.


Command Parsing

User saysSubcommand
add mcp, install mcp, connect <something>, "I want clacky to read my files / access github / query my db / search the web"add
mcp list, mcp status, "what mcps do I have"list
mcp probe <name>, "what tools does have"probe
mcp remove <name>, mcp delete <name>remove
mcp reconfigure <name>, mcp fix <name>reconfigure

If the intent is unclear, default to add — it's the most common ask.


Server Coordinates

All API calls go to the local clacky server. The host and port are exposed via environment variables:

bash
HOST="${CLACKY_SERVER_HOST:-127.0.0.1}"
PORT="${CLACKY_SERVER_PORT:-7070}"
BASE="http://${HOST}:${PORT}"

All write operations require requests to come from 127.0.0.1 or ::1. They will, because we're running locally.


API Cheat Sheet

ActionCall
List configured serverscurl -s ${BASE}/api/mcp
Add a servercurl -s -X POST ${BASE}/api/mcp -H 'Content-Type: application/json' -d '{...}'
Update a servercurl -s -X PUT ${BASE}/api/mcp/<name> -H 'Content-Type: application/json' -d '{...}'
Remove a servercurl -s -X DELETE ${BASE}/api/mcp/<name>
Probe toolscurl -s -X POST ${BASE}/api/mcp/<name>/probe

Request body for create/update — stdio (local process, default):

json
{
  "name": "filesystem",
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/Documents"],
  "env": { "API_KEY": "xxx" },
  "description": "Read/write files in ~/Documents"
}

Request body for create/update — http (remote server, streamable-http):

json
{
  "name": "linear",
  "type": "http",
  "url":  "https://mcp.linear.app/sse",
  "headers": { "Authorization": "Bearer lin_api_xxx" },
  "description": "Linear issues and projects"
}

If type is omitted but url is present, the server treats it as http.


Known-Good Server Catalog

When the user describes what they want, match it to one of these and propose it. Each entry: package, what it does, required params, recommended description.

1. filesystem — read/write local files

  • When: "read my files", "access my desktop", "browse my code"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-filesystem", "<ABSOLUTE_PATH>"]
  • Required: absolute directory path (ask user; default to ~/Documents)
  • Tools: read_file, write_file, list_directory, search_files, etc.

2. github — GitHub repos, issues, PRs

  • When: "access github", "manage my repos", "read my issues"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-github"]
  • Env: { "GITHUB_PERSONAL_ACCESS_TOKEN": "<TOKEN>" }
  • Required: PAT from https://github.com/settings/tokens (recommend repo scope)

3. fetch — fetch HTTP URLs as markdown

  • When: "fetch web pages", "read articles by url"
  • Command: uvx
  • Args: ["mcp-server-fetch"]
  • Required: nothing
  • Note: needs Python uv installed (brew install uv)

4. memory — persistent knowledge graph

  • When: "remember things across sessions", "give clacky long-term memory"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-memory"]
  • Required: nothing

5. postgres — query a Postgres database

  • When: "query my database", "connect to postgres"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-postgres", "<DATABASE_URL>"]
  • Required: DATABASE_URL like postgresql://user:pass@host:5432/dbname

6. slack — Slack messages

  • When: "read slack", "send slack messages"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-slack"]
  • Env: { "SLACK_BOT_TOKEN": "xoxb-...", "SLACK_TEAM_ID": "T..." }
  • Required: bot token and team id (Slack admin → app config)

7. brave-search — web search via Brave API

  • When: "search the web", "give clacky search"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-brave-search"]
  • Env: { "BRAVE_API_KEY": "<KEY>" }
  • Required: free API key from https://api.search.brave.com/

8. puppeteer — browser automation

  • When: "automate the browser", "scrape with js"
  • Command: npx
  • Args: ["-y", "@modelcontextprotocol/server-puppeteer"]
  • Required: nothing (downloads Chromium on first run)

Custom (anything else)

If the user names a package or path you don't recognize, take the spec from them verbatim and pass it through. Always confirm command, args, and env back in plain language before saving.

Remote / HTTP servers (streamable-http)

Some MCP servers are hosted services and don't ship as a CLI — you connect over HTTPS instead. Trigger when the user gives you a URL ending in /mcp, /sse, or hosted on *.mcp.* / mcp.*.app, or says "the server is at https://...".

  • Type: http
  • Required: url (the streamable-http endpoint)
  • Optional: headers — typically { "Authorization": "Bearer <token>" }

Examples of remote MCP servers in the wild:

  • Linear: https://mcp.linear.app/sse (Bearer API key)
  • Cloudflare: https://<workers-subdomain>.workers.dev/mcp (Bearer token)
  • GitHub Copilot: https://api.githubcopilot.com/mcp/ (OAuth, advanced)

When the user pastes a URL, ask:

  1. What service is this? (so you can pick a name and description)
  2. Does it need an authorization header? If yes, paste the token.

Save with type: "http". The local clacky never spawns a process for these — it just POSTs JSON-RPC over HTTPS.

⚠️ Wrapping a regular CLI tool: if the user gives you a CLI command that is not a stdio MCP server (e.g. mcp-cli, some-api-cli login), do NOT save it as a stdio MCP entry — it won't speak JSON-RPC over stdin. Tell them: "This looks like a regular CLI, not an MCP server. Does the service offer an HTTPS endpoint instead?"


Subcommand: add — the primary flow

Goal: the user describes what they want, you produce a working MCP entry + confirm it works. Keep questions minimal.

Step 1 — Identify intent

  • If the user's first message already names a server (e.g. "add filesystem"), pick that catalog entry directly.
  • Otherwise, ask one open question: "What would you like Clacky to be able to do? (e.g. read your files, access GitHub, search the web)"
  • Match their answer to the catalog. If multiple match, present 2–3 options with one-line descriptions and let them pick.

Step 2 — Environment preflight

Before asking for parameters, check the runtime is installed:

bash
# For npx-based servers
which npx >/dev/null 2>&1 || echo "MISSING_NPX"

# For uvx-based servers
which uvx >/dev/null 2>&1 || echo "MISSING_UVX"

If missing, tell the user how to install (brew install node for npx, brew install uv for uvx) and stop. Do not proceed.

Step 3 — Collect parameters

Ask only for the business-meaningful params from the catalog entry:

  • For filesystem: which directory? Default offer: ~/Documents. Resolve ~ to an absolute path before saving.
  • For github/brave-search/slack: tell them where to get the token, then ask them to paste it.
  • For postgres: ask for the connection URL.

Never invent values. If you don't have a sensible default, ask.

Step 4 — Confirm

Show the user the spec you're about to save, in plain language:

I'll add a server called filesystem that runs npx -y @modelcontextprotocol/server-filesystem /Users/me/Documents. It'll let me read and write files in your Documents folder. OK?

For secrets (tokens, passwords), echo only the last 4 characters: ***...abcd.

Step 5 — Save

For stdio:

bash
curl -s -X POST ${BASE}/api/mcp \
  -H 'Content-Type: application/json' \
  -d '{
        "name":        "filesystem",
        "command":     "npx",
        "args":        ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/Documents"],
        "description": "Read/write files in ~/Documents"
      }'

For http:

bash
curl -s -X POST ${BASE}/api/mcp \
  -H 'Content-Type: application/json' \
  -d '{
        "name":        "linear",
        "type":        "http",
        "url":         "https://mcp.linear.app/sse",
        "headers":     { "Authorization": "Bearer lin_api_xxx" },
        "description": "Linear issues and projects"
      }'

If the response has "ok": false, show the error and ask the user how to proceed (retry, edit, abort).

Step 6 — Probe

Immediately verify the server starts and exposes tools:

bash
curl -s -X POST ${BASE}/api/mcp/filesystem/probe
  • ok: true: extract tools[], summarize for the user. Example:

    Done. filesystem is working — Clacky now has 11 new tools (read_file, write_file, list_directory, ...). Try asking me to list files in your Documents folder.

  • ok: false: show the error verbatim and offer common fixes:
    • "command not found" → wrong runtime, suggest re-running with correct one
    • "ENOENT" / "no such file" → bad path, ask for a valid one
    • timeout → package may be downloading on first run; suggest retrying
    • auth-related → token wrong/expired, offer reconfigure

Step 7 — Hint at next steps

End with a one-line nudge: how the user can use the new MCP next. Examples:

  • filesystem: "Try: list the files in my Documents folder"
  • github: "Try: show me my open PRs"
  • fetch: "Try: fetch https://news.ycombinator.com and summarize"

Subcommand: list

bash
curl -s ${BASE}/api/mcp

Render as a short table. If configured: false, say so and offer to run add.

| Name         | Command | Args summary           | Has env |
|--------------|---------|------------------------|---------|
| filesystem   | npx     | @modelcontextprotocol… | no      |
| github       | npx     | @modelcontextprotocol… | yes     |

Don't show full args if they contain absolute paths — collapse them with ….


Subcommand: probe <name>

bash
curl -s -X POST ${BASE}/api/mcp/<name>/probe

If ok: true, list every tool with a one-line description. If ok: false, run the same error-fixing flow as in add step 6.


Subcommand: remove <name>

  1. Confirm with the user first: "Remove ? Its tools will no longer be available to Clacky. (Y/n)"
  2. On yes:
    bash
    curl -s -X DELETE ${BASE}/api/mcp/<name>
  3. Confirm completion in one line.

Subcommand: reconfigure <name>

  1. Fetch current spec from /api/mcp and show it back.
  2. Ask which fields to change (path / token / args).
  3. Build the new spec and PUT /api/mcp/<name>.
  4. Probe to verify, same as add step 6.

General Rules

  • Never write directly to ~/.clacky/mcp.json. Always go through the API.
  • Never echo full secrets. Mask all but last 4 chars of tokens/URLs.
  • One question at a time. Don't dump a form on the user.
  • Stop on errors. Don't proceed past a failed preflight or probe.
  • Quote real error messages. Don't paraphrase API errors — users may need to google them.
  • Stay in scope. If the user wants to write/edit a non-MCP file or do unrelated work, hand back to the main agent.

Frequently asked questions

What does the Mcp Manager AI skill do?

Manage MCP (Model Context Protocol) servers for openclacky: add, list, probe, remove, reconfigure. Edits ~/.clacky/mcp.json so the user never writes JSON by hand. Trigger on: add mcp, install mcp, setup mcp, configure mcp, mcp list, mcp remove, mcp probe, mcp reconfigure.

Why use Mcp Manager on TypingMind?

Because you install it once and use it with any model. Mcp Manager 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 Mcp Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/clacky-ai/openclacky/tree/main/lib/clacky/default_skills/mcp-manager. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mcp Manager?

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 Mcp Manager?

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

Is the Mcp Manager AI skill free?

Yes. It is published on GitHub by clacky-ai 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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