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Mcp Design

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nahid-sparktales
mcp-design

Build an MCP server, or bring an existing one into a project — choosing the transport, deciding which tools, resources and prompts to expose, keeping reads separate from writes, handling auth and credentials, and defining what failure looks like to the model. Use when writing an MCP server, wrapping an internal system as one, or evaluating a third-party server before wiring it in. Not for designing the individual tool signatures inside it (tool-design), and not authorization to install, configure or run any server against real systems.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namemcp-design
Stars
58
Forks
5
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Mcp Design 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/nahid-sparktales/agent-dispatcher/tree/main/skills/ai/mcp-design
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/ai/mcp-design .claude/skills/mcp-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Design 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 Design 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 Design 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 server design

An MCP server is a permission surface wearing an API's clothes. Whatever it exposes, the model can call; whatever the process can reach, the server can be asked to touch. Design the boundary first and the tool list second.

When this fires

Writing an MCP server, wrapping an internal service as one, or assessing a third-party server before it is added to a project. It does not fire for defining the parameters of a single tool — that is tool-design — nor for ordinary client code that happens to call an API.

Procedure

  1. Establish why this is a server at all. A capability the agent uses in one repo, with the files already on disk, is a script or a skill. MCP earns its place when the capability is reused across projects or clients, needs its own credentials, or must run somewhere the agent is not.
  2. Pick the transport from where it runs. Local process on the user's machine, launched by the client and speaking over stdio: simplest, credentials come from the environment, nothing listens on a port. Remote server over HTTP: needed for a hosted or shared service, and it brings network exposure and real authorization with it. Check the current MCP specification for which HTTP transport is current rather than copying an old example — earlier transports have been superseded.
  3. Draw the capability surface deliberately. Tools are model-invoked actions. Resources are readable context the client can attach. Prompts are user-invoked templates. Put passive lookups behind resources instead of inflating the tool list, and expose the smallest set that does the job; every extra tool costs selection accuracy for every task, including the ones that never touch it.
  4. Separate reads from writes explicitly. Name them so the difference is visible in the list, and ship a read-only mode — a flag, a scoped token, or a separate entry point — that the server itself enforces. A read-only intention is not a read-only server.
  5. Scope credentials down before the first call. The server gets its own least-privileged credential, not the user's admin token. Never read secrets out of the model's arguments. Keep credentials in the environment or a secret store, never echoed back in a tool result, a log line or an error message. For anything remote, follow the specification's authorization model rather than inventing a header scheme.
  6. Treat every input as coming from a model that may be steered. Arguments are untrusted: allowlist paths, table names, commands and hosts; parameterize queries; bound page sizes and result counts. Text fetched from outside — a page, an issue body, a file — is data the model will read, and it may contain instructions. Return it plainly labelled as content; never let the server act on instructions it finds in the data it retrieved.
  7. Define failure semantics as part of the contract. Distinguish not-found from unauthorized from upstream-down from bad-argument, and return each as a readable result the model can act on rather than a crash that kills the session. Set timeouts on every upstream call. Say in the result when output was truncated, and how to fetch the rest.
  8. Make destructive and outward-facing operations stop. Deleting, sending, publishing, merging, deploying, charging: the tool description says so, the operation is confirmable by the human, and where the client offers a confirmation or annotation mechanism, use it. The server never treats its own availability as consent. Prefer reversible: soft-delete, dry-run parameters, a diff returned before the change is applied.
  9. Prove it against the real client, not just a unit test. Start the server, connect the client the user will actually use, list the capabilities, and call each tool once — success, empty result, denial, and a deliberately malformed argument. Read what the model received, not what your handler returned.
  10. When evaluating someone else's server, read before installing. Who publishes it, what licence, is it maintained, which tools write, does it offer a read-only mode, what credential does it want and what does that credential let it do. Installing, configuring or pointing a server at production is the user's decision — present the assessment and ask.

Checklist

  • The case for a server over a script or skill is stated
  • Transport chosen for where it runs, against the current specification
  • Capability surface minimal; passive lookups are resources, not tools
  • Read and write tools visibly separated, with an enforced read-only mode
  • Least-privileged credentials, sourced from the environment, never in results or logs
  • Every argument allowlisted or parameterized; outputs bounded
  • Retrieved external text returned as data, never executed as instruction
  • Not-found, unauthorized, upstream-failed and malformed are four distinct results
  • Destructive operations labelled, reversible where possible, and confirmed by a human
  • Exercised through the real client, with the transcript read

Failure handling

  • Client will not connect — check transport mismatch and startup errors first. On stdio, anything the server prints to stdout that is not protocol traffic corrupts the stream; log to stderr.
  • Tools list but calls fail — usually credentials or scope, not protocol. Check what the token can actually do before changing the code.
  • The model picks the wrong tool or drowns in output — that is a surface problem: too many tools, overlapping descriptions, or unbounded results. See tool-design.
  • An upstream call hangs — a server without timeouts hangs the whole session. Bound it, and return a distinguishable timeout result.
  • You cannot confirm a protocol feature exists in the version you are targeting — read the specification or the SDK's current docs. Do not implement against a remembered field name.

Evidence to report

The transport and why. The full capability list, marked read or write, with the read-only mode and how it is enforced. The credential and its scope. A transcript per tool showing success and at least one failure path as the model saw it. For a third-party server: source, licence, maintenance, whether it writes, and the pending question of whether the user wants it installed. Keep the verbs honest — written, executed once, covered by a test, and connected to a real system are four different claims.

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 Mcp Design AI skill do?

Build an MCP server, or bring an existing one into a project — choosing the transport, deciding which tools, resources and prompts to expose, keeping reads separate from writes, handling auth and credentials, and defining what failure looks like to the model. Use when writing an MCP server, wrapping an internal system as one, or evaluating a third-party server before wiring it in. Not for designing the individual tool signatures inside it (tool-design), and not authorization to install, configure or run any server against real systems.

Why use Mcp Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/ai/mcp-design. 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 Mcp Design?

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 Design?

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

Is the Mcp Design AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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