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Mcp Builder

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vudovn
mcp-builder

MCP (Model Context Protocol) server and client engineering. Protocol design, schemas, transports, extensions, security, and migration.

Overview

Publishervudovn
Repositoryag-kit
Skill namemcp-builder
Stars
8.2K
Forks
1.5K
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 vudovn on GitHub. Read the source before you install it.

Installation

Install the Mcp Builder 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/vudovn/ag-kit/tree/main/.agents/skills/mcp-builder
  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/vudovn/ag-kit.git /tmp/ag-kit
mkdir -p .claude/skills
cp -r /tmp/ag-kit/.agents/skills/mcp-builder .claude/skills/mcp-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Builder 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 Builder 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 Builder 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 Builder

Build interoperable MCP implementations against the stable 2026-07-28 specification. Treat extensions as negotiated, opt-in capabilities rather than core protocol guarantees.


1. Stable baseline and compatibility

  • The authoritative stable baseline is MCP 2026-07-28.
  • Core requests are stateless and self-contained. Do not infer protocol version, client capabilities, identity, conversation, or task state from a transport connection or process lifetime.
  • Put cross-request state behind explicit identifiers supplied on every relevant request.
  • Negotiate the protocol version and capabilities; do not assume that a client or server implements every optional feature.
  • Preserve compatibility with earlier peers where the specification requires it, including accepting legacy resource-not-found error code -32002 while emitting -32602 for invalid parameters in the current protocol.

Core versus extensions

LayerStatusEngineering rule
Base protocol, versioning, message patternsStable coreRequired for every implementation
Resources, prompts, tools, elicitationOptional core featuresAdvertise and check capabilities before use
Tasks, Skills over MCP, MCP AppsExtensionsOpt-in; require explicit support from both peers
Draft or vendor extensionsExperimental/vendor-specificIsolate behind adapters and feature flags

Never label an extension as stable merely because one SDK or host supports it. Check the extension's own official status and version.


2. Architecture

MCP uses JSON-RPC 2.0 between hosts, clients, and servers.

text
host application
  └─ MCP client / runtime adapter
       ├─ stdio transport
       └─ Streamable HTTP transport
            └─ MCP server
                 ├─ resources
                 ├─ prompts
                 └─ tools

Stateless request model

Every request must carry the protocol metadata required by the negotiated version. For 2026-07-28 this includes per-request protocol version and client capability metadata under reserved _meta keys.

Servers must implement server/discover for protocol-version and capability discovery. Clients may call it before normal requests or use it as a backward-compatibility probe for stdio peers.

Do not:

  • use a stdio process as a conversation or session boundary;
  • depend on request ordering to establish capabilities or identity;
  • keep hidden mutable state without an explicit handle;
  • use self-reported client/server information for authorization decisions.

Do:

  • make task, thread, workspace, or subscription handles explicit;
  • validate every request independently;
  • tolerate interleaved requests from unrelated tasks;
  • document lifecycle, expiry, and cleanup for durable handles.

3. Transports and authentication

TransportTypical useSecurity baseline
stdioLocal process integrationRead credentials from the environment; constrain process and filesystem access
Streamable HTTPRemote or shared serviceUse the MCP HTTP authorization framework, validate origins where applicable, and enforce network boundaries

WebSocket is not a standard MCP transport unless defined by a separately negotiated extension or vendor adapter.

Transport concerns must not leak into protocol semantics. Reconnection or process replacement must not destroy logical state that is represented by an explicit durable handle.


4. Schema design and validation

MCP defaults to JSON Schema 2020-12 when $schema is absent. Implementations must support that dialect and should document any additional dialects.

Tool input schemas

  • Use an object root for tool arguments.
  • Give every property a clear, action-oriented description.
  • Declare required fields explicitly.
  • Prefer narrow enums, bounds, formats, and additionalProperties: false where forward compatibility does not require open objects.
  • Validate both the schema and each invocation payload.

Tool output schemas

Use structured content and outputSchema when consumers need typed, predictable results. Keep human-readable text concise and ensure structured output remains the source of truth for automation.

$ref and validator safety

  • Never dereference network $ref values automatically.
  • If remote dereferencing is explicitly enabled, use a host allowlist; reject loopback, link-local, and private addresses; apply timeouts and response-size limits; and log resolved URIs without secrets.
  • Reject schemas with unresolved external references instead of treating them as permissive.
  • Bound schema depth, total subschemas, and validation time to prevent denial-of-service through composition keywords or recursive references.

5. Tool, resource, and prompt design

Tools

  • Use clear action names and a single responsibility.
  • Mark read-only, destructive, idempotent, or open-world behavior with annotations when useful, but treat annotations as untrusted hints unless the server itself is trusted.
  • Require explicit user approval for consequential actions.
  • Return stable machine-readable errors without internal stack traces or secrets.

Resources

  • Use stable URIs and MIME types.
  • Separate discovery metadata from resource content.
  • Apply authorization per resource, not only per server connection.
  • Avoid silently forwarding resource data to another server or model without user consent.

Prompts

  • Treat prompts as user-visible templates, not hidden authority.
  • Keep tool permissions and policy enforcement outside prompt text.
  • Validate prompt arguments and clearly identify any data sources included in generated messages.

6. Security requirements

  1. Consent and least privilege — expose only required data and obtain explicit approval before tool execution or data sharing.
  2. Untrusted metadata — do not use tool descriptions, annotations, client info, server info, or model-produced arguments as authorization evidence.
  3. Input and output validation — validate schemas, arguments, structured results, URIs, and content types at trust boundaries.
  4. Secret handling — load credentials from environment or a secret manager; never place real keys in repository configuration, logs, traces, prompts, or error payloads.
  5. Network controls — defend remote schema/resource fetching against SSRF, redirect abuse, oversized responses, and slow responses.
  6. Execution isolation — sandbox subprocesses and filesystem access; use explicit path grants and reject archive or symlink escapes.
  7. Auditability — record security-relevant decisions, approvals, tool identity, and result status without storing sensitive payloads unnecessarily.

7. Extensions

Extensions are negotiated independently from the core protocol.

ExtensionUseGuardrail
TasksLong-running work, polling, durable handles, mid-flight inputDo not use the older experimental task API; negotiate the extension
Skills over MCPDiscoverable structured agent instructionsTreat skill content as untrusted input and apply host policy
MCP AppsInteractive UI rendered in conversationsConstrain origins, content, data access, and action permissions

An adapter must fail gracefully when an extension is absent. Never silently downgrade a security requirement to preserve feature parity.


8. Deprecated features and migration

The 2026-07-28 specification deprecates legacy roots, sampling, and logging shapes while keeping a compatibility window. Do not remove compatibility abruptly, but avoid designing new architecture around deprecated forms.

Migration checklist from 2025-era implementations

  • Replace connection-scoped sessions and the initialize handshake with stateless per-request metadata.
  • Implement or consume server/discover for version and capability selection.
  • Move durable state behind explicit task, thread, workspace, or application handles.
  • Send and validate negotiated protocol version and client capabilities on every request.
  • Upgrade schema validation to JSON Schema 2020-12 by default.
  • Disable network $ref resolution by default and add validation resource limits.
  • Move experimental Tasks usage to the negotiated Tasks extension.
  • Accept legacy -32002 resource-not-found responses, but emit current error semantics.
  • Treat a missing resultType from older servers as "complete"; require it for current-version results.
  • Audit roots, sampling, and logging integrations for their documented replacements and deprecation timeline.
  • Add interoperability tests against at least one older peer and one 2026-07-28 peer.

9. Testing matrix

TestExpected evidence
ContractInvalid JSON-RPC, missing metadata, unsupported version/capability, schema dialect handling
StatelessnessInterleaved requests, reconnects, process reuse, explicit-handle recovery
Schema securityExternal $ref, recursive/composed schemas, depth/count/time limits
AuthorizationDenied resource/tool access, approval gates, credential isolation
Extension negotiationExtension absent, version mismatch, feature enabled, graceful fallback
CompatibilityCurrent peer plus supported older protocol revision
Failure handlingTimeouts, cancellation, partial results, structured errors, no secret leakage

10. Review checklist

  • Stable protocol version and extension status are documented.
  • Requests are stateless; cross-request state uses explicit handles.
  • Capabilities are negotiated and checked before optional operations.
  • JSON Schema 2020-12 is supported and validator resource use is bounded.
  • Network $ref resolution is disabled by default.
  • Tool annotations are not trusted as authorization.
  • User approval, least privilege, secret handling, and execution isolation are enforced outside prompts.
  • Deprecated features have a compatibility and migration plan.
  • Contract, security, extension, and cross-version tests pass.

Frequently asked questions

What does the Mcp Builder AI skill do?

MCP (Model Context Protocol) server and client engineering. Protocol design, schemas, transports, extensions, security, and migration.

Why use Mcp Builder on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vudovn/ag-kit/tree/main/.agents/skills/mcp-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mcp Builder?

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 Builder?

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

Is the Mcp Builder AI skill free?

Yes. It is published on GitHub by vudovn 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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