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- Yuniorglez
- Gemini Elite Core
- Mcp Expert
mcp-expert_skill
- Python
7
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
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Installation
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npx veilstrat add skill yuniorglez/gemini-elite-core --skill mcp-expert- SKILL.md4.4 KB
Overview
This skill is a senior MCP architect and orchestrator toolkit for building, deploying, and operating Model Context Protocol ecosystems. It focuses on proactive onboarding, resilient orchestration, and developer-grade MCP App patterns that scale in production. The guidance targets server engineering, zero-trust security, and outcome-oriented agent tooling.
How this skill works
The skill inspects running MCP servers and agent toolsets, detects missing capabilities, and provides step-by-step onboarding actions. It defines production patterns for tools, retry semantics, and progressive-disclosure URIs to handle large datasets. It also prescribes server runtime, validation, and auth standards for secure, high-performance deployments.
When to use it
- Onboarding an agent that needs new MCP capabilities (e.g., web browsing, file processing).
- Designing or hardening MCP Apps and interactive agent UIs.
- Implementing production server patterns and validation for MCP tooling.
- Building zero-trust authentication and capability-scoped access for agents.
- Creating resilient orchestration with retries, timeouts, and error recovery.
Best practices
- Prefer outcome-oriented tools over chatty APIs to reduce token use and latency.
- Return helpful error strings and retry hints instead of raw exceptions.
- Expose large datasets via MCP URIs and progressive disclosure rather than full payloads.
- Limit server scope: build small, focused servers (5–15 tools) for easier discovery and maintenance.
- Store secrets out of code: use environment mapping and avoid hardcoded credentials.
Example use cases
- Auto-detect and provision a missing
browser-useMCP server with a guided install path. - Create an MCP App that renders a live dashboard and allows interactive filtering via URIs.
- Implement a retryable tool that suggests corrected inputs when user data is slightly malformed.
- Deploy a Bun-based MCP server with Zod validation and OAuth 2.1 capability scopes for enterprise access.
- Add a human-in-the-loop approval gate for destructive agent actions in production workflows.
FAQ
Use Bun for runtime and Zod for input validation to meet the 2025-11-25 server spec guidance.
How should I expose large documents to agents?
Use progressive disclosure: return MCP URIs for summaries and detail endpoints for on-demand retrieval.
How do I handle sensitive credentials?
Never hardcode secrets. Map credentials from environment variables and use capability-scoped tokens with OAuth 2.1.