Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    mcp tool description engineer

    1

    Optimize MCP tool definitions and schemas to stop agents from calling the wrong tools or hallucinating parameters.

    $11

    30-day refund guarantee

    Secure checkout via Stripe

    0 installsSecurity scanned

    See it in action

    You say

    My agent keeps using the 'get_log' tool for both system logs and application logs, but they require different parameters. How do I fix the tool definitions so it stops mixing them up?

    Your agent does

    1. Rename tools to 'get_system_audit_log' and 'get_app_runtime_log'.
    2. Add a WHEN clause: 'Use this only for OS-level events' vs 'Use for stack traces and app errors'.
    3. In inputSchema, change the generic 'id' parameter to 'service_name' with an enum of valid options.

    What you get

    Disambiguate overlapping tools that confuse the agent.Eliminate parameter hallucinations using strict JSON schema constraints.Structure large toolsets using namespacing and discovery patterns.Build a regression test battery for tool-selection accuracy.

    About this skill

    The problem

    Model selection accuracy degrades as you add more tools to an MCP server. Agents call the wrong functions, skip required parameters, or hallucinate argument values because your tool definitions are treated as documentation rather than steering signals.

    What it does

    • Audits existing tools/list payloads for naming collisions, ambiguous parameter schemas, and missing trigger conditions.
    • Rewrites tool names and descriptions to emphasize the decision boundary between similar capabilities.
    • Hardens inputSchema definitions using enums, format hints, and descriptive parameter names to prevent hallucinated values.
    • Architects namespacing and grouping strategies for servers exceeding 15 to 20 tools to maintain selection reliability.
    • Constructs a repeatable test battery to measure and verify tool-selection accuracy rates against realistic prompts.

    Frameworks & tools

    Model Context Protocol (MCP), JSON Schema, Anthropic tool-use specs.

    Why this beats prompting it yourself

    Writing tool definitions that "look right" to a human often fails in production because models respond to different linguistic cues. This skill applies verified patterns from primary MCP benchmarks to ensure the model picks the right tool every time, not just most of the time.

    Use cases

    • Fixing an agent that keeps calling search_users when it needs search_orders.
    • Refactoring a massive MCP server with 40+ tools that has become unreliable.
    • Preventing parameter hallucinations by tightening loose type: string definitions.
    • Validating tool selection accuracy before shipping a new MCP server to production.

    Known limitations

    This tool focuses on functional correctness and selection accuracy. It does not perform security audits for prompt injection or tool-poisoning vulnerabilities.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    30-day refund

    Not a fit? Get your money back

    Trust & safety

    Security scanned

    Verified clean today

    • One-time purchase, yours forever

    Listedtoday

    Frequently Asked Questions