What are schema hints in error responses?

Schema hints are subsets of tool definitions included in error messages to show the client exactly which parameters are required, optional, or incorrectly typed without requiring a full documentation lookup.

How does this skill handle typos in tool names?

It implements a similarity algorithm combining Levenshtein distance, prefix matching, and substring overlap to suggest the most likely intended tool names to the user.

Can this skill automatically fix user input?

Yes, it provides patterns for high-confidence auto-correction where the server can fix a parameter name and notify the client, preventing unnecessary execution failures.

Does this work with any MCP server?

Yes, these are implementation patterns and guidance strategies that can be applied to any server following the Model Context Protocol specification.

What is the primary goal of the MCP Client Guidance skill?

The goal is to transition MCP error handling from simple rejection to 'progressive enhancement' where errors guide the client toward a successful request through suggestions and hints.

MCP Client Guidance

Name: MCP Client Guidance
Author: standardbeagle

bystandardbeagle

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API Development

Enhances MCP server responses with progressive error feedback, fuzzy matching, and actionable suggestions to guide clients toward successful tool usage.

About

The MCP Client Guidance skill provides a comprehensive framework for designing robust Model Context Protocol (MCP) servers that prioritize developer experience through progressive enhancement. Instead of returning static rejection messages, this skill implements sophisticated patterns like 'did you mean' suggestions for tools, parameter auto-correction, and contextual schema hints. By providing immediate, actionable feedback and path-to-success alternatives when a client makes a mistake, it significantly reduces debugging friction and improves the reliability of agentic tool interactions within the Claude Code ecosystem.

Key Features

1 GitHub stars
Contextual next steps for both successful queries and error states
Progressive schema disclosure for contextual, non-intrusive documentation
Fuzzy matching for tool and parameter name suggestions using Levenshtein distance
High-confidence parameter auto-correction with transparent user notification
Detailed schema hints embedded directly within error responses

Use Cases

Designing user-friendly MCP tool interfaces for autonomous AI agents
Reducing agent failure rates in complex workflows due to minor syntax or naming typos
Building self-documenting API responses for specialized developer toolsets

About

Key Features

1 GitHub stars
Contextual next steps for both successful queries and error states
Progressive schema disclosure for contextual, non-intrusive documentation
Fuzzy matching for tool and parameter name suggestions using Levenshtein distance
High-confidence parameter auto-correction with transparent user notification
Detailed schema hints embedded directly within error responses

Use Cases

Designing user-friendly MCP tool interfaces for autonomous AI agents
Reducing agent failure rates in complex workflows due to minor syntax or naming typos
Building self-documenting API responses for specialized developer toolsets