mcp-validate
MCP ServerFreeValidate MCP server tool definitions against the spec. Checks names, descriptions, JSON Schema, parameter docs, and LLM-readiness.
Capabilities5 decomposed
mcp tool definition schema validation
Medium confidenceValidates MCP server tool definitions against the official Model Context Protocol specification by parsing tool metadata (name, description, input schema) and checking structural conformance to the spec's JSON Schema requirements. Uses schema introspection to ensure tools declare proper parameter types, required fields, and nested object structures before deployment.
Specifically targets MCP protocol compliance rather than generic JSON Schema validation, understanding MCP's tool definition structure (name, description, input_schema, required fields) and validating against the official MCP specification requirements
Provides MCP-specific validation that generic JSON Schema validators cannot offer, catching protocol-level errors that would cause tool registration failures in Claude or GPT integrations
tool name and description validation
Medium confidenceValidates tool naming conventions and description quality by checking that tool names follow MCP naming rules (alphanumeric, underscores, hyphens), descriptions are present and sufficiently detailed, and metadata is LLM-readable. Performs pattern matching and length validation to ensure tools are discoverable and understandable by language models.
Combines naming convention validation with LLM-readiness checks, ensuring tools are not just syntactically valid but also semantically discoverable by language models through clear, descriptive metadata
Goes beyond basic name validation to assess LLM-readiness of tool descriptions, whereas generic linters only check syntax and naming patterns
json schema parameter documentation validation
Medium confidenceValidates that tool input schemas include proper documentation for all parameters by checking for descriptions in schema properties, ensuring required fields are marked, and verifying type definitions are complete. Inspects the JSON Schema structure recursively to catch undocumented nested properties and missing type constraints that would confuse LLMs.
Performs recursive schema inspection to validate documentation at all nesting levels, not just top-level parameters, ensuring LLMs have complete information about complex tool inputs
Specifically targets parameter documentation quality for LLM consumption, whereas generic schema validators only check structural validity without assessing documentation completeness
llm-readiness assessment
Medium confidenceEvaluates whether tool definitions are optimized for language model understanding by analyzing description clarity, parameter documentation, schema completeness, and naming conventions. Produces a readiness score or report indicating whether the tool definition will be effectively understood and used by Claude, GPT, or other LLMs when exposed through MCP.
Combines multiple validation dimensions (naming, documentation, schema completeness, description quality) into a holistic LLM-readiness assessment specific to MCP tool definitions, rather than validating individual aspects in isolation
Provides LLM-specific readiness evaluation that generic validation tools cannot offer, focusing on factors that affect model understanding and tool invocation success
batch tool definition validation with reporting
Medium confidenceValidates multiple tool definitions in a single operation and generates a comprehensive report showing which tools pass/fail validation, what errors were found, and which tools need remediation. Processes tool definitions from an MCP server registry or tool collection and produces structured output suitable for CI/CD pipelines or developer dashboards.
Provides batch processing with structured reporting designed for CI/CD integration, allowing teams to validate entire tool collections and surface errors in a format suitable for automated pipelines and developer dashboards
Enables scalable validation of multiple tools with pipeline-friendly output, whereas point validation tools require per-tool invocation and manual aggregation
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓MCP server developers building custom tools
- ✓teams integrating MCP into Claude or GPT-based applications
- ✓developers migrating existing tool definitions to MCP format
- ✓MCP server developers ensuring tool discoverability
- ✓teams building LLM-integrated applications with custom tools
- ✓developers optimizing tool descriptions for model understanding
- ✓MCP developers building parameter-heavy tools
- ✓teams with complex nested tool schemas
Known Limitations
- ⚠Only validates against MCP spec structure — does not test runtime behavior or actual tool execution
- ⚠Cannot validate tool implementations themselves, only their declarations
- ⚠Requires tools to be defined in MCP-compatible format; legacy tool definitions may need conversion
- ⚠Cannot evaluate semantic quality of descriptions — only checks length and presence
- ⚠Does not test whether LLMs actually understand the tool based on description
- ⚠Naming validation is rule-based and may not catch domain-specific naming issues
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
Package Details
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Validate MCP server tool definitions against the spec. Checks names, descriptions, JSON Schema, parameter docs, and LLM-readiness.
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