mcp-tool-lint
MCP ServerFreeStatic linter for MCP tool definitions — catch quality defects before deployment
Capabilities10 decomposed
mcp tool definition schema validation
Medium confidenceValidates MCP tool definitions against the Model Context Protocol specification schema, checking for required fields, type correctness, and structural compliance. Uses JSON schema validation to ensure tool definitions conform to MCP standards before they are exposed to LLM clients, preventing runtime failures and protocol violations.
Specialized linter built specifically for MCP tool definitions rather than generic JSON validation, understanding MCP-specific constraints like tool naming conventions, input schema requirements, and Claude-specific tool metadata
More targeted than generic JSON schema validators because it understands MCP semantics and can provide MCP-specific error messages and remediation guidance
tool parameter schema linting
Medium confidenceAnalyzes tool input parameter schemas for completeness, type safety, and usability issues. Checks for missing descriptions, ambiguous type definitions, undocumented required fields, and parameter naming inconsistencies that could confuse LLM clients when invoking tools.
Evaluates parameters specifically from the perspective of LLM usability — checking whether descriptions are clear enough for an LLM to understand and invoke correctly, not just whether they are syntactically valid
Goes beyond generic schema validation by assessing parameter clarity and LLM-friendliness, whereas standard JSON schema validators only check structural correctness
tool naming and convention checking
Medium confidenceLints tool names, descriptions, and identifiers against MCP and industry best practices for naming conventions. Detects non-standard naming patterns, overly long or unclear tool names, and inconsistent naming styles across tool suites that could reduce discoverability or clarity for LLM clients.
Applies MCP-specific naming conventions and LLM discoverability heuristics rather than generic code style rules, understanding that tool names are part of the LLM's decision-making context
Specialized for MCP tool naming rather than generic code linters, with rules tailored to how LLMs parse and understand tool names
tool description quality assessment
Medium confidenceEvaluates tool descriptions for clarity, completeness, and LLM-friendliness using heuristics like length, specificity, and presence of usage examples or caveats. Detects vague descriptions, missing context about tool behavior, and descriptions that lack sufficient detail for an LLM to make informed invocation decisions.
Assesses descriptions specifically for LLM comprehension rather than human readability, using heuristics tuned to how LLMs parse tool documentation to make invocation decisions
Specialized for LLM-facing documentation quality rather than generic documentation linters, with metrics focused on clarity for AI clients
tool response schema validation
Medium confidenceValidates tool output/response schemas for completeness and consistency, checking that response structures are well-defined, documented, and compatible with MCP expectations. Detects missing response descriptions, undefined response types, and inconsistent response structures across similar tools.
Validates response schemas from the perspective of LLM client expectations, ensuring responses are structured in ways that LLM clients can reliably parse and understand
Goes beyond generic schema validation by checking response clarity and LLM-friendliness, whereas standard validators only check structural correctness
tool dependency and integration checking
Medium confidenceAnalyzes tool definitions for external dependencies, required environment variables, API keys, and integration points, flagging missing or incomplete dependency declarations. Detects tools that reference external services without documenting authentication requirements or configuration needs.
Specifically designed for MCP tool deployment scenarios, checking for MCP-specific integration patterns like authentication, configuration, and external service requirements
More targeted than generic dependency checkers because it understands MCP deployment contexts and can validate MCP-specific configuration patterns
tool error handling and edge case documentation
Medium confidenceLints tool definitions for documentation of error conditions, edge cases, and failure modes. Detects tools that lack error documentation, missing information about rate limits or quotas, and undocumented failure scenarios that could surprise LLM clients.
Specifically checks for documentation of error conditions and edge cases that matter to LLM clients, ensuring LLMs understand when tools might fail or behave unexpectedly
Specialized for LLM-facing error documentation rather than generic code quality checks, with focus on preventing LLM misuse of tools
batch tool definition linting with aggregated reporting
Medium confidenceProcesses multiple MCP tool definitions in a single pass, aggregating linting results across an entire tool suite and providing consolidated reports. Enables cross-tool consistency checking, duplicate detection, and suite-wide quality metrics with configurable rule sets and output formats.
Designed for suite-wide linting with aggregated reporting rather than single-tool validation, enabling consistency checking and quality metrics across entire MCP tool collections
More efficient than running individual linters on each tool because it processes the entire suite in one pass and provides cross-tool consistency analysis
configurable linting rules and custom rule support
Medium confidenceProvides a rule configuration system allowing teams to customize which linting checks are enabled, set severity levels, and define custom validation rules specific to their MCP tool standards. Supports rule inheritance, overrides, and environment-specific configurations for different deployment contexts.
Provides a rule configuration system specifically designed for MCP tool validation rather than generic linting, with rules tailored to MCP-specific concerns like LLM compatibility
More flexible than fixed-rule linters because it allows teams to define custom validation rules matching their specific MCP tool standards
integration with ci/cd pipelines and pre-commit hooks
Medium confidenceProvides CLI interface and exit codes compatible with CI/CD systems, enabling integration as a build step or pre-commit hook. Supports JSON output for machine parsing, failure thresholds, and automated tool validation in development workflows without manual intervention.
Designed specifically for MCP tool validation in CI/CD contexts with exit codes and output formats optimized for automated workflows
More suitable for automated tool validation than manual linting because it integrates directly with CI/CD systems and provides machine-parseable output
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with mcp-tool-lint, ranked by overlap. Discovered automatically through the match graph.
@toolspec/core
MCP tool schema linting and quality scoring engine
mcp-validate
Validate MCP server tool definitions against the spec. Checks names, descriptions, JSON Schema, parameter docs, and LLM-readiness.
mcp-schema-lint
CLI linter for MCP tool/resource schemas
@toolrank/mcp-server
ToolRank MCP Server — Score and optimize MCP tool definitions for AI agent discovery. The first ATO (Agent Tool Optimization) tool.
create-mcp-tool
Create-mcp-tool package
@irsooti/mcp
A set of tools to work with ModelContextProtocol
Best For
- ✓MCP server developers building tool integrations
- ✓teams deploying MCP tools to production
- ✓developers integrating with Claude or other LLM clients via MCP
- ✓MCP tool developers optimizing for LLM usability
- ✓teams ensuring consistent parameter documentation across tool suites
- ✓developers building tools that will be called by Claude or other LLM agents
- ✓teams building large MCP tool suites with consistency requirements
- ✓developers optimizing tool discoverability for LLM clients
Known Limitations
- ⚠Only validates against MCP schema — does not test actual tool functionality or runtime behavior
- ⚠Cannot detect semantic issues like incorrect parameter types that are syntactically valid
- ⚠Limited to static analysis — cannot validate dynamic tool behavior or conditional logic
- ⚠Does not validate parameter values at runtime — only schema structure
- ⚠Cannot detect logical conflicts between parameters (e.g., mutually exclusive options)
- ⚠Limited to static schema analysis — cannot test parameter combinations
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.
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Static linter for MCP tool definitions — catch quality defects before deployment
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