@toolrank/mcp-server
MCP ServerFreeToolRank MCP Server — Score and optimize MCP tool definitions for AI agent discovery. The first ATO (Agent Tool Optimization) tool.
Capabilities6 decomposed
mcp tool definition scoring and ranking
Medium confidenceAnalyzes MCP tool definitions against a proprietary scoring framework to generate quantitative optimization scores. The system evaluates tool metadata, parameter schemas, descriptions, and integration patterns to produce ranked recommendations for improving tool discoverability by AI agents. Scoring likely incorporates factors like schema completeness, description clarity, parameter validation coverage, and semantic alignment with common agent use cases.
First purpose-built Agent Tool Optimization (ATO) system specifically designed for MCP ecosystems — introduces quantitative scoring methodology for tool discoverability rather than treating tool quality as subjective or implicit
Provides automated, standardized evaluation of MCP tools where alternatives require manual review or rely on implicit agent preference signals from usage patterns
mcp tool definition validation and schema analysis
Medium confidenceValidates MCP tool definitions against the MCP protocol specification and performs structural analysis of tool schemas. The system checks for schema completeness, parameter type correctness, required field presence, and semantic consistency. It likely uses JSON Schema validation combined with custom rules for MCP-specific patterns (e.g., tool naming conventions, description length thresholds, parameter cardinality constraints).
Combines MCP protocol-specific validation rules with JSON Schema validation in a single pipeline, providing both structural correctness and MCP ecosystem compliance checking
More comprehensive than generic JSON Schema validators because it understands MCP-specific constraints and patterns that generic validators cannot enforce
tool optimization recommendation generation
Medium confidenceGenerates prioritized, actionable recommendations for improving tool definitions based on scoring analysis. The system identifies specific gaps in tool metadata, schema design, or description quality and suggests concrete improvements. Recommendations are likely ranked by impact on agent discoverability and include examples or templates for implementing changes (e.g., 'expand description to 150+ characters', 'add enum constraints to parameter X').
Generates contextual, ranked recommendations based on tool-specific scoring gaps rather than applying generic best-practice checklists — treats optimization as a prioritization problem
More actionable than static documentation or style guides because recommendations are dynamically generated based on actual tool definition analysis and ranked by impact
mcp server protocol implementation and endpoint exposure
Medium confidenceImplements the MCP server protocol to expose tool scoring and optimization capabilities as MCP resources and tools. The server handles MCP protocol handshakes, message routing, and tool invocation via the standard MCP interface. It likely uses a framework like Node.js MCP SDK to manage protocol compliance, request/response serialization, and error handling. The server exposes scoring and recommendation generation as callable MCP tools that other agents or clients can discover and invoke.
Implements MCP server protocol natively rather than wrapping a REST API, enabling direct integration into MCP-native agent ecosystems and tool discovery workflows
Direct MCP integration eliminates translation layers and enables seamless tool discovery compared to REST-based alternatives that require adapter code
comparative tool ranking and benchmarking
Medium confidenceCompares multiple MCP tool definitions and produces ranked leaderboards or comparative analyses. The system scores a batch of tools and generates relative rankings, percentile positions, and peer comparison data. This enables tool developers to understand their tool's position within the broader MCP ecosystem and identify competitive gaps. Likely uses the same scoring algorithm as single-tool scoring but aggregates results for comparative analysis.
Provides ecosystem-level tool benchmarking specifically for MCP, enabling comparative analysis that was previously unavailable in fragmented tool ecosystems
Enables data-driven tool selection and optimization decisions where alternatives rely on subjective evaluation or implicit popularity signals
tool description and metadata quality analysis
Medium confidenceAnalyzes the quality and completeness of tool descriptions, names, and metadata fields. The system evaluates description length, clarity, keyword coverage, semantic relevance to tool functionality, and metadata field completeness. It likely uses NLP techniques (keyword extraction, semantic similarity) to assess whether descriptions accurately represent tool capabilities and whether metadata is sufficient for agent understanding. Produces quality scores and specific feedback on description improvements.
Applies NLP-based quality analysis to tool descriptions specifically for agent discoverability, not just general writing quality — evaluates semantic alignment with tool functionality
More sophisticated than static checklist-based validation because it uses semantic analysis to assess whether descriptions actually convey tool capabilities to agents
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 @toolrank/mcp-server, ranked by overlap. Discovered automatically through the match graph.
@kind-ling/twig
MCP tool description optimizer. Agents choose you or they don't. Twig makes them choose you.
@toolspec/core
MCP tool schema linting and quality scoring engine
SchemaFlow
** - Real-time PostgreSQL & Supabase database schema access for AI-IDEs via Model Context Protocol. Provides live database context through secure SSE connections with three powerful tools: get_schema, analyze_database, and check_schema_alignment. [SchemaFlow](https://schemaflow.dev)
mcp-tool-lint
Static linter for MCP tool definitions — catch quality defects before deployment
mcp-validate
Validate MCP server tool definitions against the spec. Checks names, descriptions, JSON Schema, parameter docs, and LLM-readiness.
git-mcp
Put an end to code hallucinations! GitMCP is a free, open-source, remote MCP server for any GitHub project
Best For
- ✓MCP tool developers optimizing for agent discovery
- ✓Teams building internal tool ecosystems for multi-agent systems
- ✓Tool marketplace operators ranking and recommending MCP tools
- ✓MCP tool developers building new tools
- ✓DevOps teams validating tool definitions in CI/CD pipelines
- ✓Tool ecosystem maintainers enforcing quality standards
- ✓Individual tool developers with limited MCP expertise
- ✓Teams building tool ecosystems and wanting to establish quality standards
Known Limitations
- ⚠Scoring algorithm and weighting factors are proprietary — no transparency into how scores are calculated
- ⚠Requires well-formed MCP tool definitions; malformed schemas may produce unreliable scores
- ⚠No historical scoring data or trend analysis — only point-in-time snapshots
- ⚠Scoring model may not account for domain-specific tool optimization needs
- ⚠Validation is schema-level only — cannot detect runtime behavior mismatches
- ⚠No semantic validation of tool functionality (e.g., whether a tool actually does what its description claims)
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
About
ToolRank MCP Server — Score and optimize MCP tool definitions for AI agent discovery. The first ATO (Agent Tool Optimization) tool.
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