DocuMCP is an intelligent MCP server that automates documentation deployment for open-source projects through comprehensive analysis and workflow automation.
Analyze repository structure, dependencies, and documentation needs with configurable depth levels
Recommend the best static site generator (Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy) based on data-driven project analysis
Generate configuration files for the selected SSG with project metadata
Create Diataxis-compliant documentation structure with proper categorization and optional examples
Set up GitHub Pages deployment workflows with automated GitHub Actions, branch and custom domain options
Verify and troubleshoot deployments by checking repository setup and expected URLs
Intelligently populate documentation with project-specific content at various complexity levels
Validate Diataxis documentation for accuracy, completeness, and compliance
Check general content quality including broken links, code syntax, and references
Detect documentation gaps by comparing repository content with existing documentation
Test local deployments before production with configurable ports and timeouts
Supports configuration generation and automated deployment setup for Docusaurus documentation sites with GitHub Pages integration.
Provides configuration and deployment workflow generation for Eleventy-based static documentation sites.
Provides repository analysis and automated workflow generation for GitHub repositories, enabling intelligent documentation deployment setup.
Generates automated CI/CD workflows for documentation deployment, with SSG-specific optimizations and deployment verification.
Automates the setup and deployment of documentation sites to GitHub Pages with intelligent static site generator integration.
Enables intelligent configuration and deployment setup for Hugo static sites as part of documentation deployment workflows.
Provides configuration generation and deployment setup for Jekyll-based documentation sites with GitHub Pages integration.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@documcp analyze my repository and deploy documentation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
DocuMCP is an intelligent Model Context Protocol (MCP) server that revolutionizes documentation deployment for open-source projects. It provides deep repository analysis, intelligent static site generator recommendations, and automated GitHub Pages deployment workflows.
DocuMCP analyzes your repository, recommends the perfect static site generator (Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy), creates professional documentation structure following Diataxis principles, and deploys it automatically to GitHub Pages. Just say "analyze my repository and deploy documentation" to get started.
Related MCP server: Synapse
🔍 Repository Analysis: Deep multi-layered analysis of project structure, dependencies, and documentation needs
🎯 SSG Recommendations: Data-driven recommendations for Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy
📚 Diataxis Framework: Automatic creation of well-structured documentation following proven principles
🚀 GitHub Pages Deployment: Automated workflow generation with SSG-specific optimizations
✅ Deployment Verification: Comprehensive checks and troubleshooting for successful deployments
🧠 Historical Intelligence: Learns from past deployments to improve recommendations
👤 User Preferences: Personalized recommendations based on your preferences and patterns
📊 Deployment Analytics: Comprehensive insights into deployment patterns and success rates
🎯 Smart Scoring: Intelligent SSG scoring based on success rates from similar projects
📈 Trend Analysis: Identifies deployment trends and provides health scores
📅 Freshness Tracking: Monitor documentation staleness with configurable thresholds
✅ Freshness Validation: Initialize and update freshness metadata automatically
🗺️ Sitemap Management: Generate, validate, and manage sitemap.xml for SEO
🔗 Knowledge Graph Integration: Track freshness history for intelligent recommendations
🤖 LLM Integration: Optional integration with DeepSeek, OpenAI, Anthropic, or Ollama
🔍 Semantic Code Analysis: Detect behavioral changes beyond syntax using AI
🧪 Example Validation: Simulate code execution to verify documentation examples
🎯 Intelligent Fallback: Graceful degradation to AST-only analysis when LLM unavailable
🔒 Privacy First: Works fully offline with AST analysis, LLM completely optional
Node.js: 20.0.0 or higher
npm: Latest stable version
Option A — npm global install (recommended):
npm install -g documcpOption B — build from source (contributors / local dev):
git clone https://github.com/tosin2013/documcp.git
cd documcp
npm install
npm run buildDocuMCP works with any MCP-enabled AI client. Pick the one you use:
File location:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json
{
"mcpServers": {
"documcp": {
"command": "npx",
"args": ["documcp"]
}
}
}Restart Claude Desktop to load the configuration.
# npm global install
claude mcp add documcp -- npx documcp
# local build (replace path as needed)
claude mcp add documcp -- node /path/to/documcp/dist/index.jsVerify with claude mcp list — no restart required.
File location: ~/.cursor/mcp.json (or Settings > MCP in the UI)
{
"mcpServers": {
"documcp": {
"command": "npx",
"args": ["documcp"]
}
}
}Add to settings.json (Cmd/Ctrl + Shift + P → Preferences: Open User Settings (JSON)):
{
"mcp.servers": {
"documcp": {
"command": "npx",
"args": ["documcp"]
}
}
}Ensure npx is available in your PATH
To find a global install path: npm list -g documcp
To use the full path instead of npx:
{
"command": "node",
"args": ["/usr/local/lib/node_modules/documcp/dist/index.js"]
}Once connected to your AI client, use natural language prompts:
Analyze my repository at /path/to/my-projectRecommend a static site generator based on my analysisSet up a Diataxis documentation structure using DocusaurusDeploy my documentation to GitHub PagesDocuMCP provides 30+ tools including repository analysis, intelligent SSG recommendations, content generation, deployment automation with tracking, validation, user preference management, deployment analytics, and memory-enhanced insights. See the complete documentation for detailed tool reference.
analyze_repository - Deep repository structure and dependency analysis
recommend_ssg - Intelligent SSG recommendations with historical data and user preferences
detect_gaps - Identify missing documentation sections
deploy_site - Automated deployment to GitHub Pages or Vercel with outcome tracking (deploy_pages still works as a back-compat alias)
verify_deployment - Comprehensive deployment validation
analyze_deployments - Analytics and insights from deployment history
manage_preferences - Manage user preferences for personalized recommendations
View historical success rates and deployment patterns
Get recommendations based on similar projects' success
# Run in development mode
npm run dev
# Run tests
npm test
# Lint code
npm run lint
# Type check
npm run typecheckDocuMCP follows a modular, stateless architecture:
TypeScript-based implementation using the official MCP SDK
Stateless operation for consistency and reliability
Modular design with clear separation of concerns
Progressive complexity allowing users to start simple
DocuMCP automatically creates documentation following the Diataxis framework:
Tutorials: Learning-oriented guides for newcomers
How-To Guides: Task-oriented recipes for specific goals
Reference: Information-oriented technical descriptions
Explanation: Understanding-oriented conceptual discussions
We welcome contributions! Please see our Contributing Guide for details.
Look for issues labeled "good first issue" to get started with the project. We welcome contributions from developers of all experience levels.
Please use our issue templates when reporting bugs or requesting features.
This project adheres to the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code.
Please see our Security Policy for reporting vulnerabilities and security-related issues.
MIT License - see LICENSE for details.
Built on the Model Context Protocol
Follows the Diataxis Framework
Inspired by the need for better documentation in open-source projects
Analyze deployment patterns and generate insights from historical deployment data
| Name | Required | Description | Default |
|---|---|---|---|
| analysisType | No | Type of analysis: full_report (comprehensive), ssg_stats (per-SSG), compare (compare SSGs), health (deployment health score), trends (temporal analysis) | full_report |
| ssg | No | SSG name for ssg_stats analysis | |
| ssgs | No | Array of SSG names for comparison | |
| periodDays | No | Period in days for trend analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits but only states a high-level purpose. It does not indicate read-only nature, required permissions, data dependencies, or side effects. The schema covers parameter details but not behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise but lacking structure. It does not front-load key differentiators or use cases. Some verbosity could be excised, but it is not overly long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, multiple analysis types) and no output schema, the description is insufficiently complete. It does not explain how analysis types differ, what insights are generated, or how it relates to sibling deployment tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions (e.g., analysisType enum values and meanings). The tool description adds no additional semantic value beyond the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'analyze' and resource 'deployment patterns' from 'historical data'. It distinguishes from sibling analysis tools focused on documentation (e.g., analyze_readme). However, the description remains generic and could more precisely specify the scope (SSG-specific, health, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like verify_deployment or deploy_site. There are no prerequisites, context, or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive README analysis with length assessment, structure evaluation, and optimization opportunities
| Name | Required | Description | Default |
|---|---|---|---|
| project_path | Yes | Path to the project directory containing README | |
| target_audience | No | Target audience for analysis | community_contributors |
| optimization_level | No | Level of optimization suggestions | moderate |
| max_length_target | No | Target maximum length in lines |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It describes the analysis but does not disclose behavioral traits such as read-only nature, required permissions, or side effects. The term 'comprehensive' lacks specific behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that conveys key capabilities without verbosity. It is front-loaded with 'comprehensive README analysis' and lists core features efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters (1 required), no output schema, and no annotations, the description is too brief. It does not explain the output format, return value, or what constitutes an 'analysis'. More detail is needed for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and parameters are well-documented in the schema (e.g., enum values, defaults). The description adds no additional parameter semantics beyond the schema, so baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it 'comprehensive README analysis' with specific aspects: length, structure, optimization. It distinguishes from siblings like 'check_documentation_links' and 'detect_documentation_gaps', though 'evaluate_readme_health' is very similar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'optimize_readme' or 'readme_best_practices'. The context of README analysis is implied but not compared to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze repository structure, dependencies, and documentation needs
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the repository to analyze | |
| depth | No | standard |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions analysis but does not disclose if the tool modifies anything, required permissions, or performance implications. The behavioral impact is unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is reasonably concise. However, it could be structured to highlight key aspects more effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 2 parameters, no output schema, and moderate complexity. The description fails to specify what the analysis produces, how depth levels differ, or any prerequisites. It is incomplete for understanding the tool's full capability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema describes path and depth with 50% schema coverage. The description does not add meaning beyond what the schema provides; it does not explain how depth affects analysis or what the path parameter expects. Overall, the description adds little value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes repositories, listing structure, dependencies, and documentation needs. It differentiates from siblings like analyze_readme and analyze_deployments by being more general, but does not explicitly distinguish itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as analyze_readme or analyze_deployments. The description lacks any context about prerequisites or appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Watch code changes and trigger documentation drift detection in near real-time.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | Action to perform | status |
| projectPath | Yes | Project root path | |
| docsPath | Yes | Documentation path | |
| watchPaths | No | Paths to watch (defaults to src/) | |
| excludePatterns | No | Glob patterns to exclude | |
| debounceMs | No | Debounce window for drift detection | |
| triggerOnCommit | No | Respond to git commit events | |
| triggerOnPR | No | Respond to PR/merge events | |
| webhookEndpoint | No | Webhook endpoint path (e.g., /hooks/documcp/change-watcher) | |
| webhookSecret | No | Shared secret for webhook signature validation | |
| port | No | Port for webhook server (default 8787) | |
| snapshotDir | No | Snapshot directory override | |
| reason | No | Reason for manual trigger | |
| files | No | Changed files (for manual trigger) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only gives a high-level purpose, omitting key behavioral traits such as statefulness (start/stop), side effects (webhook server, hook installation), or interaction with git events. The rich parameter schema hints at these, but the description does not elaborate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundancy. It is front-loaded with the core action. However, given the tool's complexity (14 parameters, multiple actions), the brevity sacrifices clarity; more detail would improve usability without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is insufficient for a tool with many parameters and no output schema or annotations. It fails to explain actions (start/stop/status/trigger/install_hook), configuration (debounce, webhook), or return values. The agent lacks critical context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema provides detailed parameter descriptions (e.g., action enum, defaults, min/max). The tool description does not add any parameter-level information, staying at a generic level. Per guidelines, baseline 3 is appropriate when schema covers parameters well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool's function: 'Watch code changes and trigger documentation drift detection in near real-time.' It uses a specific verb ('watch') and resource ('code changes'), and distinguishes it from sibling tools that focus on static analysis or one-time checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description does not mention any conditions, prerequisites, or scenarios for use, leaving the agent to infer context from the name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive link checking for documentation deployment with external, internal, and anchor link validation
| Name | Required | Description | Default |
|---|---|---|---|
| documentation_path | No | Path to the documentation directory to check | ./docs |
| check_external_links | No | Validate external URLs (slower but comprehensive) | |
| check_internal_links | No | Validate internal file references | |
| check_anchor_links | No | Validate anchor links within documents | |
| timeout_ms | No | Timeout for external link requests in milliseconds | |
| max_concurrent_checks | No | Maximum concurrent link checks | |
| allowed_domains | No | Whitelist of allowed external domains (empty = all allowed) | |
| ignore_patterns | No | URL patterns to ignore during checking | |
| fail_on_broken_links | No | Fail the check if broken links are found | |
| output_format | No | Output format for results | detailed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only hints at performance impact ('slower but comprehensive' for external links), but does not disclose network usage, file system access, or whether it modifies anything. This is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded and concise. No unnecessary words. However, it could be slightly expanded for clarity without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 10 parameters and no output schema or annotations, the description is too sparse. It does not explain the return format, error handling, or practical implications of settings like 'fail_on_broken_links'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents parameters well. The description adds framing by grouping link types under 'external, internal, and anchor', which maps to the boolean parameters, but does not add meaning for other parameters like 'timeout_ms' or 'output_format'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs 'comprehensive link checking for documentation deployment' and specifies the types of links validated (external, internal, anchor). This distinguishes it from sibling tools like 'validate_content' or 'validate_documentation_freshness', which focus on other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, context, or when not to use it, which is a gap given the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detect, classify, and clean up artifacts generated by AI coding agents (e.g., TODO.md, PLAN.md, agent markers, temporary files). Supports scan, clean, and archive operations with configurable patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the project directory to scan | |
| operation | Yes | Operation: scan (detect only), clean (remove), or archive (move to .agent-archive/) | |
| dryRun | No | Show what would be changed without making changes | |
| interactive | No | Prompt for confirmation (not supported in MCP, treated as dryRun) | |
| autoDeleteThreshold | No | Confidence threshold for automatic deletion (0-1) | |
| includeGitIgnored | No | Include artifacts that are already in .gitignore | |
| customPatterns | No | Custom patterns to detect in addition to defaults |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses operations (scan, clean, archive), dry-run capability, and that interactive mode is treated as dryRun in MCP. However, it does not mention whether clean is permanent or how scanning behaves, leaving minor gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of two sentences that front-load the core purpose with examples and then summarize supported operations. No unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description does not explain what the tool returns (e.g., for scan operation, whether it outputs a list of artifacts). This is a significant omission given the complexity of 7 parameters and operations that produce different results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters with descriptions. The description adds no per-parameter context beyond what the schema provides, achieving the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool detects, classifies, and cleans up artifacts from AI coding agents, with specific examples like TODO.md and PLAN.md. It also lists supported operations (scan, clean, archive) and configurable patterns, making it distinct from siblings like memory_cleanup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for managing agent artifacts but does not explicitly state when to use this tool versus alternatives, such as memory_cleanup or other cleanup tools. No exclusion criteria or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
[DEPRECATED — use deploy_site instead] Set up GitHub Pages deployment workflow. This alias will be removed in v1.1.0.
| Name | Required | Description | Default |
|---|---|---|---|
| repository | Yes | Repository path or URL | |
| ssg | Yes | ||
| branch | No | gh-pages | |
| customDomain | No | ||
| projectPath | No | Local path to the project for tracking | |
| projectName | No | Project name for tracking | |
| analysisId | No | ID from repository analysis for linking | |
| userId | No | User ID for preference tracking | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It only states the setup action without mentioning side effects, permissions, or configuration changes, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a clear deprecation notice, front-loading the key information. It wastes no words but omits necessary operational details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no output schema), the description is grossly incomplete. It fails to explain the setup process, required inputs, or expected outcomes, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 63%, but the description adds no parameter meaning beyond what the schema already provides. It does not explain the role of any parameter or how they affect behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sets up a GitHub Pages deployment workflow and explicitly marks it as deprecated, directing to deploy_site. The verb 'Set up' is slightly vague but combined with the resource is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'DEPRECATED — use deploy_site instead' and warns of removal in v1.1.0, providing clear when-not-to-use guidance and an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set up deployment workflow for GitHub Pages or Vercel with deployment tracking and preference learning. Use the 'target' parameter to choose one deployment platform per project (github-pages or vercel). If you switch targets, the tool will warn you to remove the old workflow file.
| Name | Required | Description | Default |
|---|---|---|---|
| repository | Yes | Repository path or URL | |
| ssg | No | Static site generator to use. If not provided, will be retrieved from knowledge graph using analysisId | |
| target | No | Deployment target: 'github-pages' (default) or 'vercel'. Vercel generates vercel.json and a Vercel CLI workflow. | github-pages |
| branch | No | gh-pages | |
| customDomain | No | ||
| invokeCliCommand | No | When true and target=vercel, includes the vercel deploy --prod CLI command in next steps | |
| projectPath | No | Local path to the project for tracking | |
| projectName | No | Project name for tracking | |
| analysisId | No | ID from repository analysis for linking | |
| userId | No | User ID for preference tracking | default |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions that switching targets will trigger a warning to remove old workflow files and implies preference learning. However, it does not detail side effects (e.g., file modifications, commits) or prerequisites like repository analysis, which limits full behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two sentences with no wasted words. It front-loads the purpose and critical usage note, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main function and a key constraint but omits details like the need for prior repository analysis (analysisId), behavior of preference learning, and what the tool outputs. Given no output schema, these gaps reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 80% schema description coverage, most parameters are explained in the schema. The description adds value only by elaborating on 'target' usage. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sets up deployment workflows for GitHub Pages or Vercel, with tracking and preference learning. It specifies the resource (deployment workflow) and actions (setup, track, learn). This distinguishes it from sibling tools like deploy_pages, which likely handles only the deployment step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the use of the 'target' parameter and warns about switching targets. However, it does not provide guidance on when to use this tool versus alternatives like deploy_pages or verify_deployment, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze repository and existing documentation to identify missing content and gaps
| Name | Required | Description | Default |
|---|---|---|---|
| repositoryPath | Yes | Path to the repository to analyze | |
| documentationPath | No | Path to existing documentation (if any) | |
| analysisId | No | Optional existing analysis ID to reuse | |
| depth | No | standard |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral transparency. It does not disclose whether the tool is read-only, modifies any state, requires authentication, or has side effects. The term 'analyze' suggests a read operation, but this is implicit and not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence of 10 words with no redundancy. Every word is necessary, making it highly concise and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, and the description does not explain what the agent can expect as a result (e.g., a list of gaps, a report). Key behavioral details like return format or impact of the 'depth' parameter are missing, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters have descriptions in the schema, achieving high coverage (75% or more). The description adds no additional information beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to analyze a repository and documentation to identify missing content and gaps. The verb 'analyze' and resource 'repository and existing documentation' are specific, though it does not explicitly differentiate from sibling tools like 'validate_documentation_freshness' or 'check_documentation_links'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor are any exclusions or prerequisites mentioned. The description only states what the tool does, leaving the agent to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Evaluate README files for community health, accessibility, and onboarding effectiveness
| Name | Required | Description | Default |
|---|---|---|---|
| readme_path | Yes | Path to the README file to evaluate | |
| project_type | No | Type of project for tailored evaluation | community_library |
| repository_path | No | Optional path to repository for additional context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only says 'evaluate,' implying a read operation, but doesn't disclose whether it makes external calls, writes data, or requires permissions. No mention of idempotency or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single 12-word sentence, highly concise. Could benefit from structure (e.g., bullet points), but the brevity is appropriate for a simple purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks information about return values, evaluation methodology, and how it differs from similar tools. With no output schema and no annotations, the description is too sparse for an agent to use confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema documents all parameters. The description adds no additional meaning beyond what is already present in the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool evaluates README files for three specific dimensions: community health, accessibility, and onboarding effectiveness. This distinguishes it from sibling tools like analyze_readme (general analysis) and optimize_readme (improvement suggestions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. With siblings like analyze_readme and readme_best_practices, the agent gets no hints about differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate configuration files for the selected static site generator
| Name | Required | Description | Default |
|---|---|---|---|
| ssg | Yes | ||
| projectName | Yes | ||
| projectDescription | No | ||
| outputPath | Yes | Where to generate config files |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits like side effects (e.g., file creation, overwriting existing configs), permissions, or rate limits. It only states the action without any behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise. However, it could be slightly expanded to include key parameter guidance without losing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four parameters and no output schema, the description is incomplete. It does not clarify the generated config's structure, naming conventions, or behavior for partial inputs. The description lacks enough information for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning beyond the schema; it does not explain parameters like 'ssg' enum, 'projectName', or 'outputPath'. Schema description coverage is only 25%, and the description fails to compensate for the missing parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates configuration files for a static site generator, using a specific verb and resource. It distinguishes itself from sibling tools like deploy_site or analyze_readme by focusing on config generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as when a user needs to generate configs vs. deploying a site. Exclusions or prerequisites are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate context-aware documentation using AST analysis and knowledge graph insights (Phase 3)
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | Path to the source code file to document | |
| documentationType | No | Type of Diataxis documentation to generate | reference |
| includeExamples | No | Include code examples in generated documentation | |
| style | No | Documentation detail level | detailed |
| outputFormat | No | Output format for generated content | markdown |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It mentions using AST analysis and knowledge graph insights, but does not clarify if the tool modifies state, requires authorization, has performance implications, or what the output format or behavior is. Lacks necessary disclosure for a complex tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that conveys the core purpose. The phrase '(Phase 3)' is unnecessary and unclear, slightly reducing conciseness. Otherwise well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, no annotations, and 5 parameters with defaults. The description does not explain return values, prerequisites (e.g., need for populated knowledge graph), or how it relates to other documentation tools. Incomplete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The tool description adds minimal meaning beyond the schema, such as the context-aware aspect. Baseline 3 is appropriate since the schema already describes parameter details adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb 'Generate', the resource 'context-aware documentation', and the method 'AST analysis and knowledge graph insights'. However, it does not distinguish from sibling tools like 'populate_diataxis_content' or 'update_existing_documentation', and the mention of 'Phase 3' is ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, nor any conditions or prerequisites. The description merely states what it does without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a comprehensive LLM context reference file documenting all tools, memory system, and workflows for easy @ reference
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Path to the project root directory where LLM_CONTEXT.md will be generated | |
| includeExamples | No | Include usage examples for tools | |
| format | No | Level of detail in the generated context | detailed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose whether the tool overwrites existing files, side effects, permissions needed, or anything about the generated file's location or content. The schema parameter description hints at LLM_CONTEXT.md but the tool description itself is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that conveys the core purpose without unnecessary words. It could be more structured but remains concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain the output format or content. It only says 'comprehensive LLM context reference file', lacking details on what that includes. This is insufficient for a tool generating a file.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents parameters. The tool description adds no extra meaning beyond the schema, meeting the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a comprehensive LLM context reference file documenting all tools, memory system, and workflows. The verb 'generate' and resource are specific, and it distinguishes from sibling tools like generate_config and generate_contextual_content which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or specific scenarios. Given the many sibling tools, this is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate standardized README templates for different project types with best practices
| Name | Required | Description | Default |
|---|---|---|---|
| projectName | Yes | Name of the project | |
| description | Yes | Brief description of what the project does | |
| templateType | Yes | Type of project template to generate | |
| author | No | Project author/organization name | |
| license | No | Project license | MIT |
| includeScreenshots | No | Include screenshot placeholders for applications | |
| includeBadges | No | Include status badges | |
| includeContributing | No | Include contributing section | |
| outputPath | No | Path to write the generated README.md file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'generate' but does not mention if it overwrites files, requires permissions, or has side effects. The outputPath parameter implies file writing, but this isn't clarified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, 10 words, concise and front-loaded. Could be slightly more informative, but no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the output (e.g., returns content or writes file). For a generation tool with 9 parameters, it lacks context about the produced template and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so description adds no new meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates standardized README templates for different project types with best practices. It distinguishes from sibling analysis tools like 'evaluate_readme_health' or 'optimize_readme'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'readme_best_practices' or 'validate_readme_checklist'. The description does not specify prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Aggregate cross-project signals from the Knowledge Graph and surface community-level insights: deployment success rates per SSG, common technology stacks, frequent drift sources, and project health distribution. All data is anonymized — no raw project paths are exposed.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits. It mentions anonymization and no exposure of raw paths, which is good. But it lacks details on data freshness, latency, or any limitations that might affect agent decisions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with three sentences that each add value: the first explains the action and outputs, the second emphasizes anonymization. It is front-loaded with the verb 'Aggregate' and free of fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and no annotations, the description covers key aspects: what it does, what it returns, and a privacy note. It could be considered complete for a simple read-only tool, though additional context on data source freshness would improve it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the input schema provides no information. The description adds value by explaining the output data, but since there are no parameters, a baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool aggregates cross-project signals from the Knowledge Graph and lists specific outputs like deployment success rates, technology stacks, drift sources, and health distribution. It distinguishes itself from siblings that operate on individual repositories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly suggests usage for obtaining community-level insights with anonymized data. However, it does not explicitly state when not to use it or name alternatives, though the context makes it clear this is for aggregated data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Manage user preferences for documentation generation and SSG recommendations
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform on preferences | |
| userId | No | User ID for multi-user setups | default |
| preferences | No | Preference updates (for update action) | |
| json | No | JSON string for import action |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It does not mention side effects (e.g., mutation from update/reset/import), required permissions, idempotency, or whether operations are reversibly. The single sentence is insufficient for a tool that can modify state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that front-loads the core domain. It is appropriately concise, though a slightly more expanded form could improve clarity without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, nested object, multiple actions, no output schema), the description is too sparse. It fails to explain the purpose of each action, the default userId, how preferences affect recommendations, or what responses look like, leaving significant gaps for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters have inline documentation. The global description adds no extra meaning beyond what the schema already provides, meriting the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Manage user preferences for documentation generation and SSG recommendations' uses the vague verb 'manage' and does not specify the supported actions (CRUD, import/export). It conveys the domain but lacks a specific action-resource pairing, failing to distinguish from numerous sibling tools that might also deal with preferences or recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like recommend_ssg, memory_* tools, or other managers. No when-to-use, when-not-to-use, or exclusion criteria are present, leaving the agent to infer usage purely from the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate, validate, and manage sitemap.xml as the source of truth for documentation links. Sitemap.xml is used for SEO, search engine submission, and deployment tracking.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform: generate (create new), validate (check structure), update (sync with docs), list (show all URLs) | |
| docsPath | Yes | Path to documentation root directory | |
| baseUrl | No | Base URL for the site (e.g., https://user.github.io/repo). Required for generate/update actions. | |
| includePatterns | No | File patterns to include (default: **/*.md, **/*.html, **/*.mdx) | |
| excludePatterns | No | File patterns to exclude (default: node_modules, .git, dist, build, .documcp) | |
| updateFrequency | No | Default change frequency for pages | |
| useGitHistory | No | Use git history for last modified dates (default: true) | |
| sitemapPath | No | Custom path for sitemap.xml (default: docsPath/sitemap.xml) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It only states the tool manages sitemap but doesn't describe side effects, required permissions, or behavior on errors (e.g., what happens during validation). For a tool that likely modifies files, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and includes relevant context (SEO, deployment). No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 8 parameters, no output schema, and no annotations, the description is too brief. It doesn't explain return values, error handling, or how the sitemap is used in deployment tracking. An agent would lack context for proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already explains each parameter. The description adds no additional meaning beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating, validating, and managing sitemap.xml as the source of truth for documentation links. It also mentions SEO and deployment tracking, providing context. This differentiates it from sibling tools like check_documentation_links or sync_code_to_docs, which have different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks explicit guidance on when to use this tool versus alternatives. It doesn't mention prerequisites or disclaimers about when not to use it. Sibling tools exist for related tasks (e.g., check_documentation_links), but no comparisons are made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Manage multi-agent memory sharing and collaboration
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform | |
| agentInfo | No | ||
| memoryId | No | Memory ID for sharing operations | |
| targetAgent | No | Target agent for sync operations | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavior. It only says 'manage' without indicating side effects, read/write nature, or required permissions. The action enum hints at both reads (get_insights, network_status) and writes (register_agent, share_memory), but the description does not clarify this.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence, 5 words). While concise, it sacrifices informativeness. A longer description that front-loads key details would be more helpful without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema, annotations, and low parameter coverage, the description is insufficient. It does not explain return values, error handling, or the purpose of each action. The tool has 5 parameters and 5 actions, requiring more complete documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 60% description coverage (3 of 5 parameters described), but the description adds no additional meaning. Parameters like agentInfo and options have no schema description and are not clarified in the description. The enum values for action are not explained beyond 'Action to perform'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Manage multi-agent memory sharing and collaboration' gives a general sense but is vague. It does not specify the specific actions or how it differs from sibling tools like memory_cleanup or memory_recall. A more precise description tying to the action enum would improve clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs. alternatives. The description does not mention prerequisites, use cases, or conditions. The agent is left to infer usage from the action enum alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Clean up old memories
| Name | Required | Description | Default |
|---|---|---|---|
| daysToKeep | No | Number of days of memories to keep | |
| dryRun | No | Preview what would be deleted without actually deleting |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only says 'clean up old memories'. It doesn't mention destructiveness, reversibility, or the dryRun preview option. Fails to inform agent about safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, efficient, but could be structured to include more specifics. Front-loaded with verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given many sibling memory tools and no output schema, the description is too brief. Lacks context about what memories are affected and the effects of cleanup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good parameter descriptions. Description adds no extra meaning, but baseline is 3. No contradiction.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Clean up old memories' clearly states a verb and resource, but 'memories' is ambiguous without context. It distinguishes from siblings like memory_pruning by being a general cleanup, but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like memory_pruning or memory_migration. Absence of when-not or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform contextual memory retrieval with intelligent ranking
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| context | Yes | ||
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. However, it does not describe any behavioral traits such as side effects, permission needs, or what happens during retrieval. The agent cannot infer safety or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise. However, it is too brief to be useful, lacking critical details. Every word should earn its place; here, 'intelligent ranking' adds little without explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, no output schema, and many siblings), the description is woefully incomplete. It does not explain 'contextual' retrieval, ranking algorithm, return format, or how to handle errors. The agent lacks essential information to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33% (only 'query' has a description). The tool description adds no parameter information beyond the schema. For 3 parameters including two empty objects ('context' and 'options'), the description should clarify their roles, but it does not. This is insufficient for an agent to correctly populate parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb 'perform' and resource 'contextual memory retrieval' with 'intelligent ranking,' which is clear but vague. It does not differentiate from sibling tools like 'memory_recall,' 'memory_similar,' or 'memory_knowledge_graph,' which all involve memory retrieval. The purpose is adequate but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks information about prerequisites, context requirements, or exclusions. Given the large set of memory-related siblings, this is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get enhanced recommendations using learning and knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Path to the project | |
| baseRecommendation | Yes | Base recommendation to enhance | |
| projectFeatures | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It mentions 'using learning' but does not clarify if this tool modifies state (e.g., learns from recommendations) or is read-only. The term 'get' suggests read-only, but 'learning' implies potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence (9 words). However, conciseness comes at the cost of missing critical details. The structure is adequate but not informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three required parameters (including two empty objects), no output schema, and a complex domain (learning and knowledge graph), the description is severely incomplete. It does not explain the return value, prerequisites, or how the enhancement works, leaving the agent unable to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 67% description coverage (projectPath and baseRecommendation described, projectFeatures missing). The description does not add any parameter semantics beyond the schema. It fails to clarify what 'projectFeatures' should contain or how parameters interact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool gets enhanced recommendations using learning and knowledge graph, but it is vague. It does not specify what 'enhanced' means or differentiate from sibling tools like memory_knowledge_graph or memory_recall that also use knowledge graphs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description lacks any context about prerequisites, such as whether memory must be populated first, or scenarios where this tool is preferred over other memory tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export memories to JSON or CSV
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Export format | json |
| filter | No | Filter memories to export |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior. It only states 'Export memories' without explaining whether the tool returns data, saves to a file, or has side effects. The absence of output schema or behavioral details makes it unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant words. It is concise, though it could benefit from more detail without losing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is incomplete. It does not explain return values, the behavior of the filter, or any limitations compared to the more advanced sibling 'memory_export_advanced'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for both parameters. The description adds nothing beyond the schema, as it merely echoes the format parameter. The 'filter' parameter is an empty object with no properties, and the description does not clarify its intended use, leaving ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Export', the resource 'memories', and the target formats 'JSON or CSV'. It is specific and distinguishes the tool from unrelated siblings, though it does not differentiate from the similar sibling 'memory_export_advanced'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'memory_export_advanced' or 'memory_recall'. The description lacks context about prerequisites or ideal use cases, leaving the agent to guess.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Advanced memory export with multiple formats and options
| Name | Required | Description | Default |
|---|---|---|---|
| outputPath | Yes | Output file path | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the tool is read-only, modifies memory, supports specific formats, or has side effects. The description is too minimal to inform an agent about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it sacrifices completeness and clarity. It is front-loaded but lacks detail. Every word earns its place, but the overall informativeness is low.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a nested object parameter and no output schema, the description is incomplete. It does not explain what formats are supported, how options work, or what the output looks like. The complexity demands more explanation than provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (outputPath described, options not). The description mentions 'multiple formats and options' but does not explain the 'options' object's properties or valid values. It adds no meaningful detail beyond the schema for the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Advanced memory export with multiple formats and options', which indicates the tool performs an export operation. However, 'advanced' is vague and does not specify what distinguishes it from the simpler 'memory_export' sibling. The purpose is clear but lacks specificity and differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'memory_export'. There is no mention of prerequisites, context, or exclusions. The description fails to help an agent decide when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Advanced memory import with validation and conflict resolution
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Input file path | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description mentions validation and conflict resolution but does not detail side effects, authorization requirements, or specific behaviors, leaving agents uninformed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise and front-loaded. However, it could be expanded with key details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations, output schema, and limited parameter documentation, the description is insufficient for an agent to correctly use this complex import tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%; only inputPath has a description. The tool description adds no additional meaning to parameters, especially the undocumented 'options' object.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Advanced memory import with validation and conflict resolution', indicating a verb and resource. However, it is vague and does not differentiate from potential simpler import tools not listed among siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description lacks context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get insights and patterns from memory
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | No | Project ID to analyze | |
| timeRange | No | Time range for analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only says 'Get', which implies read-only, but does not disclose whether the tool has side effects, rate limits, authentication needs, or how it accesses memory. The description is insufficient for an agent to reason about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no excess, but it is borderline tautological (restating the tool name). It is concise but at the cost of completeness. Every word is earned, but the sentence could provide more useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of many sibling memory tools and the complexity of the input (nested timeRange object with no schema), the description is incomplete. It does not explain what insights are returned, how to interpret them, or prerequisites. No output schema is provided, increasing the need for description details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% as both parameters (projectId, timeRange) have descriptions in the schema. The tool description adds no extra meaning beyond those schema descriptions. The timeRange object is empty with no defined properties, but the description does not clarify its expected structure. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get insights and patterns from memory' states a verb and resource, but is vague and does not differentiate from sibling tools like memory_intelligent_analysis, memory_temporal_analysis, or memory_learning_stats, which could also produce insights. It lacks specificity on what kind of insights or patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No when-to-use, when-not-to-use, or prerequisite context is provided. The description simply states what it does, leaving the agent to guess optimal usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get intelligent analysis with patterns, predictions, and recommendations
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Path to the project for analysis | |
| baseAnalysis | Yes | Base analysis data to enhance |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description does not disclose behavioral traits such as side effects, permissions, or state changes. It only states it 'gets' analysis.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks sufficient detail to be useful. It is not excessively long, but it sacrifices informative content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of a nested object parameter and no output schema, the description is insufficient. It does not explain return values, how to use the baseAnalysis parameter, or any important details about the analysis.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage, but the parameter 'baseAnalysis' is described as 'Base analysis data to enhance' while its schema defines it as an empty object (no properties, additionalProperties false), creating a contradiction. The tool description does not clarify this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it provides 'intelligent analysis with patterns, predictions, and recommendations,' but it is vague about the specific domain or resource. It does not differentiate from sibling tools like 'memory_insights' or 'memory_temporal_analysis'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description lacks context for appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query the knowledge graph for relationships and paths
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full responsibility for behavioral disclosure. It only indicates a query operation but omits details like idempotency, performance implications, or access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise but under-informative. It front-loads the purpose but does not structure additional details, making it less useful than a slightly longer but more comprehensive description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having a nested object parameter and no output schema or annotations, the description provides almost no additional context. It leaves the agent guessing about the query format, return values, and usage constraints, making it highly incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema defines a single opaque 'query' object with no properties, and the description adds minimal context ('for relationships and paths'). Schema coverage is 0%, so the description fails to meaningfully compensate by explaining the query structure or required fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it queries a knowledge graph for relationships and paths, distinguishing it from sibling tools like memory_recall or memory_similar which target other memory aspects. However, it lacks specificity about the type of relationships or paths.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives. The description does not mention prerequisites, when-not-to-use, or potential use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive learning and knowledge graph statistics
| Name | Required | Description | Default |
|---|---|---|---|
| includeDetails | No | Include detailed statistics |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It indicates a read operation ('Get') but omits details like performance impact, data freshness, authentication needs, or what 'comprehensive' entails beyond the parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It efficiently communicates the tool's purpose without extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple structure (one optional param, no output schema) and the presence of many related memory tools, the description is insufficiently informative. It does not explain what statistics are included or how they differ from similar tools, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter 'includeDetails' is described adequately in the schema. The description adds no extra meaning beyond the schema, so it meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves 'comprehensive learning and knowledge graph statistics', which identifies a specific resource and action. However, it does not differentiate from siblings like 'memory_knowledge_graph' or 'memory_insights', leaving ambiguity about the exact scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The agent receives no context about prerequisites, typical use cases, or exclusions, forcing reliance on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create and execute migration plans between different memory systems
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | create_plan | |
| sourcePath | No | Source data path | |
| migrationPlan | No | ||
| sourceSchema | No | Source system schema | |
| targetSchema | No | Target system schema | |
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully convey behavioral traits. It only states the high-level action without disclosing side effects, destructive potential, or execution details. The tool performs three distinct actions (create_plan, execute_migration, validate_compatibility) but the description lumps them together without distinction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, making it concise. However, it lacks structure; it does not separate the three distinct actions or provide a hierarchical breakdown. While front-loaded with the main verb, crucial details are omitted, making it less effective than a more structured brief.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (three actions, nested objects, no output schema), the description is insufficient. It does not explain the workflow, expected inputs for each action, or return values. An agent would need to infer behavior from the action enum alone, which is risky.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Of 6 parameters, only sourceSchema and targetSchema have descriptions in the schema (50% coverage). The description adds no new information about parameters, such as what 'action' values mean or how 'migrationPlan' is structured. It fails to compensate for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: creating and executing migration plans between memory systems. The verb 'Create and execute' and resource 'migration plans' are specific, and the context 'between different memory systems' adds clarity. However, it does not explicitly distinguish from sibling tools like memory_export or memory_import_advanced, which may involve data movement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool over alternatives (e.g., memory_export for one-time transfers). There is no mention of prerequisites, typical scenarios, or prohibitions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive optimization metrics and recommendations
| Name | Required | Description | Default |
|---|---|---|---|
| includeRecommendations | No | ||
| timeRange | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose side effects, authentication needs, or rate limits. It only states 'get', implying a read operation, but provides no details on whether it modifies state, requires admin rights, or has limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence (5 words), which is concise but too brief to convey necessary details. While not verbose, it sacrifices clarity for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having two parameters (one with nested object), no output schema, and over 30 sibling memory-related tools, the description provides no differentiation, parameter explanation, or expected output. It is severely incomplete for informed tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not mention the two parameters (includeRecommendations, timeRange). The boolean and nested object parameters are left undefined, forcing the agent to guess their meaning and valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get comprehensive optimization metrics and recommendations' indicates it retrieves metrics and suggestions, but does not specify what aspect (e.g., memory usage, performance) or how comprehensive it is. Sibling tools like memory_insights and memory_learning_stats suggest similar functionality, causing ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as memory_insights or memory_cleanup. The description lacks context on prerequisites, input conditions, or situations where this tool is preferred or avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Intelligent memory pruning and optimization
| Name | Required | Description | Default |
|---|---|---|---|
| policy | No | ||
| dryRun | No | Preview pruning without executing |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'pruning' which implies deletion or removal, but does not disclose the precise nature of the operation, side effects, permissions needed, or whether it is destructive. Annotations are absent, so the description carries the full burden and fails to provide adequate behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it lacks specific information. The word 'intelligent' is a filler that does not add clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a complex input (policy object) and a dry-run feature, yet the description does not explain what the policy controls, the effect of pruning, or the return value. Without an output schema, the description should be more complete to guide usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has two parameters: 'policy' (an empty object with no description) and 'dryRun' (described). The description does not explain the 'policy' parameter, which is an opaque object. With 50% schema description coverage, the description does not compensate for the missing parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Intelligent memory pruning and optimization' suggests a tool for pruning memory, but it is vague and does not specify what exactly is pruned or how it optimizes. It does not distinguish from siblings like 'memory_cleanup' or 'memory_optimization_metrics' which have similar themes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool over alternatives, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recall memories about a project or topic
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query or project ID | |
| type | No | Type of memory to recall | |
| limit | No | Maximum number of memories to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description only states the basic purpose without disclosing behavioral traits (e.g., read-only, side effects, or rate limits). For a read-like tool, this minimal info is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise and front-loaded, but could be slightly more informative without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 3 parameters, the description is too sparse. It fails to explain what 'memories' are, return format, or how it differs from siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so schema already describes parameters. Description adds no extra semantics, meeting baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Recall memories about a project or topic' clearly states the verb and resource, but lacks differentiation from many sibling memory tools like memory_contextual_search or memory_similar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternative memory tools. No context on prerequisites or appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find similar projects from memory
| Name | Required | Description | Default |
|---|---|---|---|
| analysisId | Yes | Analysis ID to find similar projects for | |
| limit | No | Maximum number of similar projects |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must convey behavioral traits. It only states a read-like operation but does not confirm whether it is read-only, safe, or if it requires specific permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundancy. Every word is informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no output schema, the description covers the basic purpose but omits return format and how results are ranked, leaving the agent with some uncertainty.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds no extra meaning beyond parameter names and types. It does not explain what 'similar' means or how analysisId is interpreted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Find similar projects from memory' uses a clear verb and resource, but does not differentiate from sibling tools like memory_enhanced_recommendation or memory_contextual_search, which may perform similar tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool over alternatives. Given the presence of many memory-related sibling tools, explicit usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze temporal patterns and trends in memory data
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| analysisType | No | patterns |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavioral traits. It only states a vague analytical purpose, omitting whether the tool is read-only, what side effects exist, or any output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise and front-loaded. However, it sacrifices necessary detail for brevity, making it insufficient for understanding tool functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (0 required params, 1 enum, nested object, many sibling tools, no output schema), the description is woefully incomplete. It fails to explain return values, behavior, or how to effectively use the analysisType enum.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% parameter description coverage. The description adds no meaning to the parameters; the 'query' object is defined with empty properties, and 'analysisType' enum values are not explained. The agent gains no insight into how to use these parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Analyze temporal patterns and trends in memory data' clearly states the verb and resource, but fails to distinguish from sibling tools like memory_insights or memory_intelligent_analysis, which likely have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of contexts, prerequisites, or exclusions, leaving the agent to infer usage without support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate visual representations of memory data
| Name | Required | Description | Default |
|---|---|---|---|
| visualizationType | No | dashboard | |
| options | No | ||
| customVisualization | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It only says 'generate visual representations' without specifying output format, side effects, or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, highly concise. However, it is too brief to convey necessary details, sacrificing value for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters, nested objects, and no output schema, the description is incomplete. It lacks context on return values, supported visualizations, and integration with other memory tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description offers no information about parameters. Schema coverage is 0%, and the description does not mention visualizationType options or the role of options and customVisualization.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool generates visual representations of memory data, which is a clear verb+resource. It distinguishes from sibling tools focused on analysis, export, or search, but the term 'memory data' is vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like memory_insights or memory_knowledge_graph. The description provides no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Optimize README content by restructuring, condensing, and extracting detailed sections to separate documentation
| Name | Required | Description | Default |
|---|---|---|---|
| readme_path | Yes | Path to the README file to optimize | |
| strategy | No | Optimization strategy | community_focused |
| max_length | No | Target maximum length in lines | |
| include_tldr | No | Generate and include TL;DR section | |
| preserve_existing | No | Preserve existing content structure where possible | |
| output_path | No | Path to write optimized README (if not specified, returns content only) | |
| create_docs_directory | No | Create docs/ directory for extracted content |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose side effects like file writes or directory creation, which are implied by schema parameters. Minimal behavioral context beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise and front-loaded. However, could be slightly more informative without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, and description does not explain return format or behavior when output_path is not set. With 7 parameters, the description is too brief.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description does not add extra meaning to parameters like strategy or max_length beyond schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool optimizes README content through restructuring, condensing, and extracting sections. It distinguishes from siblings like analyze_readme or generate_readme_template.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like validate_readme_checklist or analyze_readme. Lacks context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Intelligently populate Diataxis documentation with project-specific content
| Name | Required | Description | Default |
|---|---|---|---|
| analysisId | Yes | Repository analysis ID from analyze_repository tool | |
| docsPath | Yes | Path to documentation directory | |
| populationLevel | No | comprehensive | |
| includeProjectSpecific | No | ||
| preserveExisting | No | ||
| technologyFocus | No | Specific technologies to emphasize |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits, but it only states 'intelligently populate'. No mention of side effects, idempotency, or whether existing content is overwritten despite the presence of a 'preserveExisting' parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single vague sentence. It is concise but under-specified, lacking structure or front-loaded key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters, required inputs, no output schema, and no annotations, the description is insufficient. It does not explain what the tool returns or how its behavior varies by parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, but the description adds no parameter details. It does not clarify the meaning or usage of parameters like 'populationLevel', 'includeProjectSpecific', or 'technologyFocus' beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool populates Diataxis documentation with project-specific content, but it lacks specificity about what 'populate' entails and does not distinguish from sibling tools like 'generate_contextual_content' or 'sync_code_to_docs'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, no prerequisites or context provided. The single sentence offers no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List files and directories within allowed roots. Use this to discover files without requiring full absolute paths from the user.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to directory (relative to root or absolute within root) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses that the tool works 'within allowed roots,' implying access restrictions. However, it does not describe the return format, error handling, or any side effects. For a read-only list tool, this is adequate but not exceptional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences. The first states the primary function, the second provides usage guidance. No unnecessary words. Front-loaded effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single parameter and no output schema, the description covers the main purpose and usage context. It does not detail the return structure, but the tool is straightforward. The many sibling tools are mostly unrelated, so differentiation is not critical here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already has a description for the 'path' parameter: 'Path to directory (relative to root or absolute within root).' The description adds nothing new beyond implying usability without absolute paths. Since schema description coverage is 100%, the baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool lists files and directories within allowed roots. The verb 'List' and resource 'files and directories' are specific. It distinguishes from siblings by highlighting the benefit of not requiring absolute paths, which is unique among the many sibling tools that seem to focus on analysis or memory operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit context: 'Use this to discover files without requiring full absolute paths from the user.' This tells the agent when to use the tool. However, it does not mention when not to use it or provide alternatives, which would improve the score further.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze README files against best practices checklist and generate templates for improvement
| Name | Required | Description | Default |
|---|---|---|---|
| readme_path | Yes | Path to the README file to analyze | |
| project_type | No | Type of project for tailored analysis | library |
| generate_template | No | Generate README templates and community files | |
| output_directory | No | Directory to write generated templates and community files | |
| include_community_files | No | Generate community health files (CONTRIBUTING.md, CODE_OF_CONDUCT.md, etc.) | |
| target_audience | No | Target audience for recommendations | mixed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses the tool is non-destructive (analysis and generation), but no details on permissions, rate limits, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with key action, no filler. Could be more structured but is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 6 parameters and no output schema, the description only provides a high-level overview. It lacks details on what the analysis returns, how templates are generated, or workflow integration, making it incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add extra meaning beyond the schema parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes README files against a best practices checklist and generates templates, distinguishing it from siblings like 'analyze_readme' (analysis only) and 'generate_readme_template' (generation only).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies combined analysis and generation, but does not explicitly state when to use this tool over alternatives (e.g., for a full analysis+template workflow) or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend the best static site generator based on project analysis and user preferences
| Name | Required | Description | Default |
|---|---|---|---|
| analysisId | Yes | ID from previous repository analysis | |
| userId | No | User ID for personalized recommendations based on usage history | default |
| preferences | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks behavioral details such as whether the tool modifies state, requires authentication, has rate limits, or what kind of output (e.g., a name, list) it returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words, making it highly concise and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (3 parameters, nested object) and no output schema, the description is somewhat incomplete—it does not explain return value, error conditions, or how the recommendation integrates with other analysis tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has adequate descriptions for analysisId and userId, and the nested preferences object has enum clarifications. The description does not add substantial new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Recommend' and the resource 'best static site generator', with explicit basis ('based on project analysis and user preferences'). This distinguishes it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after project analysis, but does not explicitly state when to use this tool versus alternatives or provide when-not instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Record the host's judgment ('actionable' | 'noise' | 'deferred') for a previously-detected drift. Outcomes feed back into priority-scoring weights via the knowledge graph (ADR-012 Phase 4, Issue #114).
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Absolute path to the project root. Must match the path used during drift detection so the outcome lands on the right project node. | |
| driftId | Yes | The driftId returned alongside a PrioritizedDriftResult. Without this anchor the outcome can't be attributed to a specific factor snapshot. | |
| outcome | Yes | User judgment: 'actionable' (drift was real, doc update needed), 'noise' (false positive, dismiss), or 'deferred' (revisit later — neutral signal). | |
| notes | No | Optional free-form notes from the host (why the drift was actionable/noise, link to PR, etc.). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral transparency. It discloses a key side effect: outcomes feed back into priority-scoring weights via the knowledge graph. It also notes constraints on parameters (projectPath must match detection, driftId anchors outcome). However, it does not mention idempotency or behavior on duplicate driftId.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each earning its place: the first describes the primary action and valid outcomes, the second explains the downstream impact. No filler or redundancy, and the critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description reasonably covers purpose, side effects, and parameter constraints. It could mention what happens if driftId is not found (e.g., error) or if outcome is overwritten, but these are minor gaps for a simple record tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by explaining why projectPath must match (so outcome lands on correct project node) and why driftId is required (to attribute outcome to a factor snapshot). This contextual information goes beyond the schema's straightforward descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Record' and the resource 'host's judgment for a previously-detected drift'. It specifies the three outcome values, making the purpose unambiguous. The tool is distinct from all sibling tools listed, which cover analysis, deployment, memory, and documentation tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates that this tool is used after drift detection to record a judgment. It implies a prerequisite (previously-detected drift) but does not explicitly state when not to use it or provide alternative tools. Since no sibling performs this specific function, the lack of alternatives is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate Playwright E2E test setup for documentation site (containers + CI/CD)
| Name | Required | Description | Default |
|---|---|---|---|
| repositoryPath | Yes | Path to documentation repository | |
| ssg | Yes | ||
| projectName | Yes | Project name for tests | |
| mainBranch | No | main | |
| includeAccessibilityTests | No | ||
| includeDockerfile | No | ||
| includeGitHubActions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does not disclose side effects such as file creation or modifications, nor does it mention the need for authentication or permissions. The agent lacks information about what the tool actually does beyond generating files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence without wasted words, but it could benefit from a structured format (e.g., bullet points) for readability. It is appropriately sized for a summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters with defaults and no output schema, the description is too minimal. It does not explain what files are generated, the return value, or how to validate success. An agent would need to infer or test behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 29%, yet the description does not add details about parameter semantics. It hints at 'containers + CI/CD' but does not explicitly clarify that includeDockerfile and includeGitHubActions control those options. The description fails to compensate for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating Playwright E2E test setup for a documentation site, mentioning containers and CI/CD. However, it does not differentiate from siblings like setup_structure or generate_config, which could overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives such as setup_structure or generate_config. There is no mention of prerequisites or scenarios where this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create Diataxis-compliant documentation structure
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Root path for documentation | |
| ssg | Yes | ||
| includeExamples | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states the basic action without mentioning side effects like file creation, overwriting, or required permissions. Essential details for safe usage are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and concise, but it is under-informative. Conciseness should not sacrifice clarity; here, critical details are omitted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema, the description should explain what the function returns (e.g., created paths, success message). It does not, leaving the agent uninformed about the tool's full scope and effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low (33%). The description does not explain the role of parameters like 'ssg' or 'includeExamples', relying solely on the schema. Since the schema itself provides limited descriptions, the tool's behavior remains ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'Create' and specifies 'Diataxis-compliant documentation structure', which clearly identifies the tool's main function among siblings like 'populate_diataxis_content' and 'validate_diataxis_content'. However, it could more explicitly differentiate from creating content versus structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'populate_diataxis_content' or 'validate_diataxis_content'. The description lacks prerequisites or context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Automatically synchronize documentation with code changes using AST-based drift detection (Phase 3)
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Path to the project root directory | |
| docsPath | Yes | Path to the documentation directory | |
| mode | No | Sync mode: detect=analyze only, preview=show changes, apply=apply safe changes, auto=apply all | detect |
| autoApplyThreshold | No | Confidence threshold (0-1) for automatic application of changes | |
| createSnapshot | No | Create a snapshot before making changes (recommended) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The description states the tool 'automatically synchronize' but does not disclose behavioral traits such as destructiveness, permissions required, or rollback capabilities. The parameter schema hints at different modes (detect, preview, apply, auto) but the description does not explain their implications. Transparency is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the core action. However, it includes 'Phase 3' which is likely internal jargon and may not be meaningful to an AI agent. Otherwise, it is well-structured and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, no output schema, and many siblings, the description is too brief. It does not explain the modes, confidence threshold, or what happens after synchronization. An agent would need to infer critical details from the schema alone, which is insufficient for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond what the schema provides. Baseline 3 is correct.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: synchronizing documentation with code changes using AST-based drift detection. It specifies a verb (synchronize) and resource (documentation with code changes), and mentions a specific technique. However, it does not explicitly differentiate from siblings like 'detect_documentation_gaps' or 'update_existing_documentation', so a 4 is appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or when not to use. Given the large number of related sibling tools, this lack of guidance is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test documentation build and local server before deploying to GitHub Pages
| Name | Required | Description | Default |
|---|---|---|---|
| repositoryPath | Yes | Path to the repository | |
| ssg | Yes | ||
| port | No | Port for local server | |
| timeout | No | Timeout in seconds for build process | |
| skipBuild | No | Skip build step and only start server |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only says 'test' without explaining expected outcomes, side effects (e.g., file modifications, cleanup), or error handling. The tool's non-destructive nature is assumed but not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no waste. However, it may be too brief given the tool's complexity and number of parameters, missing some necessary context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is insufficient for a tool with 5 parameters, no output schema, and many sibling tools. It lacks details on return values, error scenarios, and when to choose this over alternatives. More context is needed for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 80% (4 of 5 params have descriptions), meeting the high-coverage baseline. The description adds no parameter-specific information, so it neither improves nor degrades the semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool tests documentation build and local server before deployment, with a specific verb ('Test') and resource ('documentation build and local server'). It distinguishes from siblings like deploy_site and deploy_pages by focusing on pre-deployment testing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use before deploying to GitHub Pages but provides no explicit guidance on when to use this tool vs siblings (e.g., deploy_site, deploy_pages). No information on when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan documentation directory for staleness markers and identify files needing updates based on configurable time thresholds (minutes, hours, days)
| Name | Required | Description | Default |
|---|---|---|---|
| docsPath | Yes | Path to documentation directory | |
| projectPath | No | Path to project root (for knowledge graph tracking) | |
| warningThreshold | No | Warning threshold (yellow flag) | |
| staleThreshold | No | Stale threshold (orange flag) | |
| criticalThreshold | No | Critical threshold (red flag) | |
| preset | No | Use predefined threshold preset | |
| includeFileList | No | Include detailed file list in response | |
| sortBy | No | Sort order for file list | staleness |
| storeInKG | No | Store tracking event in knowledge graph for historical analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only or modifies state (e.g., the 'storeInKG' parameter implies potential writes). Critical traits like permissions, side effects, or rate limits are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 28-word sentence that immediately conveys the core purpose. It is concise, front-loaded, and contains no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, nested objects, enums, no output schema), the description is too brief. It fails to explain return values, the effect of 'storeInKG', or the preset behavior, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond the schema; it merely echoes 'configurable time thresholds' already detailed in parameters. No new value provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool scans a documentation directory for staleness markers and identifies files needing updates based on configurable thresholds. It clearly distinguishes from siblings like 'check_documentation_links' or 'validate_documentation_freshness' by focusing on tracking freshness over time.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., 'validate_documentation_freshness' or 'detect_documentation_gaps'). The description does not mention prerequisites, context, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Intelligently analyze and update existing documentation using memory insights and code comparison
| Name | Required | Description | Default |
|---|---|---|---|
| analysisId | Yes | Repository analysis ID from analyze_repository tool | |
| docsPath | Yes | Path to existing documentation directory | |
| compareMode | No | Mode of comparison between code and documentation | comprehensive |
| updateStrategy | No | How aggressively to suggest updates | moderate |
| preserveStyle | No | Preserve existing documentation style and formatting | |
| focusAreas | No | Specific areas to focus updates on (e.g., "dependencies", "scripts", "api") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only mentions 'analyze and update' but does not explain side effects (e.g., file modification vs suggestion), error states, or idempotency. Lacks behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, clear sentence that conveys core function. Could be improved by listing key parameters, but it is appropriately concise without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is moderately complex with 6 parameters, enums, and no output schema. The description lacks explanation of return values, success criteria, or potential side effects. Additional context on workflow would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions. The description adds high-level context ('memory insights and code comparison') but does not provide additional meaning per parameter beyond what schema offers. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool updates documentation using memory and code comparison, but it does not differentiate from siblings like 'sync_code_to_docs' or 'detect_documentation_gaps'. The verb 'analyze and update' and resource 'existing documentation' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Does not mention prerequisites, exclusions, or context for selection among many documentation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate general content quality: broken links, code syntax, references, and basic accuracy
| Name | Required | Description | Default |
|---|---|---|---|
| contentPath | Yes | Path to the content directory to validate | |
| validationType | No | Type of validation: links, code, references, or all | all |
| includeCodeValidation | No | Whether to validate code blocks | |
| followExternalLinks | No | Whether to validate external URLs (slower) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavior. It only lists validation types but does not mention if the tool is read-only, if it modifies content, or what the output format is. This is insufficient for an agent to understand side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence, front-loaded with the key purpose. No wasted words, but could be slightly expanded to include behavior or usage context without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description is brief. It covers basic purpose but lacks details on return values, performance implications (e.g., following external links is flagged as slower in the param description but not in the tool description), and differentiation from siblings. Adequate for simple use but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter's meaning is already clear. The description adds a summary of validation types but does not elaborate beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates content quality including specific aspects (broken links, code syntax, references, basic accuracy). It distinguishes itself from sibling tools like 'check_documentation_links' which is link-specific, and 'validate_documentation_freshness' which targets freshness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. With numerous sibling tools like 'check_documentation_links' and 'validate_documentation_freshness', the description should specify context or exclusions but does not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate the accuracy, completeness, and compliance of generated Diataxis documentation
| Name | Required | Description | Default |
|---|---|---|---|
| contentPath | Yes | Path to the documentation directory to validate | |
| analysisId | No | Optional repository analysis ID for context-aware validation | |
| validationType | No | Type of validation: accuracy, completeness, compliance, or all | all |
| includeCodeValidation | No | Whether to validate code examples | |
| confidence | No | Validation confidence level: strict, moderate, or permissive | moderate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description provides no behavioral details beyond 'validate'. It doesn't disclose return format, side effects, or what happens on failure. For a validation tool, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, concise sentence with no redundant words. It efficiently conveys the tool's purpose without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters and no output schema, the description omits critical context: what the tool returns (a report? errors?), and how results are presented. Given the rich set of sibling tools, more completeness is needed for correct agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents each parameter. The description adds no extra meaning beyond the schema's descriptions, earning baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'validate' and the resource 'Diataxis documentation', specifying three distinct aspects (accuracy, completeness, compliance). This distinguishes it from sibling tools like 'validate_content' which is more generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool compared to siblings such as 'validate_content', 'check_documentation_links', or 'evaluate_readme_health'. The agent gets no help in choosing among validation-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate documentation freshness, initialize metadata for files without it, and update timestamps based on code changes
| Name | Required | Description | Default |
|---|---|---|---|
| docsPath | Yes | Path to documentation directory | |
| projectPath | Yes | Path to project root (for git integration) | |
| initializeMissing | No | Initialize metadata for files without it | |
| updateExisting | No | Update last_validated timestamp for all files | |
| updateFrequency | No | Default update frequency for new metadata | monthly |
| validateAgainstGit | No | Validate against current git commit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions updating timestamps and initializing metadata, which implies write operations, but does not disclose potential side effects, permissions required, or whether the tool can be run in a read-only mode. The boolean flags control behavior, but the description does not clarify what happens when they are toggled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that bundles three actions, which is concise but could be more readable. Breaking it into a list or adding bullet points would improve clarity. Every clause earns its place, but structure is average.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return values, error handling, or behavior when files are missing. The schema covers parameters well, but the overall context of the tool's operation is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds little value beyond summarizing the parameters. It does not clarify how parameters like 'updateFrequency' interact with the validation process or the consequences of setting 'validateAgainstGit' to false.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates documentation freshness, initializes metadata, and updates timestamps. While it covers the main function, it does not explicitly differentiate from sibling tools like 'track_documentation_freshness' or 'validate_content', which could lead to confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'track_documentation_freshness' or 'validate_content'. It lacks explicit context for appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate README files against community best practices checklist with detailed scoring
| Name | Required | Description | Default |
|---|---|---|---|
| readmePath | Yes | Path to the README file to validate | |
| projectPath | No | Path to project directory for additional context | |
| strict | No | Use strict validation rules | |
| outputFormat | No | Output format for the validation report | console |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions validation and scoring but does not disclose behavioral details such as whether it modifies files, requires specific permissions, or how errors are handled. The scoring mechanism is not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that effectively communicates the tool's purpose without waste. It is front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema and annotations. The description does not explain what 'detailed scoring' entails, the structure of the report, or return format. Given the complexity of a checklist validation, more information is needed for an agent to correctly interpret the results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already clearly describes all parameters. The description adds no additional information about parameters beyond what the schema provides, meeting the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates README files against a community best practices checklist with scoring. It distinguishes from sibling tools like validate_content or analyze_readme by specifying the checklist and scoring aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for README validation but does not explicitly state when to use it versus alternatives like analyze_readme or evaluate_readme_health. No exclusions or alternative guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify and troubleshoot GitHub Pages deployment
| Name | Required | Description | Default |
|---|---|---|---|
| repository | Yes | Repository path or URL | |
| url | No | Expected deployment URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist. The description implies a read/check operation but lacks details on behavioral traits like authentication needs, rate limits, or side effects. 'Troubleshoot' hints at diagnostic actions but is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is concise and front-loaded, but its brevity omits valuable details. It earns its place with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters and no output schema, the description lacks specifics on what exactly is verified (e.g., DNS, SSL, build status). It could be more complete given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with basic parameter descriptions. The tool description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Verify and troubleshoot GitHub Pages deployment' clearly specifies the action (verify/troubleshoot) and resource (GitHub Pages deployment). It distinguishes from sibling tools like 'deploy_pages' and 'test_local_deployment'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives such as 'test_local_deployment' or 'validate_content'. No explicit when-to-use or when-not-to-use information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
deploy_sitev0.7.0get_community_insightsv0.6.0batch_simulate_executionrecord_drift_outcomesimulate_executionScored across 52 tools
Many memory_* tools have overlapping purposes (e.g., memory_recall, memory_contextual_search, memory_similar, memory_insights all retrieve/analyze memories), and there are multiple validation tools (validate_diataxis_content, validate_content, validate_readme_checklist) that could cause misselection. The boundaries between these tools are not clearly distinct, leading to ambiguity.
The majority follow verb_noun (e.g., analyze_repository, generate_config), but the memory_* group is inconsistent—some are verb-noun (memory_recall), others are adjective-noun (memory_similar), and there's a deprecated alias (deploy_pages) alongside deploy_site. Mixed conventions and occasional non-verb patterns reduce predictability.
52 tools is excessive for a documentation MCP server. The core workflow (analysis, SSG, generation, deployment, validation) could be covered with 10-15 tools; the large memory_* cluster and multiple overlapping validation tools inflate the surface and make it unwieldy for an agent to navigate.
The server covers a broad range: repository analysis, SSG recommendation, configuration, structure setup, content generation, validation, deployment, README analysis, memory management, and drift detection. While some operations like deleting documentation are absent, the coverage is extensive and few critical gaps are apparent for the stated purpose.
An MCP server that gives your AI access to the source code and docs of all public github repos
DocBase MCP server for AI agents
A Model Context Protocol (MCP) application for automated GitHub PR analysis and issue management.…
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
