Search and discover available tools by category, keyword, or capability. Use this to find the right tool for a task without loading all tool schemas. Returns lightweight tool metadata by default; use includeSchema:true for full schemas.

Comprehensive recursive project ecosystem analysis with advanced prompting techniques (Knowledge Generation + Reflexion)

Get detailed architectural context for specific files or the entire project, automatically sets up ADR infrastructure if missing, and provides outcome-focused workflow for project success

Generate Architectural Decision Records from a Product Requirements Document with advanced prompting techniques (APE + Knowledge Generation)

Compare TODO.md progress against ADRs and current environment to validate implementation status

Analyze content for sensitive information using AI-powered detection with optional memory integration for security pattern learning

Generate masking instructions for detected sensitive content

Configure custom sensitive patterns for a project

Apply basic content masking (fallback when AI is not available)

Validate that content masking was applied correctly

Manage MCP resource cache (clear, stats, cleanup)

Configure content masking for all MCP outputs

Suggest architectural decisions with advanced prompting techniques (Knowledge Generation + Reflexion). TIP: Read @.mcp-server-context.md first for project history, patterns, and previous ADRs to ensure consistency.

Generate a complete ADR from decision data. TIP: Reference @.mcp-server-context.md to align with existing architectural patterns and decisions.

Generate bootstrap.sh and validate_bootstrap.sh scripts to ensure deployed code follows ADR requirements. CRITICAL: Before generating scripts, use WebFetch to query the base code repository (e.g., https://github.com/validatedpatterns/common for OpenShift) and authoritative pattern documentation (e.g., https://play.validatedpatterns.io/). Merge the base repository code into your project and have bootstrap.sh call the pattern's scripts rather than generating everything from scratch. This ensures compliance with validated deployment patterns.

GUIDED EXECUTION MODE: This tool guides you through an interactive, step-by-step deployment validation workflow. It does NOT execute commands internally - instead, it tells YOU what commands to run and processes the results iteratively. Workflow: (1) First call with iteration=0: Detects platform (OpenShift/K8s/Docker), validates environment connection, and requests human approval for target platform. (2) Subsequent calls: After running each command and reporting back with output, the tool provides next steps. Environment Validation: Before deployment, the tool verifies connection to the target platform (e.g., oc status for OpenShift, kubectl cluster-info for K8s) and requires explicit human confirmation. Validated Patterns Integration: Automatically identifies base code repositories (e.g., validatedpatterns/common for OpenShift) and guides you to merge them into your project. Deployment Cleanup: Supports CI/CD-style workflows with deployment teardown/restart guidance. Call this tool iteratively, passing previous command output back each time.

Discover and catalog existing ADRs in the project

Analyze ADR timeline with smart time tracking, adaptive thresholds, and actionable recommendations. Auto-detects project context (startup/growth/mature) and generates prioritized work queue based on staleness, implementation lag, and technical debt.

Review existing ADRs against actual code implementation with cloud/DevOps expertise. TIP: After review, call get_server_context to update @.mcp-server-context.md with findings.

Validate an existing ADR against actual infrastructure reality using research-driven analysis. TIP: Compare findings against patterns in @.mcp-server-context.md for consistency checks.

Validate all ADRs in a directory against actual infrastructure reality

Incorporate research findings into architectural decisions

Create a research template file for documenting findings

Request confirmation before applying research-based changes

Generate architectural rules from ADRs and code patterns

Validate code against architectural rules

Create machine-readable rule set in JSON/YAML format

Analyze environment context and provide optimization recommendations with optional memory integration for environment snapshot tracking

Generate context-aware research questions and create research tracking system

Perform research using cascading sources: project files → knowledge graph → environment resources → web search (fallback)

Atomic tool for searching codebase files based on query patterns. Returns raw file matches with relevance scores. Extracted from ResearchOrchestrator per ADR-018.

LLM-managed web search using Firecrawl for cross-platform support

LLM-managed cloud provider operations with research-driven approach

LLM-managed database operations with research-driven approach

Analyze deployment progress and verify completion with outcome rules

Check AI execution configuration and status for debugging prompt-only mode issues

Get intelligent workflow guidance and tool recommendations based on your goals and project context to achieve expected outcomes efficiently

Get comprehensive development guidance that translates architectural decisions and workflow recommendations into specific coding tasks, implementation patterns, and development roadmap

List available file system roots that can be accessed. Use this to discover what directories are available before reading files.

List files and folders in a directory. Use this to explore the file structure within accessible roots.

Read contents of a file

Write content to a file

List contents of a directory

Generate deployment guidance and instructions from ADRs with environment-specific configurations

AI-driven security-focused git push with credential detection, file filtering, and deployment metrics tracking. Tests should be run by calling AI and results provided.

Comprehensive deployment readiness validation with test failure tracking, deployment history analysis, and hard blocking for unsafe deployments. Integrates with smart_git_push for deployment gating.

Track releases mapped to ADR decisions. Generates changelogs, manages milestones, compares releases, and assesses release readiness. Supports greenfield and brownfield projects. Writes CHANGELOG.md, creates GitHub Releases and Milestones.

Generate TODO.md from ADRs with comprehensive task breakdown. Decomposes each ADR into paired test+production tasks (TDD), links tasks to release milestones, and preserves manual edits via a bounded HTML-comment section. Re-runs are idempotent; tasks for deleted/superseded ADRs move to a Stale Tasks section.

Structured failure analysis and test plan generation with memory integration for troubleshooting session tracking and intelligent ADR/research suggestion capabilities - provide JSON failure info to get specific test commands

Central coordination for project health scoring system - recalculate, sync, diagnose, optimize, and reset scores across all MCP tools

Enhanced project planning and workflow management tool - phase-based project management, team resource allocation, progress tracking, risk analysis, and executive reporting

Interactive guided ADR planning and creation tool - walks users through structured decision-making process with research integration, option evaluation, and automatic ADR generation. TIP: Start by reading @.mcp-server-context.md to understand project context and previous decisions.

Advanced memory loading tool for the memory-centric architecture. Query, explore, and manage memory entities and relationships. Load ADRs into memory system and perform intelligent queries.

Retrieve full analysis content from tiered responses. Expand entire analysis or specific sections stored in memory. Use this when a tool returns a summary with an expandable ID.

AI-powered dynamic tool sequencing - intelligently analyze user requests and generate structured tool execution plans

Phase 3: Retrieve and expand stored content from a tiered response using its expandable ID

Phase 3: Search and retrieve conversation sessions based on filters

Phase 3: Get current conversation context snapshot for resumption or analysis

Phase 3: Get statistics about stored conversation memory

ADR-018: Simple CRUD operations for knowledge graph. Add/remove entities (intents, ADRs, tools, code) and relationships. Use knowledge://graph resource to read current state (zero token cost).

Generate a comprehensive context file showing the server's current state, memory, and capabilities. Creates .mcp-server-context.md that can be @ referenced in conversations for instant LLM awareness

Get the current date and time in various formats. Useful for timestamping ADRs, research documents, and other architectural artifacts. Returns ISO 8601, human-readable, and ADR-specific date formats.

Dynamically set the active project path for the current session. Call this at the start of a session to switch between projects without restarting the server or modifying environment variables. All subsequent tool calls will use this path as the default.

Load a specific prompt or prompt section on-demand. Part of CE-MCP lazy loading system that reduces token usage by ~96% by loading prompts only when needed. Use this to retrieve prompt templates for ADR generation, analysis, deployment, and other operations.

Sync ADRs to ADR Aggregator platform (https://adraggregator.com) for centralized tracking, visualization, and team collaboration. Supports incremental and full sync modes with optional metadata.

Fetch ADR context from ADR Aggregator including summaries, diagrams, timeline data, and code links. Useful for getting a consolidated view of architectural decisions.

Get ADR staleness report from ADR Aggregator with review compliance metrics. Identifies stale ADRs that need attention and provides governance insights.

Get domain-specific ADR templates and anti-patterns from ADR Aggregator. Includes best practices for web applications, microservices, APIs, and more. No authentication required.

Get Mermaid diagrams for ADRs from ADR Aggregator. Includes workflow, relationship, and impact diagrams. Requires Pro+ tier.

Validate ADR compliance against implementation via ADR Aggregator. Checks that code actually implements documented decisions. Requires Pro+ tier.

Get cross-repository knowledge graph from ADR Aggregator with analytics and insights. Visualize ADR relationships across repositories. Requires Team tier.

Update the implementation status of synced ADRs directly from the IDE. Supports statuses: not_started, in_progress, implemented, deprecated, blocked. Requires Pro+ tier.

Get ADR priorities for roadmap and backlog planning from ADR Aggregator. Returns prioritized ADRs with scores, dependencies, blockers, implementation status, and gap counts.

Scan local codebase and compare with ADRs to detect bi-directional gaps. Finds: (1) ADR-to-code gaps: file references in ADRs that do not exist, (2) Code-to-ADR gaps: technologies in package.json and architectural patterns without ADR coverage. Reports gaps to ADR Aggregator for tracking.

Get current code gaps from ADR Aggregator. Returns gaps with their status (open, dismissed, resolved) for tracking and management.