# think-mcp: Comprehensive MCP 2025 Enhancement Plan
> **Created:** December 31, 2025
> **Status:** Planning Complete - Ready for Implementation
> **Scope:** All 11 thinking tools with MCP 2025-11-25 capabilities
## Executive Summary
This plan details **exhaustive enhancement opportunities** for all 11 think-mcp tools, leveraging MCP 2025-11-25 capabilities. Implementation assumes best practices with **file-based persistence for testing** and **database persistence for production**.
---
## Architecture Overview
### New Capability Layers
```
┌─────────────────────────────────────────────────────────────────────────┐
│ MCP Client (Claude, etc.) │
└─────────────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────────────┐
│ Enhanced MCP Server Core │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ Capability Layer: Tasks | Sampling | Elicitation | Resources/Prompts│ │
│ └────────────────────────────────────────────────────────────────────┘ │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ Orchestration: Tool Chaining | Async Executor | Progress Tracker │ │
│ └────────────────────────────────────────────────────────────────────┘ │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ Integration: Web Search | External APIs | Webhooks | Analytics │ │
│ └────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────────────┐
│ 11 Enhanced Thinking Tools │
│ trace | model | pattern | paradigm | debug | council │
│ decide | reflect | hypothesis | debate | map │
└─────────────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────────────┐
│ Persistence Layer │
│ File Storage (Testing) ◄───► Database Storage (Production) │
│ SQLite/JSON PostgreSQL/Redis │
└─────────────────────────────────────────────────────────────────────────┘
```
### New Directory Structure
```
src/
├── index.ts # Enhanced with new MCP capabilities
├── toolNames.ts # Unchanged
├── capabilities/ # NEW: MCP 2025-11-25 capabilities
│ ├── index.ts
│ ├── tasks/
│ │ ├── TaskManager.ts # Task lifecycle management
│ │ ├── TaskExecutor.ts # Async execution engine
│ │ └── TaskProgress.ts # Progress tracking & notifications
│ ├── sampling/
│ │ ├── SamplingClient.ts # Server-initiated LLM sampling
│ │ └── AgentLoop.ts # Agentic workflow orchestration
│ ├── elicitation/
│ │ ├── ElicitationManager.ts # User input requests
│ │ └── InputValidators.ts # Input validation schemas
│ └── exposure/
│ ├── ResourceProvider.ts # MCP Resources
│ └── PromptProvider.ts # MCP Prompts
├── orchestration/ # NEW: Tool orchestration
│ ├── ToolChain.ts # Tool chaining logic
│ ├── ToolRegistry.ts # Enhanced tool registry
│ └── WorkflowEngine.ts # Multi-tool workflows
├── integrations/ # NEW: External integrations
│ ├── WebSearchAdapter.ts # Web search abstraction
│ ├── WebhookManager.ts # Webhook notifications
│ └── AnalyticsClient.ts # Usage analytics
├── persistence/ # NEW: Storage abstraction
│ ├── StorageAdapter.ts # Abstract interface
│ ├── FileStorage.ts # File-based (testing)
│ └── DatabaseStorage.ts # DB-based (production)
├── models/
│ ├── interfaces.ts # Extended with new types
│ └── schemas.ts # NEW: Zod validation schemas
└── tools/
├── base/
│ └── EnhancedToolServer.ts # Base class with all capabilities
└── [11 enhanced tool servers]
```
---
## Tool-by-Tool Enhancement Matrix
### 🔵 TRACE Tool (Sequential Thinking)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Long thought chains (10+ thoughts) run in background with progress updates | Non-blocking deep reasoning |
| **Agentic Sampling** | Auto-generate follow-up thoughts, self-critique loops, dynamic branching | Autonomous reasoning exploration |
| **Elicitation** | "Should I continue?", "Rate confidence (1-5)", "Which branch to explore?" | User-guided reasoning direction |
| **Web Search** | Fact-check claims, find supporting evidence, discover counter-examples | Evidence-backed reasoning |
| **Tool Chaining** | trace→model (apply mental model), trace→reflect (assess confidence), trace→map (visualize) | Multi-dimensional analysis |
| **Resources** | Expose thought history, branch tree, revision graph | Queryable reasoning state |
| **Prompts** | "deep-thinking" template for complex problems | Guided reasoning initiation |
**Hypothetical Scenario - Agentic Trace:**
```
User: "Analyze the implications of moving to microservices"
Agent → trace.runAgenticAnalysis({
problem: "microservices migration implications",
depth: "comprehensive",
enableBranching: true
})
Server autonomously:
1. Generates initial thought about organizational impact
2. Branches to explore: technical debt, team structure, operational complexity
3. Uses sampling to self-critique each branch
4. Discovers via web search: "70% of microservices migrations exceed timeline"
5. Chains to reflect tool to assess confidence
6. Returns complete thought tree with evidence-backed conclusions
Result: 15-thought analysis with 3 branches, 5 web citations, confidence scores
```
---
### 🔵 MODEL Tool (Mental Models)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Deep first-principles decomposition, multi-model comparison | Thorough model application |
| **Agentic Sampling** | Auto-select appropriate model, chain models, validate conclusions | Intelligent model selection |
| **Elicitation** | "Which model fits better?", "Confirm assumptions", "Add constraints?" | Tailored model application |
| **Web Search** | Find real-world examples, research historical precedents, validate assumptions | Evidence-grounded models |
| **Tool Chaining** | model→trace (step through), model→hypothesis (formalize predictions), model→debate (challenge) | Validated model conclusions |
| **Resources** | Expose model catalog, application history, success patterns | Model knowledge base |
| **Prompts** | Templates per model type: "first-principles-analysis", "pareto-breakdown" | Model-specific guidance |
**Hypothetical Scenario - Web-Enhanced Model:**
```
User: "Apply first principles to reduce cloud costs"
Agent → model.applyWithResearch({
modelName: "first_principles",
problem: "cloud cost optimization",
enableWebSearch: true
})
Server:
1. Breaks down to fundamentals: compute, storage, network, licensing
2. Web search: "AWS cost optimization case studies 2025"
3. Finds: "Reserved instances save 40-60% over on-demand"
4. Web search: "Spot instance interruption rates by region"
5. Validates assumption: "Storage costs scale linearly" - CONFIRMED
6. Returns model application with 4 external citations
Result: First principles analysis enriched with current market data
```
---
### 🔵 PATTERN Tool (Design Patterns)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Codebase pattern analysis, multi-pattern trade-off evaluation | Comprehensive pattern assessment |
| **Agentic Sampling** | Analyze code and suggest patterns, generate implementation | Smart pattern recommendations |
| **Elicitation** | "Primary concern: maintainability/performance/simplicity?", "Tech stack?" | Context-aware suggestions |
| **Web Search** | Find GitHub implementations, research pattern variants, discover anti-patterns | Real-world pattern knowledge |
| **Tool Chaining** | pattern→paradigm (compatible paradigm), pattern→map (visualize), pattern→decide (choose) | Informed pattern selection |
| **Resources** | Pattern catalog with examples, compatibility matrix | Pattern reference library |
| **Prompts** | "pattern-recommendation", "pattern-implementation" templates | Guided pattern workflows |
**Hypothetical Scenario - Pattern with Web Search:**
```
User: "Recommend a pattern for handling distributed transactions"
Agent → pattern.recommendWithResearch({
context: "distributed transactions across microservices",
constraints: ["eventual consistency acceptable", "high throughput"],
enableWebSearch: true
})
Server:
1. Identifies candidates: Saga, 2PC, Outbox
2. Web search: "Saga pattern implementation examples GitHub"
3. Web search: "Saga vs 2PC performance comparison"
4. Finds: "Netflix uses Saga for 99.9% of distributed transactions"
5. Chains to decide tool for formal comparison
6. Returns recommendation with implementation guide + GitHub links
Result: Saga pattern recommended with Netflix case study, 3 GitHub examples
```
---
### 🔵 PARADIGM Tool (Programming Paradigms)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Code transformation examples, paradigm compatibility analysis | Deep paradigm exploration |
| **Agentic Sampling** | Analyze existing code, suggest paradigm shifts, generate examples | Intelligent paradigm guidance |
| **Elicitation** | "Team experience?", "Rate: testability/performance/readability", "Required libraries?" | Team-appropriate suggestions |
| **Web Search** | Find best practices, research language support, discover trends | Current paradigm landscape |
| **Tool Chaining** | paradigm→pattern (compatible patterns), paradigm→debug (paradigm-specific) | Holistic paradigm adoption |
| **Resources** | Paradigm guides, language-paradigm mappings | Paradigm knowledge base |
| **Prompts** | "paradigm-selection", "paradigm-migration" templates | Guided paradigm decisions |
**Hypothetical Scenario - Paradigm Migration Guidance:**
```
User: "Should we migrate from OOP to functional for our data pipeline?"
Agent → paradigm.analyzeShift({
currentParadigm: "object_oriented",
targetParadigm: "functional",
context: "data pipeline processing",
enableWebSearch: true,
chainToDecide: true
})
Server:
1. Analyzes OOP vs FP for data processing
2. Web search: "functional programming data pipeline performance"
3. Web search: "OOP to FP migration case studies"
4. Finds: "Spotify migrated to Scala FP, 3x throughput improvement"
5. Chains to decide tool with criteria: testability, performance, learning curve
6. Elicits: "What's your team's FP experience level?"
Result: Decision matrix with recommendation, migration roadmap, team training needs
```
---
### 🔵 DEBUG Tool (Debugging Approaches)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Systematic binary search, log analysis, root cause investigation | Thorough debugging |
| **Agentic Sampling** | Auto-generate hypotheses, execute elimination, verify fixes | Autonomous debugging |
| **Elicitation** | "Can you reproduce?", "Which component suspected?", "Expected vs actual?" | Targeted debugging |
| **Web Search** | Search Stack Overflow, find GitHub issues, research error messages | Community knowledge |
| **Tool Chaining** | debug→hypothesis (formalize bug theory), debug→trace (step through), debug→map (visualize) | Scientific debugging |
| **Resources** | Debug session history, resolution patterns | Debugging knowledge base |
| **Prompts** | "systematic-debug", "production-incident" templates | Guided debugging workflows |
**Hypothetical Scenario - Web-Assisted Debugging:**
```
User: "Debug: 'ECONNREFUSED' errors in production, intermittent"
Agent → debug.investigateWithResearch({
issue: "ECONNREFUSED errors, intermittent, production only",
approachName: "cause_elimination",
enableWebSearch: true
})
Server:
1. Generates hypotheses: DNS, connection pool exhaustion, firewall, health checks
2. Web search: "ECONNREFUSED intermittent Node.js production"
3. Finds: "Common cause: connection pool exhaustion under load"
4. Web search: "Node.js connection pool best practices 2025"
5. Chains to hypothesis tool to formalize test plan
6. Returns elimination matrix with probability scores
Result: 4 hypotheses ranked by likelihood, test plan, 3 Stack Overflow references
```
---
### 🔵 COUNCIL Tool (Collaborative Reasoning)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Multi-round deliberations, consensus building with voting | Extended expert debates |
| **Agentic Sampling** | Auto-generate personas, simulate discussions, synthesize recommendations | Autonomous deliberation |
| **Elicitation** | "Which perspectives needed?", "Add another viewpoint?", "Rate consensus (1-5)" | User-guided deliberation |
| **Web Search** | Research domain expert opinions, find case studies, validate claims | Expert-backed perspectives |
| **Tool Chaining** | council→debate (structured argumentation), council→decide (formal decision), council→reflect (bias check) | Comprehensive deliberation |
| **Resources** | Persona library, deliberation transcripts, consensus history | Council knowledge base |
| **Prompts** | "technical-council", "stakeholder-council", "risk-council" templates | Role-specific councils |
**Hypothetical Scenario - Agentic Council:**
```
User: "Convene a council on whether to build vs buy our auth system"
Agent → council.conveneAsync({
topic: "Build vs Buy authentication system",
duration: "comprehensive",
enableSampling: true,
enableWebSearch: true
})
→ Task ID returned immediately, agent continues other work
Server autonomously over 2-3 minutes:
1. Generates 5 personas: Security Architect, CTO, DevOps Lead, Product Manager, Finance
2. Security Architect (via sampling): "Build gives control but requires dedicated team"
3. Web search: "Auth0 vs custom auth total cost of ownership"
4. CTO (via sampling): "Buy accelerates time-to-market"
5. Finance (via sampling): "Auth0 costs $50k/year at our scale"
6. Synthesizes disagreements into structured debate
7. Reaches recommendation with confidence score
Result: Build vs Buy analysis with 5 expert perspectives, cost comparison, recommendation
```
---
### 🔵 DECIDE Tool (Decision Framework)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Multi-criteria evaluation, scenario simulation, sensitivity analysis | Rigorous decision analysis |
| **Agentic Sampling** | Auto-generate criteria, evaluate options, generate recommendation | Autonomous decision support |
| **Elicitation** | "Rate criterion importance (1-10)", "Add constraints?", "Which stakeholders?" | User-calibrated decisions |
| **Web Search** | Research option comparisons, find case studies, validate assumptions | Evidence-based decisions |
| **Tool Chaining** | decide→council (get perspectives), decide→hypothesis (test assumptions), decide→reflect (confidence) | Validated decisions |
| **Resources** | Decision frameworks, historical decisions, outcome tracking | Decision knowledge base |
| **Prompts** | "weighted-decision", "scenario-analysis", "risk-assessment" templates | Decision methodology guides |
**Hypothetical Scenario - Complete Decision Workflow:**
```
User: "Help decide: PostgreSQL vs MongoDB for our new product"
Agent → decide.analyzeComprehensive({
decisionStatement: "Database selection for new product",
options: ["PostgreSQL", "MongoDB"],
analysisType: "weighted-criteria",
enableWebSearch: true,
chainToCouncil: true
})
Server:
1. Elicits criteria importance: scalability, query flexibility, team expertise, cost
2. Web search: "PostgreSQL vs MongoDB 2025 benchmark comparison"
3. Web search: "MongoDB licensing cost calculator"
4. Chains to council for multi-perspective input
5. Runs sensitivity analysis: "What if scalability weight changes?"
6. Generates expected value calculation with confidence intervals
Result: Decision matrix, sensitivity analysis, council perspectives, recommendation with 85% confidence
```
---
### 🔵 REFLECT Tool (Metacognitive Monitoring)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Deep knowledge assessment, bias detection, reasoning validation | Thorough self-assessment |
| **Agentic Sampling** | Auto-assess knowledge boundaries, generate probing questions, identify gaps | Autonomous reflection |
| **Elicitation** | "How confident in this domain?", "What sources?", "Areas of uncertainty?" | User self-assessment |
| **Web Search** | Fact-check confident claims, find contradicting evidence, validate knowledge | Truth-grounded reflection |
| **Tool Chaining** | reflect→trace (examine reasoning), reflect→model (error propagation), reflect→debate (challenge) | Deep metacognition |
| **Resources** | Knowledge maps, confidence history, bias patterns | Self-awareness knowledge base |
| **Prompts** | "knowledge-audit", "bias-check", "confidence-calibration" templates | Reflection workflows |
**Hypothetical Scenario - Fact-Checking Reflection:**
```
User: "Assess my confidence in this market analysis conclusion"
Agent → reflect.assessWithFactCheck({
task: "Q4 market growth projection: 15% increase",
claims: [
{ claim: "Competitor X is exiting the market", status: "inference" },
{ claim: "Supply chain issues resolved", status: "speculation" }
],
enableWebSearch: true
})
Server:
1. Assesses knowledge domain: Market Analysis (proficiency: familiar)
2. Web search: "Competitor X market exit news 2025"
3. Finds: No evidence of exit - CONTRADICTS claim
4. Web search: "Supply chain semiconductor 2025 status"
5. Finds: "Supply chains 70% recovered" - PARTIALLY SUPPORTS
6. Identifies bias: Optimism bias in projection
Result: Adjusted confidence from 80% to 55%, 2 claims invalidated, bias identified
```
---
### 🔵 HYPOTHESIS Tool (Scientific Method)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Experiment design and execution, data analysis, multi-hypothesis testing | Complete scientific inquiry |
| **Agentic Sampling** | Auto-generate hypotheses, design experiments, analyze results, draw conclusions | Autonomous research |
| **Elicitation** | "What would disprove this?", "What data can you collect?", "Rate experimental validity" | Guided experimentation |
| **Web Search** | Research existing studies, find methodologies, validate approaches | Research-backed science |
| **Tool Chaining** | hypothesis→debug (test debugging hypotheses), hypothesis→reflect (validity), hypothesis→trace (evidence) | Rigorous validation |
| **Resources** | Experiment history, hypothesis library, methodology guides | Research knowledge base |
| **Prompts** | "ab-test-design", "root-cause-hypothesis", "performance-experiment" templates | Scientific workflows |
**Hypothetical Scenario - Full Scientific Inquiry:**
```
User: "Test hypothesis: Adding caching will reduce API latency by 50%"
Agent → hypothesis.runFullInquiry({
initialObservation: "API latency averages 500ms, target <250ms",
domain: "Performance Optimization",
enableWebSearch: true,
enableSampling: true
})
Server autonomously:
1. Forms hypothesis: "Redis caching of frequent queries reduces latency 50%"
2. Web search: "Redis caching latency improvement benchmarks"
3. Designs experiment: A/B test with 10% traffic, 1-week duration
4. Defines variables: Independent (cache enabled), Dependent (p95 latency), Controlled (traffic pattern)
5. Specifies predictions: "If cache hit rate >80%, then latency <250ms"
6. Uses sampling to generate test cases
7. Provides analysis framework for results interpretation
Result: Complete experimental design with methodology, success criteria, and analysis plan
```
---
### 🔵 DEBATE Tool (Structured Argumentation)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Multi-round dialectical exchange, argument strength analysis | Extended debates |
| **Agentic Sampling** | Auto-generate counter-arguments, evaluate validity, synthesize positions | Autonomous argumentation |
| **Elicitation** | "Which position do you lean toward?", "Arguments not considered?", "Rate rebuttal strength" | User-guided debate |
| **Web Search** | Find supporting evidence, research counter-examples, validate premises | Evidence-backed arguments |
| **Tool Chaining** | debate→council (multi-perspective), debate→decide (formalize decision), debate→reflect (argument quality) | Comprehensive dialectic |
| **Resources** | Argument graphs, debate history, logical fallacy library | Argumentation knowledge base |
| **Prompts** | "thesis-antithesis-synthesis", "devil-advocate", "steelman" templates | Debate workflows |
**Hypothetical Scenario - Autonomous Debate:**
```
User: "Debate: 'AI code review should replace human review'"
Agent → debate.runDialectic({
claim: "AI code review should replace human review",
rounds: 3,
enableSampling: true,
enableWebSearch: true
})
Server autonomously:
1. Thesis (via sampling): "AI catches 95% of bugs faster, more consistently"
2. Web search: "AI code review accuracy studies"
3. Antithesis (via sampling): "Humans understand context, architecture, business logic"
4. Web search: "AI code review limitations research"
5. Objection: "AI misses security vulnerabilities requiring domain knowledge"
6. Rebuttal: "Hybrid approach: AI for syntax, humans for architecture"
7. Synthesis: "AI augments human review, doesn't replace"
Result: Complete dialectical analysis with evidence, 3 rounds, nuanced synthesis
```
---
### 🔵 MAP Tool (Visual Reasoning)
| Enhancement | Implementation | Value Proposition |
|-------------|----------------|-------------------|
| **Async Tasks** | Complex diagram generation, multi-iteration refinement, layout optimization | Sophisticated visualizations |
| **Agentic Sampling** | Auto-generate diagrams from description, analyze patterns, suggest improvements | Autonomous visualization |
| **Elicitation** | "Best diagram type?", "Add more detail?", "Primary relationship to highlight?" | User-guided visualization |
| **Web Search** | Find diagram templates, research notation standards, discover best practices | Professional visualizations |
| **Tool Chaining** | map→trace (visualize thoughts), map→council (map stakeholders), map→pattern (diagram structure) | Visual analysis |
| **Resources** | Diagram library, template catalog, notation guides | Visualization knowledge base |
| **Prompts** | "system-architecture", "process-flow", "concept-map" templates | Visualization workflows |
**Hypothetical Scenario - Intelligent Diagramming:**
```
User: "Visualize our microservices architecture with failure modes"
Agent → map.generateIntelligent({
description: "E-commerce platform with 12 services",
diagramType: "stateDiagram",
includeFailureModes: true,
enableWebSearch: true
})
Server:
1. Creates base service dependency graph
2. Web search: "microservices failure mode visualization best practices"
3. Adds failure mode states: circuit breaker, retry, fallback
4. Web search: "UML state diagram notation standards"
5. Chains to trace for failure cascade analysis
6. Generates multi-layer diagram with normal/failure states
Result: Architecture diagram with failure modes, cascade paths, recovery strategies
```
---
## Cross-Tool Integration Patterns
### Predefined Tool Chains
| Chain Name | Flow | Use Case |
|------------|------|----------|
| **deep-analysis** | trace → model → reflect | Comprehensive problem analysis |
| **decision-support** | council → debate → decide | Multi-perspective decision making |
| **debug-investigation** | debug → hypothesis → trace | Scientific debugging |
| **architecture-review** | pattern → paradigm → map → council | Architecture evaluation |
| **research-validation** | hypothesis → reflect → debate | Research quality assurance |
### Web Search Integration Matrix
| Tool | Primary Search Use | Secondary Search Use |
|------|-------------------|---------------------|
| trace | Fact-checking claims | Finding counter-examples |
| model | Real-world examples | Historical precedents |
| pattern | GitHub implementations | Anti-pattern warnings |
| paradigm | Language best practices | Migration case studies |
| debug | Stack Overflow | GitHub issues |
| council | Expert opinions | Case studies |
| decide | Option comparisons | Cost/benefit data |
| reflect | Claim validation | Contradicting evidence |
| hypothesis | Existing research | Methodology references |
| debate | Supporting evidence | Counter-arguments |
| map | Notation standards | Template examples |
---
## Infrastructure Components
### 1. Task Management System
```typescript
interface Task<TInput, TOutput> {
id: string;
toolName: ToolName;
status: "pending" | "in_progress" | "completed" | "failed" | "cancelled";
progress: number; // 0-100
progressMessage?: string;
input: TInput;
output?: TOutput;
error?: string;
parentTaskId?: string;
childTaskIds?: string[];
createdAt: Date;
updatedAt: Date;
completedAt?: Date;
}
interface TaskManager {
create(toolName: string, args: unknown, options?: TaskOptions): Task;
execute(taskId: string): Promise<void>;
cancel(taskId: string): Promise<void>;
get(taskId: string): Task | undefined;
list(filter?: TaskFilter): Task[];
onProgress(taskId: string, callback: ProgressCallback): void;
onComplete(taskId: string, callback: CompleteCallback): void;
}
```
### 2. Agentic Sampling Client
```typescript
interface SamplingClient {
createMessage(request: SamplingRequest): Promise<SamplingResponse>;
runAgentLoop(config: AgentLoopConfig): AsyncGenerator<AgentStep>;
sampleWithTools(messages: Message[], tools: ToolDefinition[]): Promise<SamplingWithToolsResponse>;
}
interface AgentLoopConfig {
systemPrompt: string;
initialMessages: Message[];
availableTools: string[]; // Tool names to make available
maxIterations: number;
stopCondition?: (response: SamplingResponse) => boolean;
}
```
### 3. Tool Chaining System
```typescript
interface ToolChain {
define(name: string, steps: ChainStep[]): ChainDefinition;
execute(chainName: string, input: unknown): Promise<ChainResult>;
chain(tool1: string, tool2: string, transformer?: DataTransformer): ToolChain;
}
interface ChainStep {
tool: string;
inputMapper?: (prevResult: unknown, context: ChainContext) => unknown;
condition?: (context: ChainContext) => boolean;
onError?: "fail" | "skip" | "retry" | ErrorHandler;
}
```
### 4. Web Search Adapter
```typescript
interface WebSearchAdapter {
search(query: string, options?: SearchOptions): Promise<SearchResult[]>;
searchForTool(toolName: ToolName, context: string, query: string): Promise<EnrichedSearchResult[]>;
}
interface SearchResult {
title: string;
url: string;
snippet: string;
relevance: number;
source: string;
}
// Implementation options:
// - SerpApi integration (recommended)
// - Brave Search API
// - Custom web scraping
```
### 5. Persistence Layer
```typescript
interface StorageAdapter {
// Session management
createSession(sessionId: string): Promise<void>;
getSession(sessionId: string): Promise<SessionData | null>;
updateSession(sessionId: string, data: Partial<SessionData>): Promise<void>;
// Task persistence
saveTask(task: Task): Promise<void>;
getTask(taskId: string): Promise<Task | null>;
listTasks(filter?: TaskFilter): Promise<Task[]>;
// Tool state
saveToolState(toolName: string, sessionId: string, state: unknown): Promise<void>;
getToolState(toolName: string, sessionId: string): Promise<unknown>;
}
// FileStorage for testing (SQLite + JSON files)
// DatabaseStorage for production (PostgreSQL + Redis)
```
### 6. Webhook Manager
```typescript
interface WebhookManager {
register(event: WebhookEvent, url: string, config?: WebhookConfig): string;
unregister(webhookId: string): void;
trigger(event: WebhookEvent, payload: unknown): Promise<WebhookResult>;
}
type WebhookEvent =
| "task.created" | "task.progress" | "task.completed" | "task.failed"
| "chain.started" | "chain.completed"
| "session.created" | "session.ended";
```
### 7. Analytics Client
```typescript
interface AnalyticsClient {
trackToolUsage(toolName: ToolName, duration: number, success: boolean): void;
trackChainExecution(chainName: string, steps: number, duration: number): void;
trackWebSearch(toolName: ToolName, query: string, resultCount: number): void;
getUsageReport(dateRange: DateRange): UsageReport;
}
```
---
## MCP Resources & Prompts
### Resources to Expose
| Resource URI | Content | Use Case |
|--------------|---------|----------|
| `think://models/catalog` | Mental model catalog | Model discovery |
| `think://models/{name}` | Specific model details | Model reference |
| `think://patterns/catalog` | Design pattern catalog | Pattern discovery |
| `think://patterns/{name}` | Specific pattern details | Pattern reference |
| `think://sessions/current` | Current session state | Session introspection |
| `think://sessions/{id}/history` | Session history | Session replay |
| `think://tasks/active` | Active tasks list | Task monitoring |
### Prompts to Expose
| Prompt Name | Arguments | Description |
|-------------|-----------|-------------|
| `deep-analysis` | problem | Comprehensive multi-tool analysis |
| `decision-support` | decision, options | Structured decision workflow |
| `debug-investigation` | issue, context | Scientific debugging |
| `architecture-review` | system | Multi-perspective architecture analysis |
| `research-validation` | hypothesis | Research quality workflow |
---
## Implementation Phases
### Phase 1: Foundation (Weeks 1-3)
**Priority: Critical Infrastructure**
1. **Persistence Layer**
- StorageAdapter interface
- FileStorage implementation (SQLite + JSON)
- DatabaseStorage implementation (PostgreSQL)
- Migration utilities
2. **Task Management**
- TaskManager with full lifecycle
- Progress tracking and notifications
- Task persistence integration
3. **Base Tool Enhancement**
- EnhancedToolServer base class
- Zod validation for all tools
- Consistent return types
- Async/await conversion
### Phase 2: Intelligence (Weeks 4-6)
**Priority: Agentic Capabilities**
4. **Agentic Sampling**
- SamplingClient implementation
- AgentLoop with tool support
- Security controls and limits
5. **Tool Chaining**
- ToolChain orchestrator
- Predefined chains (deep-analysis, decision-support, etc.)
- Dynamic chain construction
6. **Elicitation**
- ElicitationManager
- Input validation schemas
- User interaction hooks
### Phase 3: Integration (Weeks 7-9)
**Priority: External Capabilities**
7. **Web Search**
- WebSearchAdapter with SerpApi
- Per-tool search optimization
- Result caching and rate limiting
8. **Webhooks & Analytics**
- WebhookManager with retry logic
- AnalyticsClient for usage tracking
- Dashboard integration
9. **Resources & Prompts**
- ResourceProvider implementation
- PromptProvider with templates
- MCP capability registration
### Phase 4: Polish (Weeks 10-12)
**Priority: Production Readiness**
10. **Testing & Documentation**
- Integration tests for all workflows
- Performance benchmarks
- API documentation
- User guides
11. **Security & Compliance**
- Rate limiting per capability
- Cost controls
- Audit logging
- OAuth integration (if applicable)
12. **Deployment & Monitoring**
- Docker containerization
- Health checks
- Alerting integration
- Production runbooks
---
## Success Metrics
| Metric | Current | Target |
|--------|---------|--------|
| Token efficiency (vs manual orchestration) | Baseline | 80% reduction |
| Reasoning depth (avg thoughts per analysis) | 3-5 | 10-20 |
| Evidence coverage (web citations per analysis) | 0 | 3-5 |
| User interaction (elicitations per session) | 0 | 2-3 |
| Tool chaining (avg tools per workflow) | 1 | 3-5 |
| Task completion time (complex analysis) | N/A | <3 min |
---
## Critical Files to Modify
| File | Changes |
|------|---------|
| `src/index.ts` | Add Task/Resource/Prompt handlers, capability registration |
| `src/models/interfaces.ts` | Add Task, Chain, Elicitation, Search types |
| `src/tools/*.ts` (all 11) | Convert to async, add Zod, extend with new capabilities |
| `package.json` | Add dependencies: zod, better-sqlite3, pg, redis |
---
## Risk Mitigation
| Risk | Mitigation |
|------|------------|
| SDK compatibility | Pin MCP SDK version, maintain fallback sync mode |
| Web search costs | Implement caching, rate limiting, cost budgets |
| Agentic runaway | Max iterations, token limits, human-in-loop controls |
| Performance degradation | Async operations, task queuing, result caching |
| Breaking changes | Maintain backward compatibility, version API |
---
## Conclusion
This enhancement plan transforms think-mcp from a collection of individual thinking tools into an **intelligent reasoning platform** capable of:
1. **Autonomous multi-step reasoning** via agentic sampling
2. **Evidence-backed analysis** via web search integration
3. **Collaborative workflows** via tool chaining
4. **Interactive refinement** via elicitation
5. **Production-grade reliability** via tasks and persistence
The implementation follows best practices with clear separation of concerns, extensible architecture, and comprehensive testing strategy.
---
## References
- [MCP 2025-11-25 Specification](https://modelcontextprotocol.io/specification/2025-11-25)
- [MCP First Anniversary Blog Post](http://blog.modelcontextprotocol.io/posts/2025-11-25-first-mcp-anniversary/)
- [WorkOS MCP 2025-11-25 Analysis](https://workos.com/blog/mcp-2025-11-25-spec-update)
- [Anthropic Code Execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp)
- [Agentic AI Foundation Announcement](https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation)
