Cognitive reasoning MCP server using DAG thought graphs with 10 strategies, metacognition, confidence scoring, and knowledge integration.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
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💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Deep Thinker.
thinkAdd a thought to the cognitive graph using a reasoning strategy.
evaluateEvaluate the thinking process with confidence scoring, critique, and graph health analysis.
metacogMetacognitive operations — monitor and control the thinking process.
graphQuery and visualize the thought graph.
prunePrune and optimize the thought graph.
resetReset the thought graph and start a fresh session, save, or resume a saved session.
Advanced cognitive thinking MCP server with DAG-based thought graph, 10 reasoning strategies (including auto-selection), 17 tools, node aliases, session persistence, structured responses, and intelligent error handling.
A significant evolution beyond sequential-thinking MCP, providing structured deep reasoning with graph-based thought management, schema validation, and intelligent strategy selection.
| Example | Strategy | Use Case |
|---|---|---|
| Architecture Decision | Dialectic + Parallel | Monolith vs microservices |
| Debugging Incident | Abductive | Production 500 errors |
| Feature Prioritization | Parallel + Dialectic | Q3 roadmap planning |
| Scientific Hypothesis | Analogical + Abductive | LNP delivery for CRISPR |
| Breaking Dead Ends | Metacognitive switch | Serverless cost analysis |
"last", "best", "root" instead of cryptic node IDs for any nodeId parameterMCPResponse JSON with status, summary, confidence, nextSuggested action~/.deep-thinker/sessions/; resume across MCP restarts with reset({ resume: "name" })"confidence 0 ile 1 arasında...")Add to your claude_desktop_config.json:
Or if installed globally:
The server communicates over stdio. Point your MCP client to the deep-thinker command or node path/to/dist/index.js.
All tool responses follow the MCPResponse structure:
The nextSuggested field always recommends the next logical step, making it easy to chain tool calls without guessing.
Instead of looking up cryptic node IDs, use aliases for any nodeId, parentId, or targetId parameter:
| Alias | Resolves To |
|---|---|
"last" | Most recently added node (insertion order) |
"best" | Node with highest confidence score |
"root" | First node with no incoming edges |
Thought graphs are automatically saved after every think call. Sessions are stored in ~/.deep-thinker/sessions/.
thinkAdd a thought to the cognitive graph using a reasoning strategy.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
content | string | Yes | The thought content |
type | string | No | Thought type: hypothesis, analysis, evidence, conclusion, question, assumption, insight, critique, synthesis, observation |
strategy | string | No | Strategy: sequential, dialectic, parallel, analogical, abductive, first_principles, counterfactual, systems_thinking, mcts, auto |
confidence | number | No | Initial confidence 0-1 (default: 0.5) |
parentId | string | No | Parent node ID or alias (default: last leaf) |
branch | string | No | Branch name for parallel exploration |
tags | string[] | No | Tags for categorization |
edgeTo | object | No | Explicit edge: { targetId, type } |
dialectic | object | No | Dialectic mode: { thesis, antithesis?, synthesis? } |
parallel | array | No | Parallel mode: [{ content, type, confidence }] |
analogical | object | No | Analogical mode: { sourceDomain, mapping, projectedConclusion } |
abductive | object | No | Abductive mode: { observation, explanations[], bestExplanation? } |
firstPrinciples | object | No | First Principles mode: { problem, assumptions?, depth?, domain? } |
counterfactual | object | No | Counterfactual mode: { currentState?, variablesToChange, rippleDepth? } |
systemsThinking | object | No | Systems Thinking mode: { systemDescription?, components, focusArea? } |
mcts | object | No | MCTS mode: { problem?, possibleActions, numSimulations? } |
knowledge | object | No | Attach knowledge: { source, content, relevance } |
Strategy details:
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