What can you do with this server?

The Sequential Thinking Multi-Agent System server enhances LLM clients with advanced sequential thinking capabilities by orchestrating 6 specialized AI agents (Factual, Emotional, Critical, Optimistic, Creative, Synthesis) that analyze problems from diverse cognitive perspectives. Core Capabilities: * AI-powered routing: Automatically determines optimal processing strategies (Single, Double, Triple, or Full Agent sequences) based on problem complexity * Multi-perspective analysis: Each agent applies unique cognitive approaches for comprehensive problem assessment * Web research integration: Four agents conduct targeted research using ExaTools for current facts, counterexamples, success stories, and innovations * Sequential processing: Manages iterative thought sequences, revisions, branching into alternative approaches, and tracks progress through complex problems * Dual-model strategy: Uses Enhanced Models for complex synthesis tasks and Standard Models for individual agent processing * Multi-provider support: Works with DeepSeek, Groq, OpenRouter, Anthropic, GitHub Models, and Ollama * MCP integration: Extends LLM clients like Claude Desktop with sophisticated thinking capabilities via the sequentialthinking tool Ideal for philosophical, analytical, creative, and multi-faceted problems requiring deep analysis and comprehensive synthesis from multiple cognitive angles.

Which integrations are available for this server?

Supports configuration through environment variables, allowing secure storage of API keys for external services like DeepSeek and Exa. Enables robust data validation for thought steps in the sequential thinking process, ensuring input integrity before processing by the agent team. Leverages the Python AI/ML ecosystem for implementing the Multi-Agent System architecture, supporting advanced sequential thinking capabilities. Referenced as the language of the original implementation that this version evolved from, showing architectural progression from a simple state tracker to a Multi-Agent System.

How do I use Sequential Thinking Multi-Agent System?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Sequential Thinking Multi-Agent System analyze the pros and cons of implementing a four-day workweek" 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.

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Sequential Thinking Multi-Agent System

by FradSer

Overview Schema Related Servers Score Discussions

Python

Remote

Sequential Thinking Multi-Agent System (MAS)

Python Version Framework Twitter Follow

English | 简体中文

An MCP server that processes sequential thoughts through a team of specialized AI agents, each analyzing the problem from a different cognitive perspective.

What This Is

This is an MCP server, not a standalone application. It runs as a background service that extends an MCP-compatible LLM client (like Claude Desktop) with structured sequential-thinking capabilities. It exposes one tool, sequentialthinking, that runs every thought through a fixed multi-agent workflow: an initial synthesis, several specialist agents thinking in parallel, and a final synthesis that answers the original question.

Related MCP server: Sequential-Thinking

How It Works

The system uses a fixed full_exploration strategy for every request. The AI complexity analyzer still runs to record diagnostic metadata (complexity score, problem type, required thinking modes), but it no longer changes the execution path — all thoughts take the same route:

flowchart TD
    A[Input Thought] --> B[AI Complexity Analyzer]
    B --> C[Complexity Metadata Stored]
    C --> D[Fixed Strategy: full_exploration]
    D --> E[Step 1: Initial Synthesis]
    E --> F[Step 2: Parallel Specialist Agents]
    F --> G[Step 3: Final Synthesis]
    G --> H[Unified Response]

The Specialist Agents

Each request runs six specialist agents in parallel, plus a synthesis agent that runs twice (once at the start, once at the end). Every specialist except synthesis can optionally use web research via ExaTools.

Agent	Thinking direction	Focus	Time budget
Factual	`factual`	Objective facts and verified data	120s
Emotional	`emotional`	Intuition and gut reactions	30s
Critical	`critical`	Risks, weaknesses, logical flaws	120s
Optimistic	`optimistic`	Benefits, opportunities, value	120s
Creative	`creative`	New ideas and alternatives	240s
Meta-cognitive	`metacognitive`	Bias detection and reasoning-process evaluation	90s
Synthesis	`synthesis`	Integration and final answer	60s

Key properties:

Deterministic: every request runs the same multi-step path.
Parallel: the specialist agents run simultaneously with asyncio.gather.
Synthesis-driven: both orchestration and the final answer come from the synthesis agent, which uses the enhanced model.

Model Strategy

Two models are configured per provider:

Enhanced model: used by the synthesis agent (integration tasks).
Standard model: used by the specialist agents.

Research Capabilities

ExaTools is attached to every agent except synthesis. Research is optional — it activates only when EXA_API_KEY is set. Without it, the system works on pure reasoning.

The `sequentialthinking` Tool

The server exposes one MCP tool.

Input

{
  thought: string,               // One focused reasoning step
  thoughtNumber: number,         // 1-based step index; increment each call
  totalThoughts: number,         // Planned number of steps
  nextThoughtNeeded: boolean,    // true for intermediate steps, false on final step
  isRevision: boolean,           // true only when revising earlier conclusions
  branchFromThought?: number,    // Set with branchId to branch from a prior step
  branchId?: string,             // Branch identifier (required when branching)
  needsMoreThoughts: boolean     // true only when extending beyond totalThoughts
}

Output

{
  should_continue: boolean,      // Canonical continuation signal
  next_thought_number: number?,  // Recommended next thoughtNumber
  stop_reason: string,           // Why to continue/stop/retry
  current_thought_number: number,
  total_thoughts: number,
  next_call_arguments?: {        // Suggested next-call arguments when applicable
    thoughtNumber: number,
    totalThoughts: number,
    nextThoughtNeeded: boolean,
    needsMoreThoughts: boolean
  },
  parameter_usage: Record<string, string>
}

Call Contract

Treat this tool as a multi-step loop, not a one-shot call.
After every response, read structuredContent.should_continue.
Keep calling until should_continue is false.
Actively use reflection: when a step is weak or incorrect, send a revision step with isRevision=true.
Prefer structuredContent.next_thought_number and next_call_arguments when building the next request.

Supported Providers

Provider	Env var	Default enhanced model	Default standard model
DeepSeek (default)	`DEEPSEEK_API_KEY`	`deepseek-chat`	`deepseek-chat`
Groq	`GROQ_API_KEY`	`openai/gpt-oss-120b`	`openai/gpt-oss-20b`
OpenRouter	`OPENROUTER_API_KEY`	`deepseek/deepseek-chat-v3-0324`	`deepseek/deepseek-r1`
GitHub Models	`GITHUB_TOKEN`	`openai/gpt-5`	`openai/gpt-5-min`
Anthropic	`ANTHROPIC_API_KEY`	`claude-3-5-sonnet-20241022`	`claude-3-5-haiku-20241022`
Ollama	none	`devstral:24b`	`devstral:24b`

Installation

Prerequisites

Python 3.10+
An LLM API key from one of the providers above
Optional: EXA_API_KEY for web research
uv package manager (recommended) or pip

Install

git clone https://github.com/FradSer/mcp-server-mas-sequential-thinking.git
cd mcp-server-mas-sequential-thinking

uv pip install .        # or: pip install .

Configure an MCP Client

Add to your MCP client configuration:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "mcp-server-mas-sequential-thinking",
      "env": {
        "LLM_PROVIDER": "deepseek",
        "DEEPSEEK_API_KEY": "your_api_key",
        "EXA_API_KEY": "your_exa_key_optional"
      }
    }
  }
}

Environment Variables

# LLM provider (required)
LLM_PROVIDER="deepseek"  # deepseek, groq, openrouter, github, anthropic, ollama
DEEPSEEK_API_KEY="sk-..."

# Optional: override the models per provider (prefixed by provider name)
# DEEPSEEK_ENHANCED_MODEL_ID="deepseek-chat"
# DEEPSEEK_STANDARD_MODEL_ID="deepseek-chat"

# Optional: web research (enables ExaTools)
# EXA_API_KEY="your_exa_api_key"

# Optional: custom endpoint
# LLM_BASE_URL="https://custom-endpoint.com"

# Optional: team orchestration mode (standard/broadcast, route, coordinate)
# TEAM_MODE="standard"

Run the Server Directly

mcp-server-mas-sequential-thinking        # installed script
uv run mcp-server-mas-sequential-thinking  # or via uv

Development

# Install with dev dependencies
uv pip install -e ".[dev]"

# Code quality
uv run ruff check . --fix
uv run ruff format .
uv run mypy .

# Run tests
uv run pytest tests/

# Or use the Makefile
make test        # all tests with coverage + quality checks
make test-fast   # fast run without coverage
make check-all   # all quality checks

Test with MCP Inspector

npx @modelcontextprotocol/inspector uv run mcp-server-mas-sequential-thinking

Open http://127.0.0.1:6274/ and test the sequentialthinking tool.

Token Consumption Warning

The multi-agent architecture consumes significantly more tokens than a single-agent tool — roughly 5-10x more per sequentialthinking call, because every call invokes multiple specialist agents. The tradeoff is deeper, multi-perspective analysis.

Project Structure

mcp-server-mas-sequential-thinking/
├── src/mcp_server_mas_sequential_thinking/
│   ├── main.py                          # MCP server entry point (MCPServer)
│   ├── processors/
│   │   ├── multi_thinking_core.py       # Specialist agent definitions
│   │   └── multi_thinking_processor.py  # Parallel sequence execution
│   ├── routing/
│   │   ├── ai_complexity_analyzer.py    # AI complexity analysis
│   │   ├── complexity_types.py          # Complexity metric models
│   │   └── multi_thinking_router.py     # Fixed full_exploration routing
│   ├── services/
│   │   ├── server_core.py               # ThoughtProcessor implementation
│   │   ├── processing_orchestrator.py   # Agno Team orchestration
│   │   ├── workflow_executor.py
│   │   └── context_builder.py
│   ├── infrastructure/
│   │   ├── persistent_memory.py         # SQLite session storage
│   │   └── learning_resources.py        # Agent learning machine
│   ├── security/rate_limiter.py         # Rate limiting and request validation
│   └── config/
│       ├── modernized_config.py         # Provider strategies
│       └── constants.py                 # System constants
├── scripts/mcp_python_client_smoke.py   # Protocol smoke test
├── tests/                               # Unit and integration tests
├── pyproject.toml
└── Makefile

Changelog

See CHANGELOG.md for version history.

Contributing

Contributions are welcome. Please ensure:

Code follows the project style (ruff, mypy)
Commit messages use conventional commits format
All tests pass before submitting a PR
Documentation is updated as needed

License

This project does not yet declare a license. See the LICENSE discussion if you need to reuse it.

Acknowledgments

Built with Agno v2.x
Model Context Protocol by Anthropic
Research capabilities powered by Exa (optional)
Multi-dimensional thinking inspired by Edward de Bono's work

Support

GitHub Issues: Report bugs or request features
Documentation: see CLAUDE.md for implementation notes
MCP Protocol: Official MCP Documentation

Available Tools

1 tool

sequentialthinkingA

Multi-step sequential reasoning contract. Always treat this tool as iterative: after each response, read structuredContent.should_continue and continue calling until it is false. Actively use reflection: when a step reveals a flaw, explicitly send a revision step with isRevision=true. Input contract:

thought: one concrete reasoning step in natural language.
thoughtNumber: 1-based step index; increment by one for each new step.
totalThoughts: target number of steps for the current plan.
nextThoughtNeeded: true while the sequence should continue; false on final step.
isRevision: true only when revising an earlier conclusion.
branchFromThought + branchId: set together to explore an alternative branch.
needsMoreThoughts: true only when extending beyond totalThoughts. Output contract:
structuredContent.should_continue: canonical continuation signal.
structuredContent.next_thought_number: next recommended thoughtNumber.
structuredContent.stop_reason: canonical reason code for orchestration.

ParametersJSON Schema

Name	Required	Description
`thought`	Yes	Current reasoning step text. Keep this to one concrete step.
`thoughtNumber`	Yes	1-based sequence index for this thought. Increment for each step.
`totalThoughts`	Yes	Estimated total number of steps in the current reasoning plan.
`nextThoughtNeeded`	Yes	Set true when another thought follows this one. Set false on the final thought.
`isRevision`	Yes	Set true only when this step revises an earlier conclusion.
`branchFromThought`	Yes	Original thought number for branching. Null for the main path.
`branchId`	Yes	Branch identifier. Required when branchFromThought is not null.
`needsMoreThoughts`	Yes	Set true only when you must continue beyond totalThoughts.

Output Schema

ParametersJSON Schema

Name	Required	Description
`should_continue`	Yes	If true, call sequentialthinking again. The tool is designed for multi-step reasoning loops.
`next_thought_number`	No	Recommended thoughtNumber for the next call. Null means no next step is currently required.
`stop_reason`	Yes	Machine-readable reason that explains why to continue or stop.
`current_thought_number`	Yes	Echo of the current thoughtNumber after normalization.
`total_thoughts`	Yes	Echo of current totalThoughts after normalization.
`next_call_arguments`	No	Concrete argument recommendations for the next call when the current run completes successfully and should continue.
`parameter_usage`	Yes	Contract reminders for each core parameter to keep multi-step iterations consistent.

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries full burden. It discloses the iterative nature, revision mechanism, and output contract. It lacks explicit statements about side effects or auth but is sufficient for the intended reasoning use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (input contract, output contract) and front-loaded with the core concept. It is slightly lengthy but each sentence adds value, so it earns a 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 required params, output schema), the description covers the input and output contracts, usage pattern, and corner cases like revision and branching. It is nearly complete, lacking only examples or defaults, which are covered by the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 8 parameters. The description adds value by contextualizing parameter usage, such as grouping branchFromThought and branchId, and clarifying that needsMoreThoughts extends beyond totalThoughts.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is a 'Multi-step sequential reasoning contract' and explains its iterative, revision, and branching capabilities. It is very specific and distinguishes itself by detailing the contract-like behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs to treat the tool iteratively, read structuredContent.should_continue, and continue until false. It also specifies when to use revisions (when a flaw is revealed) and branching (with branchFromThought and branchId).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

1 tool updatev0.8.0Apr 29, 2026

Changedsequentialthinking31 fields changed

addedInput schema / properties / branchFromThought

Added value: +{
+  "anyOf": [
+    {
+      "minimum": 1,
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "description": "Original thought number for branching. Null for the main path.",
+  "title": "Branchfromthought"
+}

addedInput schema / properties / branchId

Added value: +{
+  "anyOf": [
+    {
+      "minLength": 1,
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "description": "Branch identifier. Required when branchFromThought is not null.",
+  "title": "Branchid"
+}

removedInput schema / properties / branch_from

Removed value: -{
-  "anyOf": [
-    {
-      "type": "integer"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Branch From"
-}

removedInput schema / properties / branch_id

Removed value: -{
-  "anyOf": [
-    {
-      "type": "string"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Branch Id"
-}

addedInput schema / properties / isRevision

Added value: +{
+  "description": "Set true only when this step revises an earlier conclusion.",
+  "title": "Isrevision",
+  "type": "boolean"
+}

removedInput schema / properties / is_revision

Removed value: -{
-  "default": false,
-  "title": "Is Revision",
-  "type": "boolean"
-}

addedInput schema / properties / needsMoreThoughts

Added value: +{
+  "description": "Set true only when you must continue beyond totalThoughts.",
+  "title": "Needsmorethoughts",
+  "type": "boolean"
+}

removedInput schema / properties / needs_more

Removed value: -{
-  "default": false,
-  "title": "Needs More",
-  "type": "boolean"
-}

addedInput schema / properties / nextThoughtNeeded

Added value: +{
+  "description": "Set true when another thought follows this one. Set false on the final thought.",
+  "title": "Nextthoughtneeded",
+  "type": "boolean"
+}

removedInput schema / properties / next_needed

Removed value: -{
-  "title": "Next Needed",
-  "type": "boolean"
-}

removedInput schema / properties / revises_thought

Removed value: -{
-  "anyOf": [
-    {
-      "type": "integer"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Revises Thought"
-}

addedInput schema / properties / thought / description

Added value: +"Current reasoning step text. Keep this to one concrete step."

addedInput schema / properties / thought / minLength
```
Added value: +1
```

addedInput schema / properties / thoughtNumber

Added value: +{
+  "description": "1-based sequence index for this thought. Increment for each step.",
+  "minimum": 1,
+  "title": "Thoughtnumber",
+  "type": "integer"
+}

removedInput schema / properties / thought_number

Removed value: -{
-  "title": "Thought Number",
-  "type": "integer"
-}

addedInput schema / properties / totalThoughts

Added value: +{
+  "description": "Estimated total number of steps in the current reasoning plan.",
+  "minimum": 1,
+  "title": "Totalthoughts",
+  "type": "integer"
+}

removedInput schema / properties / total_thoughts

Removed value: -{
-  "title": "Total Thoughts",
-  "type": "integer"
-}

changedInput schema / required

Previous value: -[
-  "thought",
-  "thought_number",
-  "total_thoughts",
-  "next_needed"
-]New value: +[
+  "thought",
+  "thoughtNumber",
+  "totalThoughts",
+  "nextThoughtNeeded",
+  "isRevision",
+  "branchFromThought",
+  "branchId",
+  "needsMoreThoughts"
+]

addedInput schema / title

Added value: +"sequentialthinkingArguments"

addedOutput schema / $defs

Added value: +{
+  "NextCallArguments": {
+    "description": "Recommended arguments for the next tool call.",
+    "properties": {
+      "needsMoreThoughts": {
+        "description": "Set to true only when you need to exceed totalThoughts and extend the sequence.",
+        "title": "Needsmorethoughts",
+        "type": "boolean"
+      },
+      "nextThoughtNeeded": {
+        "description": "Set to true when another step should follow the next call. Set to false on the final thought.",
+        "title": "Nextthoughtneeded",
+        "type": "boolean"
+      },
+      "thoughtNumber": {
+        "description": "Recommended thoughtNumber for the next tool call.",
+        "minimum": 1,
+        "title": "Thoughtnumber",
+        "type": "integer"
+      },
+      "totalThoughts": {
+        "description": "Recommended totalThoughts for the next tool call.",
+        "minimum": 1,
+        "title": "Totalthoughts",
+        "type": "integer"
+      }
+    },
+    "required": [
+      "thoughtNumber",
+      "totalThoughts",
+      "nextThoughtNeeded",
+      "needsMoreThoughts"
+    ],
+    "title": "NextCallArguments",
+    "type": "object"
+  },
+  "StopReason": {
+    "description": "Reason code for continuing or stopping thought iteration.",
+    "enum": [
+      "next_thought_required",
+      "needs_more_thoughts",
+      "thought_sequence_complete",
+      "validation_error",
+      "processing_error",
+      "rate_limited",
+      "request_too_large",
+      "unexpected_error"
+    ],
+    "title": "StopReason",
+    "type": "string"
+  }
+}

addedOutput schema / description

Added value: +"Structured control fields returned on every tool response."

addedOutput schema / properties / current_thought_number

Added value: +{
+  "description": "Echo of the current thoughtNumber after normalization.",
+  "minimum": 1,
+  "title": "Current Thought Number",
+  "type": "integer"
+}

addedOutput schema / properties / next_call_arguments

Added value: +{
+  "anyOf": [
+    {
+      "$ref": "#/$defs/NextCallArguments"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Concrete argument recommendations for the next call when the current run completes successfully and should continue."
+}

addedOutput schema / properties / next_thought_number

Added value: +{
+  "anyOf": [
+    {
+      "minimum": 1,
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Recommended thoughtNumber for the next call. Null means no next step is currently required.",
+  "title": "Next Thought Number"
+}

addedOutput schema / properties / parameter_usage

Added value: +{
+  "additionalProperties": {
+    "type": "string"
+  },
+  "description": "Contract reminders for each core parameter to keep multi-step iterations consistent.",
+  "title": "Parameter Usage",
+  "type": "object"
+}

removedOutput schema / properties / result

Removed value: -{
-  "title": "Result",
-  "type": "string"
-}

addedOutput schema / properties / should_continue

Added value: +{
+  "description": "If true, call sequentialthinking again. The tool is designed for multi-step reasoning loops.",
+  "title": "Should Continue",
+  "type": "boolean"
+}

addedOutput schema / properties / stop_reason

Added value: +{
+  "$ref": "#/$defs/StopReason",
+  "description": "Machine-readable reason that explains why to continue or stop."
+}

addedOutput schema / properties / total_thoughts

Added value: +{
+  "description": "Echo of current totalThoughts after normalization.",
+  "minimum": 1,
+  "title": "Total Thoughts",
+  "type": "integer"
+}

changedOutput schema / required

Previous value: -[
-  "result"
-]New value: +[
+  "should_continue",
+  "stop_reason",
+  "current_thought_number",
+  "total_thoughts",
+  "parameter_usage"
+]

changedOutput schema / title

Previous value: -"sequentialthinkingOutput"New value: +"SequentialThinkingStructuredContent"

1 tool updatev1.0.0Sep 22, 2025
- First observedsequentialthinking

Install Server

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TDQS

A4.7/5.0

Disambiguation5/5

Only one tool exists, so there is no possibility of ambiguity or confusion between tools.

Naming Consistency5/5

With a single tool, naming consistency is inherently perfect; the name clearly describes the action.

Tool Count5/5

One tool is appropriate for a focused multi-step reasoning system; the tool itself is complex and self-contained.

Completeness5/5

The tool covers the full sequential reasoning lifecycle including revision, branching, and extension, leaving no obvious gaps.

Maintenance

ActivityMaintained

ResponsivenessSyncing

Resources

GitHub Repository

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MCP directory API

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Sequential Thinking Multi-Agent System (MAS)

What This Is

How It Works

The Specialist Agents

Model Strategy

Research Capabilities

The sequentialthinking Tool

Input

Output

Call Contract

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Installation

Prerequisites

Install

Configure an MCP Client

Environment Variables

Run the Server Directly

Development

Test with MCP Inspector

Token Consumption Warning

Project Structure

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