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Sequential Thinking MCP Server

by arben-adm
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Python
Remote

Sequential Thinking MCP Server

MCP Toplist

A Model Context Protocol (MCP) server providing a structured thinking journal: schema-validated thoughts, an append-only audit trail, structural analysis, and session export/import. It records and organizes a thinking process through defined stages — it does not evaluate, generate, or improve the reasoning itself; that stays with whatever model is calling it.

Python Version License: MIT Code Style: Ruff

Features

  • Structured Thinking Framework: Organizes thoughts through standard cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), with warnings (or, in --strict-stages mode, rejection) when a thought skips or backtracks a stage

  • Revisions & Branching: Revise earlier thoughts or fork alternative lines of reasoning, with revision- and branch-aware analysis and summaries

  • Thought Tracking: Records and manages sequential thoughts with metadata as a structured, typed audit trail (structured_content on every tool response)

  • Related Thought Analysis: Finds thoughts that are lexically similar to the current one, independent of stage, plus a separate same-tag/same-stage grouping — a categorical signal, not a claim of semantic relevance

  • Progress Monitoring: Explicit mainline position, total recorded thoughts, branch count, and revision count — not a single ambiguous percentage

  • Summary Generation: Extracts the actual recorded thinking (per-stage excerpts, aggregated challenged assumptions, open branches, revision chains) alongside structural statistics — a deterministic extraction, not new reasoning

  • Persistent Storage: Append-only JSONL session log with thread-safety and automatic crash recovery

  • Data Import/Export: Share and reuse thinking sessions

  • Extensible Architecture: Easily customize and extend functionality

  • Robust Error Handling: Protocol/validation errors (bad stage, duplicate thought number, path traversal) fail the call outright; execution errors the caller can adapt to come back as a normal tool result

  • Type Safety: Comprehensive type annotations (mypy --strict clean) and Pydantic validation, including declared output schemas for every tool

Related MCP server: Sequential Thinking MCP Server

Prerequisites

Key Technologies

  • Pydantic: For data validation, serialization, and structured tool output schemas

  • Portalocker: For thread-safe file access

  • MCP Python SDK 2.x (mcp.server.mcpserver.MCPServer): For Model Context Protocol integration

Project Structure

mcp-sequential-thinking/
├── mcp_sequential_thinking/
│   ├── server.py       # Main server implementation and MCP tools
│   ├── models.py       # Data models with Pydantic validation
│   ├── storage.py      # Thread-safe persistence layer
│   ├── storage_utils.py # Shared utilities for storage operations
│   ├── analysis.py     # Thought analysis and pattern detection
│   ├── utils.py        # Common utilities and helper functions
│   ├── logging_conf.py # Centralized logging configuration
│   └── __init__.py     # Package initialization
├── tests/              
│   ├── test_analysis.py # Tests for analysis functionality
│   ├── test_models.py   # Tests for data models
│   ├── test_storage.py  # Tests for persistence layer
│   └── __init__.py
├── run_server.py       # Server entry point script
├── debug_mcp_connection.py # Utility for debugging connections
├── README.md           # Main documentation
├── CHANGELOG.md        # Version history and changes
├── example.md          # Customization examples
├── LICENSE             # MIT License
└── pyproject.toml      # Project configuration and dependencies

Quick Start

The package is published on PyPI as mcp-sequential-thinking. The easiest way to run it is via uvx — no install step needed:

uvx mcp-sequential-thinking

Or install it permanently:

pip install mcp-sequential-thinking
mcp-sequential-thinking

Development Setup

To work on the code, clone the repository and set it up from source:

  1. Set Up Project

    # Create and activate virtual environment
uv venv
.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Unix

# Install package and dependencies
uv pip install -e .

# For development with testing tools
uv pip install -e ".[dev]"

# For all optional dependencies
uv pip install -e ".[all]"

  2. Run the Server

    # Run directly
uv run -m mcp_sequential_thinking.server

# Or use the installed script
mcp-sequential-thinking

  3. Run Tests

    # Run all tests
pytest

# Run with coverage report
pytest --cov=mcp_sequential_thinking

Claude Desktop Integration

Add to your Claude Desktop configuration:

  • Linux: ~/.config/Claude/claude_desktop_config.json

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Option 1: Using uvx with the PyPI package (recommended)

No clone, no venv, no manual updates — uvx fetches the package from PyPI and runs it:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

To test unreleased changes, point uvx at the repository instead:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/arben-adm/mcp-sequential-thinking",
        "mcp-sequential-thinking"
      ]
    }
  }
}

Option 2: Using the installed entry point

If you've installed the package with pip install mcp-sequential-thinking (or pip install -e . from a clone):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "mcp-sequential-thinking"
    }
  }
}

Option 3: Using a local clone's virtual environment (development)

If you have set up the project with uv venv && uv pip install -e ., point directly to the venv Python interpreter. This avoids dependency resolution issues (e.g., on systems with Python 3.14+):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "/path/to/mcp-sequential-thinking/.venv/bin/python",
      "args": [
        "-m",
        "mcp_sequential_thinking.server"
      ],
      "cwd": "/path/to/mcp-sequential-thinking"
    }
  }
}

Option 4: Using uv run on a local clone (development)

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/mcp-sequential-thinking",
        "-m",
        "mcp_sequential_thinking.server"
      ]
    }
  }
}

Editor & IDE Integration

Cursor

Add to your Cursor MCP configuration at .cursor/mcp.json in your project root (or globally at ~/.cursor/mcp.json):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

VS Code (Copilot MCP)

VS Code supports MCP servers since version 1.99+. Add to .vscode/mcp.json in your workspace or to your user settings.json:

{
  "servers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Note: Enable MCP support in VS Code via "chat.mcp.enabled": true in your settings.

Zed

Add to your Zed settings (~/.config/zed/settings.json):

{
  "context_servers": {
    "sequential-thinking": {
      "command": {
        "path": "uvx",
        "args": ["mcp-sequential-thinking"]
      }
    }
  }
}

Claude Code (CLI)

Add the server using the CLI:

claude mcp add sequential-thinking -- uvx mcp-sequential-thinking

Or manually create/edit .mcp.json in your project root:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Windsurf

Add to your Windsurf MCP configuration at ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Gemini CLI

Add to your Gemini CLI settings at ~/.gemini/settings.json:

{
  "mcpServers": {
    "sequential-thinking": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-sequential-thinking"],
      "env": {}
    }
  }
}

Tip: All editor configurations above run the published PyPI package via uvx. To run from a local clone instead (e.g. for development), use uv run --directory /path/to/mcp-sequential-thinking -m mcp_sequential_thinking.server or point directly to the venv Python interpreter (see Claude Desktop Options 3 and 4).

How It Works

The server maintains a history of thoughts and processes them through a structured workflow. Each thought is validated using Pydantic models, categorized into thinking stages, and stored with relevant metadata in a thread-safe storage system. The server automatically handles data persistence, backup creation, and provides tools for analyzing relationships between thoughts.

Sessions are persisted as an append-only JSONL log at ~/.mcp_sequential_thinking/current_session.jsonl (override the directory with the MCP_STORAGE_DIR environment variable). Each process_thought call appends a single fsynced line, so the file doubles as an audit trail and a truncated final line from an interrupted write is recovered automatically. Sessions from v0.5.x (current_session.json) are migrated losslessly on first start; the original file is kept as current_session.json.migrated-to-v2.

Usage Guide

The Sequential Thinking server exposes five main tools:

1. process_thought

Records and analyzes a new thought in your sequential thinking process.

Parameters:

  • thought (string): The content of your thought

  • thought_number (integer): Position in your sequence (e.g., 1 for first thought)

  • total_thoughts (integer): Expected total thoughts in the sequence

  • next_thought_needed (boolean): Whether more thoughts are needed after this one

  • stage (string): The thinking stage - must be one of:

    • "Problem Definition"

    • "Research"

    • "Analysis"

    • "Synthesis"

    • "Conclusion"

  • tags (list of strings, optional): Keywords or categories for your thought

  • axioms_used (list of strings, optional): Principles or axioms applied in your thought

  • assumptions_challenged (list of strings, optional): Assumptions your thought questions or challenges

  • is_revision (boolean, optional): Whether this thought revises an earlier one

  • revises_thought_number (integer, optional): The number of the earlier thought being revised (required together with is_revision)

  • branch_from_thought (integer, optional): The thought number to fork from when exploring an alternative path

  • branch_id (string, optional): Identifier for the branch (letters, digits, -, _; max 64 characters; requires branch_from_thought)

Example:

# First thought in a 5-thought sequence
process_thought(
    thought="The problem of climate change requires analysis of multiple factors including emissions, policy, and technology adoption.",
    thought_number=1,
    total_thoughts=5,
    next_thought_needed=True,
    stage="Problem Definition",
    tags=["climate", "global policy", "systems thinking"],
    axioms_used=["Complex problems require multifaceted solutions"],
    assumptions_challenged=["Technology alone can solve climate change"],
)

# Revise an earlier thought
process_thought(
    thought="Framing the problem purely around emissions was too narrow; adaptation matters equally.",
    thought_number=6,
    total_thoughts=6,
    next_thought_needed=True,
    stage="Problem Definition",
    is_revision=True,
    revises_thought_number=1,
)

# Fork an alternative line of reasoning
process_thought(
    thought="What if we approach this from a market-incentive angle instead?",
    thought_number=7,
    total_thoughts=7,
    next_thought_needed=True,
    stage="Analysis",
    branch_from_thought=3,
    branch_id="market-incentives",
)

2. generate_summary

Generates a summary of your entire thinking process.

Example output:

{
  "summary": {
    "totalThoughts": 5,
    "stages": {
      "Problem Definition": 1,
      "Research": 1,
      "Analysis": 1,
      "Synthesis": 1,
      "Conclusion": 1
    },
    "timeline": [
      {"number": 1, "stage": "Problem Definition"},
      {"number": 2, "stage": "Research"},
      {"number": 3, "stage": "Analysis"},
      {"number": 4, "stage": "Synthesis"},
      {"number": 5, "stage": "Conclusion"},
      {"number": 6, "stage": "Problem Definition", "isRevision": true},
      {"number": 7, "stage": "Analysis", "branchId": "market-incentives"}
    ],
    "branches": {
      "market-incentives": {"fromThought": 3, "thoughtCount": 1}
    },
    "revisionCount": 1
  }
}

3. clear_history

Resets the thinking process by clearing all recorded thoughts.

4. export_session

Exports the current thinking session to a JSON file for sharing or backup.

Parameters:

  • file_path (string): Path to the output JSON file. Since v0.6.0, exports are confined to the exports/ subdirectory of the storage directory; relative paths resolve to ~/.mcp_sequential_thinking/exports/ and parent directories are created automatically.

Example:

export_session(file_path="my-analysis.json")
# -> written to ~/.mcp_sequential_thinking/exports/my-analysis.json

5. import_session

Imports a previously exported thinking session from a JSON file. Exports created with v0.5.x remain importable.

Parameters:

  • file_path (string): Path to the JSON file to import. Like exports, resolved inside the exports/ subdirectory of the storage directory.

Comparison to the official sequential-thinking server

The official MCP sequential-thinking server provides the core paradigm: numbered thoughts with revisions and branching, held in memory for the duration of the process. This server implements the same paradigm and adds:

  • Persistence: sessions survive restarts (append-only JSONL log with crash recovery and automatic migration), and can be exported, shared and re-imported as JSON.

  • Thinking stages: thoughts are categorized into cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), enabling stage-based filtering and completeness checks.

  • Analysis: related-thought detection via stages and tags, per-thought progress, and rich summaries including branch and revision statistics.

If you only need ephemeral chain-of-thought scaffolding, the official server is a lighter choice; if you want durable, analyzable thinking sessions, this one is built for that.

Practical Applications

  • Decision Making: Work through important decisions methodically

  • Problem Solving: Break complex problems into manageable components

  • Research Planning: Structure your research approach with clear stages

  • Writing Organization: Develop ideas progressively before writing

  • Project Analysis: Evaluate projects through defined analytical stages

Getting Started

With the proper MCP setup, simply use the process_thought tool to begin working through your thoughts in sequence. As you progress, you can get an overview with generate_summary and reset when needed with clear_history.

Customizing the Sequential Thinking Server

For detailed examples of how to customize and extend the Sequential Thinking server, see example.md. It includes code samples for:

  • Modifying thinking stages

  • Enhancing thought data structures with Pydantic

  • Adding persistence with databases

  • Implementing enhanced analysis with NLP

  • Creating custom prompts

  • Setting up advanced configurations

  • Building web UI integrations

  • Implementing visualization tools

  • Connecting to external services

  • Creating collaborative environments

  • Separating test code

  • Building reusable utilities

License

MIT License

Available Tools

5 tools
clear_historyB

Clear the thought history.

Returns:
    dict: Status message
ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.3/5.0
Behavior2/5

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

No annotations exist, so the description must disclose all behavioral traits. It only states the action and return type, omitting details like destructiveness, scope, or confirmation requirements.

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 concise at two sentences, front-loading the key action. While it lacks depth, it contains no superfluous information.

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

Completeness2/5

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

Despite the tool's simplicity, the description is incomplete. It does not mention that clearing history is irreversible or provide any behavioral context, especially given the lack of annotations and output 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?

The input schema has zero parameters, so the baseline is 4. The description does not need to add parameter information since none exist.

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 explicitly states 'Clear the thought history,' which matches the tool name 'clear_history.' The verb 'clear' and resource 'thought history' are clear and distinct from sibling tools like export_session or process_thought.

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

Usage Guidelines2/5

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 prerequisites, side effects, or context for clearing history.

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

export_sessionB

Export the current thinking session to a file.

Args:
    file_path: Path to save the exported session

Returns:
    dict: Status message
ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYes

TDQS

B3.2/5.0
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose side effects, file overwrite behavior, or access permissions; merely states the action and returns 'Status message' without detail.

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?

Two sentences plus structured Args/Returns sections, clear and front-loaded; no unnecessary text.

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

Completeness3/5

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

Adequate for a simple tool with one parameter and no output schema, but lacks information on file format, overwrite behavior, or status message contents.

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

Parameters3/5

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

Schema coverage is 0%, but the description adds 'Path to save the exported session' for file_path, clarifying its purpose beyond the schema's type definition.

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 uses a specific verb ('Export') and resource ('current thinking session') with destination ('to a file'), clearly distinguishing from siblings like import_session or generate_summary.

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

Usage Guidelines2/5

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; lacks context on prerequisites or situations like saving vs sharing.

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

generate_summaryB

Generate a summary of the entire thinking process.

Returns:
    dict: Summary of the thinking process
ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

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

No annotations exist, and the description does not disclose behavioral traits (e.g., whether it is a read-only operation, requires state, or has side effects). It only states it returns a dict, which 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.

Conciseness5/5

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

The description is extremely concise with two short sentences, no unnecessary words, and the key information is front-loaded.

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

Completeness2/5

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

Given no output schema and sibling tools, the description lacks details about what the summary contains, how it is generated, or any dependencies. It feels incomplete for a tool that produces a significant output.

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?

There are no parameters, so schema coverage is trivially 100%. The description adds meaning by specifying the output is a summary of the thinking process, which is helpful beyond an empty schema.

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 action ('Generate') and the resource ('summary of the entire thinking process'). It is a specific verb+resource combination that distinguishes it from sibling tools like 'clear_history', 'export_session', etc.

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

Usage Guidelines2/5

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 prerequisites, context, or exclusions.

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

import_sessionC

Import a thinking session from a file.

Args:
    file_path: Path to the file to import

Returns:
    dict: Status message
ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYes

TDQS

C2.6/5.0
Behavior2/5

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 'Import a thinking session from a file' without mentioning side effects (e.g., overwriting current session), file requirements, or error behavior.

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

Conciseness3/5

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

The description is brief (three lines) and uses a standard Args/Returns structure. However, it is too terse to be fully effective, lacking essential details.

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

Completeness2/5

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

Given the absence of an output schema, the description only vaguely states 'dict: Status message'. It does not explain what the status indicates or what happens to the existing session, leaving the agent uninformed.

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

Parameters2/5

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

The input schema has 0% description coverage, so the description must compensate. It merely repeats 'Path to the file to import', adding no detail about file format, size limits, or path constraints.

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

Purpose4/5

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

The description clearly states the action (import) and the object (thinking session from a file). It is distinguishable from sibling tools like export_session, but lacks specifics on file format or source.

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

Usage Guidelines2/5

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 context on prerequisites 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.

process_thoughtB

Add a sequential thought with its metadata.

Args:
    thought: The content of the thought
    thought_number: The sequence number of this thought
    total_thoughts: The total expected thoughts in the sequence
    next_thought_needed: Whether more thoughts are needed after this one
    stage: The thinking stage (Problem Definition, Research, Analysis, Synthesis, Conclusion)
    tags: Optional keywords or categories for the thought
    axioms_used: Optional list of principles or axioms used in this thought
    assumptions_challenged: Optional list of assumptions challenged by this thought
    is_revision: Whether this thought revises an earlier thought
    revises_thought_number: The number of the earlier thought being revised (required if is_revision is true)
    branch_from_thought: The thought number this thought branches from, to explore an alternative path
    branch_id: Identifier for the branch (letters, digits, '-', '_'; max 64 chars; requires branch_from_thought)
    ctx: Optional MCP context object

Returns:
    dict: Analysis of the processed thought
ParametersJSON Schema
NameRequiredDescriptionDefault
ctxNo
tagsNo
stageYes
thoughtYes
branch_idNo
axioms_usedNo
is_revisionNo
thought_numberYes
total_thoughtsYes
branch_from_thoughtNo
next_thought_neededYes
assumptions_challengedNo
revises_thought_numberNo

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are present, so the description must disclose all behavioral traits. It details what the tool does but omits side effects, permission requirements, error handling, or the state modifications (e.g., appending to a thought list). The return value is only vaguely described as 'dict: Analysis of the processed thought.'

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 front-loaded with a one-line summary, then structured as a docstring with Args and Returns. It is reasonably concise, though the parameter list is lengthy. Every sentence adds value, but could be more compact.

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

Completeness3/5

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

Given the tool's complexity (13 parameters, no output schema, no annotations), the description explains each parameter but lacks guidance on the overall workflow (e.g., sequential numbering, when to set 'next_thought_needed'). The stage values are enumerated, but the return value and error conditions are unspecified.

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

Parameters3/5

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

The input schema has no property descriptions (0% coverage), so the description must compensate. The Args list provides brief explanations for each parameter, but these mostly restate the parameter names (e.g., 'thought: The content of the thought') without adding deeper semantics, constraints, or examples.

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 opens with 'Add a sequential thought with its metadata,' which clearly states the action and resource. This distinguishes it from sibling tools (clear_history, export_session, generate_summary, import_session) which serve different purposes.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives. The usage is implied by the tool name and sibling context, but no exclusion criteria or when-not scenarios are provided.

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. 5 tool updatesv1.0.1
    • First observedclear_history
    • First observedexport_session
    • First observedgenerate_summary
    • First observedimport_session
    • First observedprocess_thought
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TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a distinct purpose: clearing history, exporting/importing sessions, generating summaries, and processing thoughts. No functional overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., clear_history, export_session). The naming is predictable and clear.

Tool Count5/5

Five tools is appropriate for a focused sequential thinking server, covering core operations without unnecessary bloat.

Completeness4/5

The tool surface covers the full lifecycle: adding thoughts (with revision and branching), clearing, exporting/importing, and generating summaries. Minor gap: lack of a dedicated edit/delete tool, but revisions handle edits.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

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