Skip to main content
Glama
coachgruden

Sequential Thinking MCP Server

by coachgruden
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.6.2

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    Single tool, so naming consistency is perfect. The name 'sequentialthinking' is descriptive and follows a single convention.

    Tool Count3/5

    A single tool for a server focused on complex sequential thinking feels thin. Even though the tool is comprehensive, the count is at the lower boundary of acceptability.

    Completeness4/5

    The tool covers many aspects of sequential thinking: problem analysis, revision, branching, hypothesis generation, and verification. Minor gaps might exist in explicit state management, but overall it is quite complete for its domain.

  • Average 4.7/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Inno Setup License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    Annotations indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about the tool's adaptive and reflective nature, such as questioning previous thoughts and generating hypotheses. It does not contradict annotations and enriches understanding.

    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 long but well-structured with sections, bullet points, and clear headings. It is front-loaded with purpose and use cases. However, it could be slightly more concise without losing essential information.

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

    Completeness5/5

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

    Given the tool's complexity, the description covers all necessary aspects: purpose, when to use, key features, parameter explanations, and step-by-step guidance. The presence of an output schema further supports completeness.

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

    Parameters5/5

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

    Schema coverage is 100%, but the description adds significant meaning beyond the schema by explaining each parameter in context (e.g., what 'thought' can include like revisions or hypothesis generation). It provides usage examples and clarifies the role of each parameter.

    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 for dynamic and reflective problem-solving through thoughts. It lists specific use cases like breaking down complex problems and planning with room for revision, distinguishing it as a thinking tool even though no siblings exist.

    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 provides a detailed 'When to use this tool' section and 'You should' steps, offering clear context and instructions for when to apply the tool. It does not explicitly state when not to use it, but the guidance is comprehensive.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

sequential-thinking-mcp-sse MCP server

Copy to your README.md:

Score Badge

sequential-thinking-mcp-sse MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/coachgruden/sequential-thinking-mcp-sse'

If you have feedback or need assistance with the MCP directory API, please join our Discord server