Uses an Ollama-hosted model (e.g., qwen2.5-coder) to evaluate the semantic plausibility of thoughts against provided grounding context, rejecting hallucinated or drifted thoughts.
Persists thought history, branches, and revisions to a PostgreSQL database, enabling an auditable DAG of reasoning and state persistence across sessions.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@krusch-sequential-mcp Think step by step: why is the app slow? Use grounding context: 'Database is PostgreSQL.'"
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.
The standard sequential-thinking MCP provides a great tool for chain-of-thought reasoning, but it suffers from the "Telephone Game" problem in multi-agent environments, where agents can confidently hallucinate ungrounded thoughts that poison the context window.
krusch-sequential-mcp solves this by introducing Semantic Plausibility Gating alongside a highly reliable DBOS PostgreSQL persistence layer.
🧠 Semantic Plausibility Gating: Autonomously rejects drifted or hallucinated thoughts via an edge model evaluator.
💾 DBOS PostgreSQL Persistence: Synchronously persists every thought, branch, and revision into a dbos_thoughts table, creating an auditable DAG of reasoning.
🛑 Deterministic State Reliability: Halts poisoned thought execution, forcing agents to re-evaluate their reasoning path.
🔌 Drop-In Replacement: Fully compatible with the standard sequential-thinking interface while supporting the new groundingContext parameter.
📦 Zero External Dependencies: The plausibility evaluator is fully self-contained — no external toolkit required.
Related MCP server: Tyra Advanced Memory MCP Server
When an agent proposes a thought, the internal evaluator screens it against the provided groundingContext.
graph TD;
A[Agent Thought Proposed] --> B{Grounding Context Provided?};
B -- No --> C[Accept & Persist to DBOS];
B -- Yes --> D[Edge Model Evaluator];
D -- Plausible --> C;
D -- Hallucinated/Drifted --> E[Reject Thought];
E --> F[Return Soft Error to Agent];
F --> G[Agent Re-evaluates];npm install -g krusch-sequential-mcpOr configure it in your MCP settings file (e.g., claude_desktop_config.json or .cursor/mcp.json):
{
"mcpServers": {
"krusch-sequential-mcp": {
"command": "npx",
"args": ["-y", "krusch-sequential-mcp"]
}
}
}Agents can invoke the sequentialthinking tool with the standard parameters (thought, thoughtNumber, totalThoughts, nextThoughtNeeded, etc.).
To engage the plausibility gate, include the groundingContext parameter in your tool call:
{
"thought": "Since the user is asking about the database schema, I will assume it uses MongoDB and write a query for it.",
"thoughtNumber": 1,
"totalThoughts": 3,
"nextThoughtNeeded": true,
"groundingContext": "The current codebase exclusively uses DBOS PostgreSQL for persistence. No NoSQL databases are present."
}Because the thought conflicts with the groundingContext, the evaluator will autonomously reject it, returning an error to the agent to rethink its approach.
Variable | Required | Default | Description |
| No | (none — persistence disabled) | PostgreSQL connection string (e.g., |
| No |
| Base URL to the Ollama service for plausibility checks. |
| No |
| Ollama model used for plausibility screening. Should be a small, fast model. |
Copy .env.example for a quick start:
cp .env.example .envWe welcome contributions! Please ensure your tests pass and adhere to the project formatting standards.
Run tests via npm run build and npm start (or node build/index.js).
MIT License © 2026 kruschdev
A detailed tool for dynamic and reflective problem-solving through thoughts. Augmented with Semantic Plausibility Gating.
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | Your current thinking step | |
| branchId | No | Branch identifier | |
| isRevision | No | Whether this revises previous thinking | |
| thoughtNumber | Yes | Current thought number | |
| totalThoughts | Yes | Estimated total thoughts needed | |
| revisesThought | No | Which thought is being reconsidered | |
| groundingContext | No | OPTIONAL: Provide the source context for this thought. The server will independently verify the plausibility of your thought against this context. | |
| branchFromThought | No | Branching point thought number | |
| needsMoreThoughts | No | If more thoughts are needed | |
| nextThoughtNeeded | Yes | Whether another thought step is needed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It mentions 'Semantic Plausibility Gating' which hints at a verification mechanism, but does not explain how it works, what data is stored, or any side effects. The schema's groundingContext field description provides some behavior, but the main description is insufficient for an agent to understand the tool's operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short at one sentence, which is lean but not necessarily well-structured. It front-loads the core concept, but the single sentence lacks detail that could be organized in a more informative way. It is not overly verbose, but it doesn't make effective use of its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 10 parameters and no output schema, the description is inadequate. It doesn't explain the workflow (e.g., how to sequence thoughts, the meaning of branchId, revision, nextThoughtNeeded), nor does it describe the plausibility gating behavior beyond naming it. The schema helps but the description leaves the agent without context on how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 10 parameters have schema descriptions, so the baseline is 3. The main description adds no parameter-specific meaning, and it doesn't mention any relationships between params. However, given 100% schema coverage, the schema itself is sufficient for understanding parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description describes the tool as 'for dynamic and reflective problem-solving through thoughts', which conveys the general domain but lacks a specific action verb (e.g., submit, record) or resource. The name 'sequentialthinking' partially compensates, but the description alone doesn't clarify what the tool does beyond generic problem-solving.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 other tools. No alternatives are mentioned, and no context is provided about the appropriate use case. The description simply states what it is without indications of when it should be preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0sequentialthinkingOnly one tool exists, so there is no possibility of confusion or overlap. The tool's purpose is singular and clear.
With a single tool, naming consistency is trivially satisfied. The name 'sequentialthinking' is descriptive and matches the server's focus.
The server has exactly one tool, which feels minimal. While it is appropriate for a focused sequential thinking utility, the count is on the borderline of being too thin.
The tool appears to provide a comprehensive capability for sequential thinking and problem-solving. However, being the only tool, there may be missing auxiliary operations like reset or history, though no obvious gaps are evident.
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