Systematic Reasoning AI MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| set_reasoning_budgetC | Takes a task description and (optionally) a token budget, then returns a string instruction for an AI to reason adaptively. |
| log_reasoning_reflectionA | Logs the outcome of a reasoning cycle to the memory log. |
| search_learningsA | Searches the learning bank for past reflections within the current project. |
| revert_reasoning_transactionA | Reverts a reasoning transaction, removing the ticket and any associated learning logs. Use this to recover from a corrupted or unwanted state. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: searching learnings, reverting transactions, setting budgets, and logging reflections. There is no meaningful overlap or confusion between these operations.
All tools follow the same snake_case verb_noun pattern: search_learnings, revert_reasoning_transaction, set_reasoning_budget, log_reasoning_reflection. Naming is predictable and consistent across the entire server.
Four tools is a well-scoped count for a focused meta-cognition server. Each tool earns its place by covering a distinct part of the reasoning workflow without unnecessary bloat.
The server covers search, logging, budget-setting, and rollback, which form a coherent reasoning support system. A minor gap exists in that there is no explicit tool for starting or listing reasoning transactions, though the AI may maintain that context externally.