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rayss868

Systematic Reasoning AI MCP Server

by rayss868

revert_reasoning_transaction

Undo a reasoning cycle by removing its ticket and learning logs, restoring a clean workspace state after corruption or unwanted changes.

Instructions

Reverts a reasoning transaction, removing the ticket and any associated learning logs. Use this to recover from a corrupted or unwanted state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspace_pathYesThe absolute path of the project's workspace directory.
reasoning_ticket_idYesThe UUID of the reasoning cycle to revert.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the destructive effect: 'removing the ticket and any associated learning logs.' It does not mention irreversibility or permissions, but the core destructive behavior is clearly disclosed.

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?

Two sentences with no wasted words. The first sentence states the action and effect, and the second gives the use case. Every sentence contributes useful information.

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?

The description explains what the tool does, what it removes, and when to use it, which is sufficient for a simple two-parameter destructive operation. It does not describe return values or failure modes, but no output schema is present and those are not essential for invoking this tool correctly.

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 covers both parameters fully at 100% coverage with clear descriptions. The tool description adds no parameter-specific detail beyond referring to the reasoning ticket, so the baseline score of 3 is appropriate.

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 names a specific verb ('Reverts') and resource ('a reasoning transaction'), and clarifies the concrete effect: removing the ticket and any associated learning logs. This clearly distinguishes it from sibling tools like search_learnings, set_reasoning_budget, and log_reasoning_reflection.

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

Usage Guidelines4/5

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

'Use this to recover from a corrupted or unwanted state' provides explicit context for when the tool should be invoked. It does not name alternatives or exclusions, but the sibling tools are sufficiently different in purpose that no confusion is likely.

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

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