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rayss868

Systematic Reasoning AI MCP Server

by rayss868

log_reasoning_reflection

Log a reasoning cycle's outcome and lesson learned to create a searchable memory, enabling future tasks to reference past successes and failures.

Instructions

Logs the outcome of a reasoning cycle to the memory log.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe original task description.
outcomeYesThe outcome of the task.
learningYesA concise lesson learned from the reasoning cycle.
workspace_pathYesThe absolute path of the project's workspace directory.
reasoning_ticket_idYesThe UUID of the reasoning cycle to complete.

Schema Changelog

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

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It clearly signals a write/logging side effect to the memory log, which is useful, but it does not disclose whether the log is append-only, whether it finalizes the reasoning ticket, or whether it can be reverted. The existence of revert_reasoning_transaction suggests transaction semantics that the description leaves unstated.

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 a single front-loaded sentence with no redundant words or filler. Every part contributes to identifying the action, the subject, and the destination, making it concise without being vague.

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?

The schema fully documents all required parameters, including the success/failure enum, so the tool is invocable from structured data alone. However, with no output schema and no annotations, the description leaves return behavior and transaction lifecycle implied, and it does not mention how this tool relates to revert_reasoning_transaction. It is adequate but not fully complete.

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 description coverage is 100%, so all five parameters are already documented with descriptions and the outcome parameter has an enum. The description only conceptually maps to the parameters ('outcome of a reasoning cycle') and adds no parameter-level meaning beyond what the schema provides. Baseline 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 uses a specific verb ('Logs') and identifies both the resource ('outcome of a reasoning cycle') and the destination ('memory log'), so the tool's function is immediately clear. This distinguishes it from sibling tools such as search_learnings (read-only) and revert_reasoning_transaction (undo), even without naming them.

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 implies the tool is used after a reasoning cycle to persist an outcome and lesson, but it never states when to use it explicitly or contrasts it with alternatives like search_learnings or revert_reasoning_transaction. An agent can infer timing from the name and schema, but the guidance is only implicit.

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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