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record_memory

Record a decision, progress update, failure, or handoff summary to persist AI coding agent memory across sessions. Provide project, type, title, and content in Markdown.

Instructions

Save one memory: a decision, progress update, failure, or handoff summary.

project: project name; letters, digits, '_', '-', '.' only (e.g. "my-app") type: one of decision / progress / failure / handoff (configurable via AI_MEMORY_TYPES) title: one-line title content: the details; multi-paragraph Markdown is fine llm: who is writing this (e.g. "Claude", "Codex"); strongly recommended tags: comma-separated keywords, optional

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llmNo
tagsNo
typeYes
titleYes
contentYes
projectYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the behavioral burden. It indicates a persistent write action, notes that 'type' is configurable via AI_MEMORY_TYPES, and recommends the llm parameter, but it does not disclose return behavior, error conditions, or side effects beyond saving.

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 purpose sentence is front-loaded, followed by a compact, scannable per-parameter list. Every line adds necessary information with no filler or repetition.

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?

For a 6-parameter mutation tool with no schema coverage and no output schema, the description covers all inputs, required fields, formatting, and one configuration dependency. It could add what happens on save or any duplicate-handling behavior, but nothing essential is missing for invoking the tool.

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 description coverage is 0%, so the description fully compensates: every parameter gets a concrete explanation, format constraints for project, allowed type values, Markdown support for content, and examples. This is far above typical parameter documentation.

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 opens with 'Save one memory' and enumerates the content kinds (decision, progress update, failure, handoff summary), giving a specific verb, resource, and scope. It is clearly distinct from the read/search-oriented sibling tools.

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?

The context is clear: this is the write-side tool among read/search siblings, so an agent can infer when to use it. It does not explicitly state when not to use it or name alternatives, but the sibling list makes the distinction nearly self-evident.

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