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remember

Save important project details with provenance to persist across AI coding sessions. Store decisions, bugs, and context for later retrieval.

Instructions

Store a long-term memory with provenance. Memories persist across sessions and can be retrieved later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for categorization
contentYesThe memory content
summaryNoBrief summary
importanceNoImportance score
project_idYesProject this memory belongs to
memory_typeYesType of memory

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.0

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 usefully discloses that memories persist across sessions and are retrievable later, and mentions provenance. However, it does not explain what provenance entails, return/acknowledgment behavior, or side effects, leaving gaps for a tool with zero annotation coverage.

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 filler. The purpose is stated first, and the persistence behavior follows immediately. Every component earns its place.

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?

Given no annotations and no output schema, the description provides the essential purpose and persistence trait but omits details like what provenance means, what the tool returns or acknowledges, and explicit guidance on when to prefer it over logging tools. It is sufficient for basic selection and invocation, but not comprehensive.

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 the schema already documents all parameters. The description adds no parameter-level detail beyond the notion of long-term memory and provenance, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Store') and resource ('long-term memory with provenance'), making the core action unambiguous. It also adds persistence semantics, but it does not name sibling tools to explicitly differentiate itself, so it stops short of a 5.

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 description conveys clear usage context: this is for memories that should persist across sessions and can be retrieved later. It does not mention exclusions or alternatives like log_activity or append_event, but the intended use case is reasonably clear.

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