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memory-mcp

memory-mcp

by palimpsest-labs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recent

Retrieve newly created or updated entities, relations, and observations from the knowledge graph within a specified hour window, defaulting to 24 hours. Adjust the window up to 720 hours to focus on specific periods.

Instructions

Return entities, relations, and observations created or updated in the last N hours.

Args: hours: Look-back window in hours (default 24, max 720)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "recent"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description must fully explain behavior. It clearly states that only created/updated items are returned (not deletions) and defines the time window semantics. However, it does not disclose potential ordering, pagination, or any side effects (though none are expected). It adds some transparency but could go further in describing result behavior.

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 extremely concise: one opening sentence and a two-line docstring for the parameter. No unnecessary words or repetition. It is well-structured with the main purpose first, making it easy to scan.

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?

Given the low complexity (one parameter, no nested objects) and the presence of an output schema, the description appropriately covers what is returned and the time filter. It lacks details like sorting or limits, but for a simple time-based query, the provided information is sufficient. Sibling tools are diverse enough that the unique scope is clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only provides the parameter name and type with a default, but no description. The description compensates by explaining that 'hours' is a look-back window and adding a maximum of 720 that is not present in the schema. This adds significant meaning beyond the schema for the single parameter.

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 'Return' and names the exact resources (entities, relations, observations) with a clear time-based scope. This distinctly separates it from sibling tools like delete_entities or search_nodes, making its purpose immediately obvious.

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 usage for time-based queries by explaining the look-back window and default/max values, but it does not explicitly state when to use this tool instead of alternatives like search_nodes or read_graph. No 'when not to use' guidance is given, so it lacks explicit exclusions.

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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