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mymemory-mcp

mymemory-mcp

by 20vision
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

mymemory_get_context

Retrieve a user's stored standing rules, facts, preferences, and notes at conversation start to apply their personalized context across every AI tool they use.

Instructions

Fetch the user's MyMemory vault context: a compiled context_block (directives first, then facts/preferences/notes, one per line) plus the raw active entries. Call once at conversation start and apply it — these are the user's standing rules, facts, and preferences across every AI tool they use.

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": "mymemory_get_context"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the content structure (context_block format, directives-first ordering) which is genuinely useful behavioral info. It doesn't mention authentication requirements, pagination, or failure modes, but for a zero-parameter read-only fetch tool the disclosure is reasonably complete for what's exposed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose then structure then usage guidance. Slightly dense single paragraph but every clause earns its place. Could optionally be split into bullet points but it's efficient as written.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A zero-parameter, no-output-schema tool with clear READ semantics. The description covers what it returns, the internal structure, when to call it, and what to do with the result. For a simple fetch tool this is fully complete — nothing meaningful is left unexplained.

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?

There are 0 parameters and schema coverage is 100%, making parameter semantics a non-issue. The dimension baseline for 0 params is 4, and the description adds context about what behavior to expect without parameters (returns full context).

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?

Clear verb (Fetch) + specific resource (user's MyMemory vault context) with detailed description of what's returned (compiled context_block with directives/facts/preferences/notes structure plus raw active entries). Distinguishes well from siblings: get_context returns the full compiled context while search/propose serve different purposes.

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

Usage Guidelines5/5

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

Explicitly states 'Call once at conversation start and apply it' — gives a precise trigger condition. The description's ending ('these are the user's standing rules, facts, and preferences across every AI tool they use') explains why this tool matters and implies it should be prioritized early, which differentiates it from alternative sibling tools.

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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