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Mnemonic

Mnemonic

by bacharyehya
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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mnemonic_search

Search memories using full-text queries and tag filters to find relevant notes. Ranked results combine title and content matches for accurate retrieval.

Instructions

Search memories using full-text search and/or tag filtering.

The query searches both title and content using SQLite FTS5. Results are ranked by relevance and weight.

Examples: - query: "wishly deals" # Find memories about Wishly deals - tags: ["project"] # All memories tagged 'project' - query: "API", tags: ["technical"] # Combine search and filtering

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

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

Input Schema

NameRequiredDescriptionDefault
paramsYesInput for searching memories.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Since no annotations are provided, the description bears the full burden of behavioral disclosure. It states that the query searches both title and content via SQLite FTS5 and that results are ranked by relevance and weight, which goes beyond the schema. It could mention edge cases like empty queries, but the core behavior is well disclosed.

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 concise and front-loaded: the first sentence states the primary purpose, followed by two sentences of technical detail and examples. Every sentence earns its place, with no fluff or repetition.

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?

The description is comprehensive for a search tool of this complexity. It covers the search scope, ranking behavior, and provides practical examples. The output schema exists, so the description need not explain return values. The schema covers parameter details, making the combination 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?

The schema description coverage is 100%, and the schema already provides detailed descriptions for all parameters (tags, limit, query). The description adds examples but no new parameter semantics, so the baseline of 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 clearly states the tool's purpose: 'Search memories using full-text search and/or tag filtering.' It specifies the verb (search), the resource (memories), and the mechanism (full-text and tag filtering), distinguishing it from sibling tools like list, get, and create.

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 provides clear context on how to use the tool with examples covering different modes (query-only, tags-only, combined). While it doesn't explicitly state when not to use it (e.g., vs. mnemonic_list), the search-focused language and examples imply the appropriate scenario.

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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