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mcp-lance-db

mcp-lance-db

by kyryl-opens-ml
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_memories

Search semantic memories by query to retrieve the most relevant matches based on meaning.

Instructions

Search memories using semantic similarity Args: query: The search query limit: Maximum number of results to return

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": {
      "query": "<query>"
    },
    "name": "search_memories"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.6

TDQS

C2.6/5.0
Behavior2/5

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

No annotations exist, and the description fails to disclose behavioral traits beyond the basic operation. It does not mention whether the tool is read-only, requires authentication, or has any side effects, leaving the agent uninformed.

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

Conciseness2/5

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

The description is overly brief, consisting only of a one-line summary and parameter list. While concise, it lacks necessary detail and structure, making it under-specified rather than efficiently concise.

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

Completeness2/5

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

For a simple tool with two parameters and no output schema, the description is still incomplete. It does not explain what 'semantic similarity' entails, what the result format is, or any limitations, leaving gaps for an AI agent.

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

Parameters1/5

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

The description merely restates the parameter names and types ('query: The search query', 'limit: Maximum number of results to return') without adding semantic value beyond the schema. With 0% schema coverage, this is insufficient.

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 function: 'Search memories using semantic similarity'. It uses a specific verb ('search') and resource ('memories'), and distinguishes from the sibling 'add_memory' which serves a different purpose.

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

Usage Guidelines2/5

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

No guidance on when or when not to use this tool. While the sibling 'add_memory' suggests a clear alternative, the description does not provide explicit context or exclusions for usage.

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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