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Simple Memory MCP Server

Simple Memory MCP Server

by AojdevStudio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_nodes

Search knowledge graph nodes with fuzzy matching and relevance scoring to retrieve relevant results sorted by match quality.

Instructions

Search for nodes in the knowledge graph with relevance scoring and fuzzy matching. Returns results sorted by relevance with detailed match metadata.

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_nodes"
  }
}

Input Schema

NameRequiredDescriptionDefault
fuzzyNoEnable fuzzy matching for typo tolerance (default: true)
limitNoMaximum number of results to return (default: 50, max: 200)
queryYesThe search query to match against entity data
fieldsNoWhich fields to search in (default: all fields)
minScoreNoMinimum relevance score (0-100) to include in results (default: 0)
fuzzyThresholdNoSimilarity threshold for fuzzy matching (0-1, default: 0.7)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It discloses ordering (sorted by relevance), the presence of match metadata, and the use of fuzzy matching, but it does not explicitly state that the operation is read-only or has no side effects. This is a moderate level of transparency for a search tool.

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 two sentences, both informative and free of fluff. It front-loads the core action and adds a concise note about output characteristics, making it easy to parse and use.

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 tool's moderate complexity (6 parameters, no output schema, no annotations), the description provides essential context: what it searches, how results are ordered, and that metadata is included. It does not detail the exact metadata fields, but the parameter schema covers invocation details well, leaving only a minor gap in return-structure specificity.

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 input schema covers 100% of parameters with detailed descriptions, including defaults and ranges. The tool description does not add additional meaning beyond what the schema provides, so the baseline score of 3 applies.

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 with a specific verb ('Search') and resource ('nodes in the knowledge graph'), and distinguishes it from siblings by mentioning relevance scoring and fuzzy matching. It also describes the output (results sorted by relevance with metadata), making its purpose unambiguous.

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 targeted searching rather than reading the whole graph, but it does not explicitly state when to prefer this over alternatives like 'read_graph' or 'open_nodes'. There is no mention of exclusions or trade-offs, so the guidance is implicit rather than direct.

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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