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Memory Server MCP

Memory Server MCP

by hridaya423
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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search_memory_content

Find stored memories by searching content with fuzzy matching to retrieve relevant information from persistent storage.

Instructions

Search memory content using fuzzy search

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_memory_content"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesThe search query

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'fuzzy search', which hints at approximate matching, but doesn't explain what 'memory content' entails, how results are returned (e.g., format, pagination), error conditions, or performance implications. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence: 'Search memory content using fuzzy search'. It is front-loaded with the core action and resource, with no wasted words or unnecessary elaboration, making it highly concise and well-structured.

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?

Given the complexity of search operations and the lack of annotations and output schema, the description is insufficient. It doesn't cover what 'memory content' includes, how results are structured, limitations of fuzzy search, or error handling. For a tool with no structured behavioral data, this leaves the agent poorly informed about how to use it effectively.

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 has 100% description coverage, with the 'query' parameter documented as 'The search query'. The description adds no additional meaning beyond this, such as examples of valid queries or how fuzzy search affects query interpretation. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose as 'Search memory content using fuzzy search', which includes a verb ('Search') and resource ('memory content'), making it clear what it does. However, it doesn't differentiate from sibling tools like 'find_memories_within_degrees', 'find_related_memories', or 'search_by_relationship', leaving ambiguity about when to use this specific search method versus others.

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?

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools related to searching or finding memories (e.g., 'find_memories_within_degrees', 'find_related_memories', 'search_by_relationship'), there is no indication of when fuzzy search is preferred, what contexts it applies to, or any prerequisites or 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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