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ZeroDB Agent Memory MCP Server

ZeroDB Agent Memory MCP Server

by AINative-Studio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

zerodb_semantic_search

Search stored memories by semantic similarity using text or vector embeddings to retrieve relevant context for AI agents.

Instructions

Search memory by semantic similarity without needing a text query. Directly search using vector embeddings or similar memories.

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": "zerodb_semantic_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
textNoText to find semantically similar memories for (will be embedded automatically)
limitNoMaximum number of similar memories to return
vectorNoPre-computed embedding vector to search with (alternative to text)
session_idNoOptional session ID to limit search scope
min_similarityNoMinimum cosine similarity score (0.0 to 1.0)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.7

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations present, the description carries full responsibility for disclosing behavior. It mentions 'directly search using vector embeddings' but omits critical details such as the relationship and exclusivity between text and vector parameters, expected return format, error behavior, or how similarity thresholds are applied. This creates ambiguity for an agent.

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 only two sentences, front-loaded with the action ('Search memory by semantic similarity'), and contains no filler or repetitive content. Every word contributes to the core purpose.

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 tool with five parameters and no output schema or annotations, this minimal description is insufficient. It omits crucial context like whether text is still accepted despite the 'without needing a text query' phrasing, how session_id scopes the search, and what min_similarity impacts. The agent would need to infer too much.

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 provides 100% parameter coverage, so the baseline is met. The description's mention of 'vector embeddings' aligns with the vector parameter but adds no new insight beyond the schema. It does not explain parameter interplay or selection strategies for text vs. vector.

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 searches memory by semantic similarity, with a specific focus on vector embeddings. It distinguishes from sibling text-search tools by noting 'without needing a text query' and 'using vector embeddings or similar memories.' This makes the primary 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 when a vector embedding or semantic similarity is desired, and hints that a text query is not required. However, it does not explicitly name alternative tools (like zerodb_search_memory) or provide when-not-to-use guidance, leaving the choice somewhat to the agent.

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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