Skip to main content
Glama

semantic_search

Find entities in your knowledge graph by semantic meaning using vector embeddings and similarity thresholds. Specify query, limit, similarity, entity types, and hybrid search options.

Instructions

Search for entities semantically using vector embeddings and similarity in your Memento MCP knowledge graph memory

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe text query to search for semantically
limitNoMaximum number of results to return (default: 10)
min_similarityNoMinimum similarity threshold from 0.0 to 1.0 (default: 0.6)
entity_typesNoFilter results by entity types
hybrid_searchNoWhether to combine keyword and semantic search (default: true)
semantic_weightNoWeight of semantic results in hybrid search from 0.0 to 1.0 (default: 0.6)
Behavior2/5

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

No annotations provided, so description must disclose behavior. It does not mention return format, result interpretation, performance implications, or safety traits. Only states the action without side-effect details.

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?

Single sentence that efficiently communicates the tool's purpose with no redundancy. All words contribute to clarity.

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?

Despite having 6 parameters, no output schema, and no annotations, the description provides only a high-level overview. Missing details on return values, pagination, and specific behaviors for parameters like hybrid_search or entity_types.

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?

Schema coverage is 100%, so parameters are already documented in the schema. The description adds no additional meaning beyond the schema, meeting the baseline of 3.

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 for entities semantically using vector embeddings and similarity, specifying the resource (Memento MCP knowledge graph memory) and methodology. It distinguishes from sibling tools like search_nodes which likely use keyword search.

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 to use this tool versus alternatives (e.g., search_nodes, open_nodes). Does not mention prerequisites or when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/gannonh/memento-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server