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Run direct vector similarity search over records whose property is indexed with createEmbeddingIndex. Deprecated alias for vectorSearch; use vectorSearch for new integrations.

Instructions

Deprecated alias for vectorSearch. Performs direct vector similarity search over records whose propertyName has been indexed with createEmbeddingIndex. Prefer vectorSearch for new integrations. For natural-language schema-aware querying, use smartSearch.

Input Schema

TableJSON Schema

Name	Required	Description
`ef`	No	ANN gather breadth when the search runs on the ANN graph.
`mode`	No	How to rank candidates. "auto" (default): exact on small indexes, the ANN graph on large ones. "exact"/"ann" force a path ("ann" needs a cosine index).
`skip`	No	Number of results to skip for pagination (default 0).
`limit`	No	Maximum number of results to return (default 10).
`query`	No	Free-text query to embed and compare against stored vectors (managed indexes only). Mutually exclusive with queryVector.
`where`	No	Optional filter applied before scoring (activates prefilter mode).
`labels`	Yes	One or more record labels to scope the search. The first label is used to resolve the embedding index.
`dimensions`	No	Disambiguates when multiple indexes share the same label+property. Inferred from queryVector length when queryVector is supplied.
`sourceType`	No	Select which index type to search. Required when both managed and external indexes exist for the same label+property.
`queryVector`	No	Pre-computed embedding vector to search with (external indexes). Mutually exclusive with query. Length must match index dimensions.
`propertyName`	Yes	Name of the indexed property to search against.
`scoreThreshold`	No	Keep only results scoring at least this much (scores depend on the embedding model).
`similarityFunction`	No	Disambiguates when multiple indexes share the same label+property+sourceType.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`total`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.0.1Oct 21, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds genuine context beyond that: deprecation status and the hard prerequisite that the property must have an embedding index created first. It does not discuss result ranking or index-type behavior, but the schema covers those.

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?

Four short sentences, with the most decision-relevant fact (deprecation) front-loaded, followed by what it does, then the two routing alternatives. No filler.

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

Completeness5/5

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

Given an output schema exists, return values need no explanation, and with 100% schema coverage the 13 parameters are self-documenting. The description supplies exactly what structured fields cannot: deprecation, the indexing prerequisite, and sibling routing.

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 description coverage is 100% across all 13 parameters, including enums for mode, sourceType, and similarityFunction, so the schema already carries the parameter semantics. The description only reinforces propertyName's indexing prerequisite; baseline 3 is appropriate.

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?

States a specific verb and resource ('direct vector similarity search over records') plus the precondition that propertyName must be indexed with createEmbeddingIndex. It explicitly distinguishes itself from vectorSearch (the preferred replacement) and smartSearch, so an agent can route without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit when-not-to-use guidance: it is deprecated, new integrations should use vectorSearch, and natural-language schema-aware querying should use smartSearch. This is a complete routing decision with named alternatives.

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