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dotlab-hq

@dotlab-hq/vector-store-mcp

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Search Vector Store

openai_search_vector_store
Read-onlyIdempotent

Search an OpenAI vector store to find relevant document chunks based on a query and optional file-attribute filters.

Instructions

Search an OpenAI vector store for relevant chunks based on a query and optional file-attribute filters.

Use this to perform semantic search across the documents in a vector store. You can provide a single query string or an array of queries, and optionally filter by file attributes (e.g., department = "engineering").

Results include ranked search hits with content snippets, file IDs, and relevance scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vector_store_idYesThe ID of the vector store to search.
queryYesA query string or array of query strings for search.
max_num_resultsNoMaximum number of results to return (1–50, default 10).
rewrite_queryNoWhether to rewrite the query for better search results.
filtersNoFilter to apply based on file attributes.
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readable.markdown
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that results include 'ranked search hits with content snippets, file IDs, and relevance scores,' which provides useful behavioral context beyond annotations.

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 sentences, front-loaded with purpose, follows with usage and result description. Every sentence adds value with no redundancy.

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?

Despite no output schema, the description details return values (snippets, file IDs, scores). Covers query types, filters, and usage context completely for a search tool.

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% with good descriptions. The description adds usage examples (e.g., 'department = "engineering"') and explains result format, but does not significantly deepen understanding of individual parameters beyond schema.

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 'Search an OpenAI vector store for relevant chunks based on a query and optional file-attribute filters,' specifying verb and resource. Among siblings, only this tool performs search, so it is well-differentiated.

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

Usage Guidelines4/5

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

The description says 'Use this to perform semantic search' but does not explicitly mention when not to use it. However, since it is the only search tool among siblings, context is clear enough.

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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