Skip to main content
Glama

save_memories

Batch-save multiple documents to a vector database by embedding with Ollama and upserting to Qdrant, applying shared metadata and optional collection targeting.

Instructions

Save multiple documents into the vector DB in one batch.

All texts are embedded in a single Ollama call and upserted together. metadata (JSON string or object) is applied to every document. If collection is given, the memories are stored there (created automatically if missing).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYes
metadataNo{}
collectionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does add real behavior: embeddings happen in a single Ollama call, writes are upserts, metadata is stamped on every document, and the collection is auto-created if missing. It omits error/failure behavior, overwrite semantics for existing documents, and any size or rate constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four short sentences with no filler, front-loaded with what the tool does before the operational details. The information is well ordered, though the parenthetical about JSON string or object slightly duplicates the schema's anyOf.

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

Completeness3/5

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

An output schema exists so return values need not be described, and the mutation semantics are partially covered. However, for a no-annotation batch write the description leaves the agent without error handling, overwrite behavior, or batch-size expectations.

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 0%, so the description must compensate; it explains that metadata is applied to every document and that collection defaults to being auto-created. It says nothing about how texts is chunked, per-call limits, or the string-vs-object metadata duality, leaving gaps the schema does not fill.

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

Purpose4/5

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

States a specific verb and resource ('Save multiple documents into the vector DB') and the scope ('in one batch'), which implicitly but clearly separates it from the singular save_memory sibling. It stops short of naming the sibling it is not, so differentiation relies on the agent inferring from 'multiple'/'batch'.

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 explicit when-to-use versus when-not guidance and no acknowledgment of save_memory, search_memory, or update_memory as alternatives. The batch framing is the only signal for choosing this over the single-document tool, which is weak routing guidance.

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