Skip to main content
Glama

memorize_multiple_texts

Store multiple text passages for later retrieval based on semantic meaning using local embeddings and vector storage.

Instructions

Memorize multiple texts for later retrieval based on relevance in meaning, not just keywords.

Args:
    texts (list): A list of texts to memorize.
Returns:
    str: A message indicating success or failure of the operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYes
metadataNo
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that retrieval is based on relevance in meaning, not keywords, but omits side effects, authorization, or limitations (e.g., max texts). The return value is described only as a success/failure message, lacking detail on behavior.

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 highly concise: two sentences of purpose followed by structured Args/Returns. Every sentence is essential and front-loaded.

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?

Given the tool's complexity (2 params, nested metadata, no output schema), the description is insufficient. It omits metadata details and how this differs from memorize_text. More context like storage size limits or relation to sibling tools would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It explains the 'texts' parameter as a list, but ignores the 'metadata' parameter entirely. The description adds partial meaning for one of two parameters.

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's purpose: memorize multiple texts for later retrieval based on relevance in meaning, not keywords. It uses a specific verb-resource pair and distinguishes from siblings like memorize_text (single text) and remember_similar_texts (retrieval).

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 guidance is given on when to use this tool versus alternatives like memorize_text or remember_similar_texts. The description does not mention when not to use it or provide any exclusions.

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/renl/mcp-rag-local'

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