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ricleedo

MCP Embedding Storage Server

by ricleedo

save-memory

Store content in a vector database for semantic search and retrieval. Save text with unique identifiers to enable AI-powered similarity-based queries.

Instructions

Save content to vector database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe content to store
pathYesUnique identifier path for the content
typeNoContent type (e.g., 'markdown')
sourceNoSource of the content
parentPathNoPath of the parent content (if applicable)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Save content to vector database' implies a write operation but reveals nothing about permissions required, whether saves are idempotent, rate limits, error conditions, or what happens if the path already exists. For a mutation tool with zero annotation coverage, this leaves critical behavioral aspects undocumented.

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 maximally concise with a single, clear sentence that states the core functionality without any wasted words. It's appropriately sized for a tool with good schema documentation and gets straight to the point.

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?

For a mutation tool with 5 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what happens after saving (success indicators, returned data), error handling, or the vector database context. The agent lacks critical information needed to use this tool effectively in real scenarios.

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?

The description adds no parameter information beyond what the schema already provides. With 100% schema description coverage, all 5 parameters are documented in the schema itself. The description doesn't explain relationships between parameters (like how path and parentPath interact) or provide usage examples, so it meets the baseline but adds no extra value.

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?

The description clearly states the action ('Save') and target resource ('content to vector database'), making the purpose immediately understandable. However, it doesn't differentiate from its sibling 'search-memory' beyond the obvious verb difference, missing an opportunity to clarify the complementary relationship between save and search operations.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'search-memory' tool, prerequisites for saving content, or any constraints about when saving is appropriate versus other operations. The agent receives no contextual usage information.

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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