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littlebigbrains

@littlebigbrain/mcp

lbb_embeddings_delete

DestructiveIdempotent

Delete an embedding and its stored vectors, removing its class from search. Requires repeating the name as confirmation before the deletion is performed.

Instructions

Delete an embedding: its name and its vectors (index-gc removes the stored runs). Its class stops being searchable until it is declared and built again. Ask the user first; confirm must repeat the name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe embedding name.
graphNoGraph to target; defaults to the connection's graph
detailNoResponse detail level. Defaults to compact.
confirmYesThe same name again, to confirm.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.2

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark destructiveHint=true and idempotentHint=true, but the description adds valuable context: index-gc removes stored runs and the embedding class becomes non-searchable until rebuilt. This discloses side effects beyond the annotation flags and does not contradict any 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?

Three sentences with no wasted words. The primary action is front-loaded, and subsequent sentences add crucial details (index-gc, searchability, user confirmation) in a logical order. It is efficient yet thorough.

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?

For a destructive operation with four parameters and no output schema, the description covers what happens (deletion, vector removal, searchability impact) and the required confirmation flow. It gives an agent everything needed to invoke it correctly and understand the consequences, making it complete.

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 detailed parameter descriptions, so the schema already explains each parameter. The description adds context about the confirm requirement and its effect, but those are already present in the schema's description for 'confirm'. It does not introduce new parameter-level semantics beyond the 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 the action (delete an embedding) and its effects (removes name and vectors, stops searchability). It goes beyond a simple verb+resource by explaining the consequence that the class must be redeclared and rebuilt, making the purpose unmistakable.

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?

It gives explicit guidance on usage: ask the user first and require confirmation via repeating the name. It does not compare directly with sibling tools like lbb_embeddings_manage, but the unique destructive nature and safety instructions provide clear context for when it should be used.

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