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Rebuild Semantic Vectors

montycat_reembed_semantic
Destructive

Replace an enrolled keyspace's text embedding model and rebuild all vectors. Clears existing vectors and backfills new embeddings; use semantic_status to verify the updated configuration.

Instructions

Replace an enrolled keyspace's text embedding model and backfill it.

This clears its current vectors, then has the engine rebuild them. Use montycat_semantic_status to observe the resulting configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldNo
storeNo
keyspaceYes
semantic_modelYes
Install Server

TDQS

A4.4/5.0
Behavior5/5

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

The description explicitly discloses that the operation clears the current vectors before rebuilding them, which is essential behavioral context beyond the destructiveHint annotation. It also directs the agent to verify the result via montycat_semantic_status, making the side effects and follow-up clear.

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 three sentences and front-loads the action. It contains no filler, clearly states the destructive effect, and adds a useful follow-up command. Every sentence earns its place.

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

Completeness4/5

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

For a destructive tool with no output schema and sparse parameter names, the description explains the main behavior, the side effect on existing vectors, and how to observe the outcome. It is sufficient for a basic call but leaves optional parameters and possible error conditions undocumented, so it is not fully 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?

The prose gives semantic meaning to the two required parameters: keyspace identifies the enrolled keyspace and semantic_model is the replacement embedding model. However, with 0% schema description coverage, the optional field and store parameters are left completely unexplained, so the description only partially compensates for the schema gap.

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 identifies the action: replace an enrolled keyspace's text embedding model and backfill it. It also explains the consequence (clears and rebuilds vectors), which distinguishes it from sibling tools like enabling semantic search or creating a keyspace.

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 context is clear: this is used when you want to swap the embedding model for an already-enrolled keyspace. It also points the agent to montycat_semantic_status for observing the resulting configuration, though it does not explicitly state when NOT to use this tool or name alternatives for similar operations.

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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