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kb_backfill_embeddings

Generates embeddings for knowledge base entries that are missing or have outdated content, skipping entries with matching content hashes. Supports dry run and batch processing.

Instructions

Embed any KB entries that are missing or stale (model/content changed). Idempotent: skips entries whose stored content_hash still matches. Requires LORE_SEMANTIC_SEARCH=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batch_sizeNoHow many entries to encode per batch (default 32).
limitNoOptional cap on entries to process this run.
dry_runNoIf true, report what would be embedded without writing.
confirm_productionNoRequired (true) to run a real backfill when LORE_ENV=production. Guards against accidental large-scale writes; ignored for dry runs and non-prod.
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses idempotency and environment requirement, but does not describe write impact, error behavior, or rate limits. Adequate but not comprehensive.

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?

Two concise sentences with no wasted words. Front-loads purpose and key characteristics (idempotent, requires env var). Optimal for quick parsing.

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?

No output schema; description does not specify what the tool returns (e.g., counts, errors). While the purpose is clear, the missing return information slightly reduces completeness for a backfill operation.

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?

Input schema covers all 4 parameters with descriptions (100% coverage). Description adds no extra parameter-level meaning beyond what schema already provides, so baseline score of 3 is appropriate.

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?

Description states 'Embed any KB entries that are missing or stale' with specific verb and resource, and notes idempotency and environment requirement, clearly distinguishing from sibling tools like kb_embedding_status or kb_add.

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?

Clearly indicates when to use (backfill missing/stale entries) and prerequisites (LORE_SEMANTIC_SEARCH=true). Does not explicitly mention when not to use or alternatives, but the context is sufficient for an agent to decide.

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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