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Idempotent

Precompute and cache symbol embeddings to eliminate first-query latency when using semantic search. Call after reindexing.

Instructions

Precompute and cache symbol embeddings for semantic / hybrid search. Embeddings are also computed lazily on first semantic query, but calling this once after a fresh index avoids the first-query latency spike. Requires AI provider to be enabled in config (ollama/openai). Set force=true to drop and recompute all existing embeddings. Mutates the vector store; idempotent. Use after reindex when you plan to use semantic search. Returns JSON: { status, indexed_this_run, total_embedded, coverage_pct, duration_ms }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batch_sizeNoSymbols per embedding API batch (default 50)
forceNoDrop existing embeddings and re-embed everything (default false — incremental)
Behavior4/5

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

The description adds value beyond annotations: it notes that the tool mutates the vector store, is idempotent, and describes the force parameter behavior. Annotations already indicate idempotentHint=true, so no contradiction.

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 concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value without redundancy.

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?

Given the tool's complexity (2 optional params, no output schema), the description is complete. It explains prerequisites, use case, behavior, and the return format. Slight improvement could be more detail on output, but it mentions keys.

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%, so baseline is 3. The description explains the force parameter well but does not add detail about batch_size beyond the schema. Acceptable.

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 that the tool precomputes and caches symbol embeddings for semantic/hybrid search, distinguishing it from the lazy computation that happens on first query. It is specific and differentiates from sibling tools like reindex.

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 description provides clear usage context: 'Use after reindex when you plan to use semantic search.' It also mentions a prerequisite (AI provider enabled). While it doesn't explicitly state when not to use, the context is sufficient.

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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