Embeddings Status

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the agent knows it's a safe read. The description adds behavioral context: it checks configuration and status, and details the return fields (available, provider, chunk_count, path). It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two paragraphs: first paragraph focuses on usage guidance, second lists return fields. It is front-loaded with the main purpose, and every sentence provides necessary information. No redundant or vague statements.

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

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

For a zero-parameter tool with an output schema, the description fully explains the return fields and their semantic meaning. It covers the diagnostic use case and provides actionable guidance. It is complete for the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so the baseline is 4. The description adds value by explaining the output fields and their interpretation, which goes beyond the input schema (empty).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear verb and resource: 'Check the embedding provider configuration and vector index status.' It also specifies the use case: 'diagnose why semantic search is unavailable.' This distinguishes it from sibling tools like build_embeddings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('to diagnose why semantic search is unavailable') and provides guidance on interpreting the chunk_count field relative to the FTS chunk count from 'stats'. It also directs to call 'build_embeddings' (without force) if chunk_count is lower, and specifies when to use force=True. This provides clear when-to-use and when-not-to-use guidance.

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