anythingllm_search

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so no contradiction. The description adds useful behavioral context beyond annotations, such as the score_threshold semantics ('Lower = more results') and the top_n range (1-20, default 4). This helps the agent understand the search behavior and result control.

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 concise: a single purpose sentence followed by a clean argument list. No unnecessary words. Each line earns its place by specifying a parameter or behavior.

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?

Given the output schema exists, return values need no explanation. The description covers purpose, all parameters, and key behaviors. For a search tool with this complexity, it is complete. The sibling context also reinforces that this is the dedicated search operation.

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?

Schema description coverage is 0%, so the description carries full responsibility for parameter meaning. It documents all four parameters: slug, query, top_n (with range and default), and score_threshold (with range and default). This is exactly the kind of compensation needed.

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 opens with a specific verb+resource: 'Search for relevant document chunks within a workspace using vector similarity.' This clearly distinguishes it from sibling tools like list_documents (listing) or chat (conversation). The method (vector similarity) adds further specificity.

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 implies usage (semantic search over chunks) but does not explicitly state when to use this tool over alternatives like chat or list_documents. There are no exclusionary statements or named alternative tools, so guidance is implicit rather than explicit.

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