vector_search

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

No annotations are provided, so the description carries full burden. It discloses that results are nodes sorted by relevance score and mentions cross-project behavior. It does not cover rate limits or authentication, but for a read-only search tool, the behavioral disclosure is adequate.

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 and well-structured: first sentence states purpose, second provides result handling, third gives a clear usage pattern, and fourth explains cross-project behavior. Every sentence adds value with no redundancy.

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 tool's complexity (semantic search across multiple indexed layers without output schema), the description provides sufficient context: purpose, usage pattern, parameter behavior, and follow-up tools. It is complete for effective agent selection and invocation.

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 coverage is 80% (4 of 5 parameters described in schema). The description adds value by explaining the workspace_id parameter's cross-project behavior and implies the top_k default. It adds modest meaning beyond the schema, justifying a score above baseline 3.

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 clearly states the tool does semantic vector search across indexed layers (Entity + Chunk + Fact) to find relevant knowledge by meaning. It distinguishes from siblings by emphasizing it is the primary entry point for regression search, contrasting with tools like graph_get_node or desktop_focus.

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 provides explicit guidance on when to use: for finding relevant knowledge by meaning, and outlines a regression search pattern (1. vector_search -> 2. graph_get_node -> 3. read files). It also clarifies cross-project behavior with workspace_id, helping the agent decide when to narrow scope.

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