extract_vector_context

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

With no annotations, the description carries the behavioral burden, but it only adds high-level claims about vector reasoning and hierarchical chunking. It does not disclose whether the operation is read-only, how chunks are returned, what max_tokens affects, or any side effects or limits.

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 core sentence is concise and front-loaded after a flashy preamble. The preamble's '100M Token Vector Reasoning & Hierarchical Semantic Chunker Engine' is redundant with the actual sentence and does not earn its place.

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?

Even though the schema covers parameters, the tool has no output schema and the description never explains what the caller receives or how chunk extraction behaves on large texts. For a complex vector-chunking tool, this leaves substantial ambiguity.

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?

Input schema coverage is 100%, so all four parameters are already documented, and the description adds no meaning beyond the schema. The generic mention of extracting from documents aligns with full_text but provides no extra detail.

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 states a specific action and target: 'Extracts relevant semantic chunks from large documents/attachments.' This is clear enough to differentiate from sibling scripture-search tools, though the opening phrase '100M Token Vector Reasoning & Hierarchical Semantic Chunker Engine' is marketing-oriented rather than functional.

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 phrase 'from large documents/attachments' implies the tool is for document-scale context extraction, but it never states when to prefer it over siblings like search_semantic or get_chapter_context, nor does it describe exclusions. It gives only implicit guidance.

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