trace-mcp

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

With no annotations provided, the description carries full burden and does well by disclosing: 1) the tool performs computation and caching (write operation), 2) has a prerequisite (AI provider enabled), 3) explains the default incremental behavior vs. force=true destructive behavior, and 4) mentions latency implications. It doesn't cover error handling or performance characteristics.

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?

Three sentences with zero waste: first states purpose and benefit, second states prerequisite, third explains parameter semantics. Each sentence earns its place by providing distinct, valuable information. Well front-loaded with the core purpose.

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 2-parameter tool with no annotations and no output schema, the description provides good coverage of purpose, usage context, and behavioral implications. It could benefit from mentioning what happens on success/failure or the format of any implicit output, but given the tool's focused scope, it's mostly complete.

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 100%, so baseline is 3. The description adds value by explaining the semantic difference between default incremental behavior and force=true ('drop and recompute all existing embeddings'), which goes beyond the schema's technical description. It also mentions the purpose of batch_size optimization.

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 specific action ('Precompute and cache symbol embeddings for semantic / hybrid search') and distinguishes it from sibling tools by focusing on embedding optimization rather than analysis, search, or refactoring. It explains both the primary purpose and the latency-avoidance benefit.

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?

Explicitly states when to use ('calling this once after a fresh index avoids the first-query latency spike') and when not to use (requires AI provider enabled in config). It also implies an alternative (lazy computation on first query) and provides guidance on the force parameter for different scenarios.

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