query_expertise

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

With no annotations, the description carries the full behavioral burden; it helpfully discloses the shape of results (expertise areas backed by employment, repos, talks, research interests) but says nothing about read-only status, result ranking, or pagination beyond the schema's limit field.

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?

Two sentences, front-loaded with purpose before the trigger list. The topic enumeration is long but functions as concrete activation examples rather than filler, so it mostly earns its space.

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 simple two-parameter read tool with no output schema and no annotations, the description covers purpose, triggers, and returned content adequately. Mention of ordering or how the limit interacts with result totals would close the remaining gap.

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 100%, with both 'topic' and 'limit' fully documented including examples, so the schema does the heavy lifting. The description adds no syntax, matching, or scoping guidance for the topic string beyond the schema's own examples.

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?

States a specific verb and resource ('Search Ali Can Efe's expertise areas') and enumerates the kinds of evidence returned. It is clear what the tool does, but it never distinguishes itself from siblings such as get_active_research or ask_cv, which also touch the same subject matter.

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 second sentence explicitly says 'Use this when the user asks about experts in:' followed by concrete trigger topics, which gives an agent a clear activation cue. It stops short of stating when NOT to use it or naming an alternative tool for overlapping queries.

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