Exa MCP Server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool finds but doesn't describe important behavioral traits like rate limits, authentication requirements, response format, pagination behavior, or whether this is a read-only operation. The description is functional but lacks operational transparency needed for an agent to use it effectively.

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 appropriately sized at two sentences, with the first sentence stating the core functionality and the second providing usage context. It's front-loaded with the essential information and avoids unnecessary elaboration. Every sentence earns its place, though the second sentence could be slightly more specific.

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 no annotations and no output schema, the description is incomplete for a search tool with 3 parameters. It doesn't describe what the return format looks like (structured data, raw HTML, JSON), how results are ranked, error conditions, or any limitations of the LinkedIn search through Exa AI. For a tool with no structured behavioral metadata, the description should provide more operational context.

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 the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain query construction best practices, searchType implications, or numResults limitations. Baseline 3 is appropriate when the schema does the heavy lifting.

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 searches LinkedIn profiles and companies using Exa AI, specifying the resource (LinkedIn) and purpose (finding professional profiles, company pages, business content). It distinguishes from some siblings like web_search_exa by mentioning LinkedIn specifically, but doesn't explicitly differentiate from company_research_exa or deep_search_exa which might have overlapping domains.

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 implied usage context ('useful for networking, recruitment, and business research'), suggesting when this tool might be appropriate. However, it doesn't explicitly state when to use this tool versus alternatives like company_research_exa or deep_search_exa, nor does it provide any exclusion criteria or specific prerequisites for use.

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