Enrich — Company Domain Intelligence

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

With no annotations, the description must carry the full burden of behavioral disclosure. It describes the output fields but does not clarify whether the operation is read-only, if any side effects occur, or how it handles invalid or non-existent domains. The 'enrich' term implies a lookup but remains ambiguous.

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: two sentences, front-loaded with the primary action and input, followed by a compact list of return fields. Every sentence adds value, and there is no fluff or repetition.

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?

The tool is simple (one parameter, no output schema), and the description covers its main purpose and outputs. However, it lacks details on failure modes, output shape, or field availability, and it does not reference the sibling alternative. It is minimally viable but not comprehensive.

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%; the schema already documents the 'domain' parameter with examples. The tool description adds no additional parameter semantics beyond specifying 'by domain name', which is already present in the schema. The baseline of 3 is appropriate.

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's action ('Enrich a company') and its primary input ('by domain name'), and lists the returned data categories. This distinguishes it from the sibling tool 'enrich_company_by_name', which enriches by name instead.

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 implies usage when a company domain is known, but it does not explicitly mention the sibling tool 'enrich_company_by_name' or provide when-not-to-use guidance. It only states the input, leaving the comparison to alternatives implicit.

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

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

With no annotations, the description carries the burden of disclosing behavior. It states that partial matching is supported and lists the return fields (domain, country, contact emails, phone numbers, social media profiles), which indicates a read/enrichment operation. However, it does not mention potential multiple matches, failure modes, or rate limits, limiting full transparency.

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 extremely concise—two sentences—and front-loaded with the action ('Enrich a company by name'). Every word earns its place, with no fluff or 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?

For a single-parameter tool with no output schema, the description is fairly complete: it states the input type, matching behavior, and the categories of returned data. It lacks details on output structure or edge cases, but given the simplicity, it covers most essential 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?

The schema already covers the 'name' parameter with a description and example. The tool description adds the key semantic of 'partial match', which is not present in the schema, providing additional meaning beyond the structured definition. This exceeds the baseline for 100% schema coverage.

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's verb ('Enrich') and resource ('a company by name'), and specifies it performs a partial match. It distinguishes itself from the sibling 'enrich_company' by noting the partial-match behavior, though it doesn't explicitly compare the two tools.

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 implies when to use this tool: when you have a company name and want enrichment, with partial matching. It provides clear context (using a name with partial match) but does not explicitly state exclusions or alternative tools for exact matches or non-name inputs.

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