Apollo.io B2B Person Enrichment & Verified Email Finder (x402)

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

No annotations are provided, so the description carries the full behavioral burden, and it does substantial work: discloses the paid cost ($0.10 USDC via x402 on Base), that no signup or API key is required, and that output is deterministic JSON with specific fields. It doesn't cover failure modes, timeout behavior, or what happens if payment is missing, which keeps it from a 5.

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 a single dense sentence that front-loads price and core purpose well, but it is heavily packed and partly duplicates the schema-level description at the end. Every clause is on-topic, though there is some redundancy between the tool description and the input-schema description.

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 9-parameter paid tool with no annotation coverage and no output schema, the description covers pricing, auth model, input patterns, and the shape of the returned profile. It would be stronger if it noted error handling or the relationship with get_enrichment_quote.

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 parameters are already fully documented in the schema (including examples and combination hints like 'best combined with first_name and domain'). The description adds the higher-level grouping of the three input patterns, which is mildly useful but largely redundant with the schema.

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 chain (enrich, find, resolve) and resource (B2B person / work email / LinkedIn profile) with the underlying data source (Apollo.io). Clearly distinguishable from the only sibling, get_enrichment_quote, which is presumably a pricing/quote tool.

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 lists the three accepted input patterns (name+company, LinkedIn URL, email reverse lookup), which tells the agent when the tool is applicable. However, it never names get_enrichment_quote as an alternative or prerequisite, so the agent must infer the relationship between quoting and paying.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses that the call is free (vs the $0.10 USDC paid path) and that it returns latency telemetry, but it says nothing about auth requirements, rate limits, or whether it is strictly read-only.

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?

It is a single front-loaded sentence with the key qualifier ('FREE pre-flight') first, which is good. But it is overloaded with jargon and parentheticals ($0.10 USDC on Base, txHashes) that make it denser than needed for a discovery call.

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?

With no output schema and no parameters, the description does the work of enumerating what comes back (pricing, input/output schema, SLA telemetry, settlement txHashes), which is enough for an agent to decide to call it. Only auth/cost-to-caller details are absent.

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 tool takes zero parameters and the schema is empty at 100% coverage, so there is nothing for the description to disambiguate. Baseline 4 applies; no parameter meaning is missing.

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 concrete action (inspect) over concrete resources (pricing, schema, latency telemetry, settlement txHashes) for a named upstream service (Apollo.io B2B Person Enrichment). It is clearly distinct from the sibling enrich_person, which performs the actual enrichment, though the phrasing is dense and marketing-flavored rather than a clean verb+resource.

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?

'FREE pre-flight discovery tool' implies it should be called before the paid enrichment step, which is useful usage context. However, it never explicitly names enrich_person or states when-not to use it, so the routing is left to inference.

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