Multi-source enriched B2B records: name + email + LinkedIn URL + title + company size + industry. Cross-referenced from 3+ sources for accuracy. Tier 1: 1-50 ($3-15). Tier 2: 51-5000 ($15-500). Tier 3: 5001-100K ($250-3000).
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | ||
| industry | No | ||
| quantity | Yes | ||
| seniority | No | Director, VP, C-suite, etc. | |
| company_size | No | Range like '11-50' or '500+'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions data enrichment and pricing tiers but lacks disclosure of important behavioral traits such as rate limits, authentication requirements, idempotency, or whether the tool performs a search or purchase. The pricing info is useful but incomplete.
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 and front-loaded with key information about the tool's outputs and pricing. It is efficient with no filler, though it could be more structured (e.g., bullet points) for clarity.
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 the tool has 5 parameters, no output schema, and no annotations, the description provides partial completeness: it outlines return fields and pricing tiers but omits details on how to specify 'role' and 'industry', the ordering process, and potential costs beyond the tiers. More context needed for safe invocation.
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 input schema has 5 parameters with only 40% coverage (seniority and company_size have descriptions). The description adds meaning for 'quantity' by linking it to pricing tiers, but fails to explain 'role' and 'industry' parameters. Thus, it partially compensates for schema gaps but not fully.
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 that the tool provides multi-source enriched B2B records including name, email, LinkedIn URL, title, company size, and industry, with cross-referencing from 3+ sources. This differentiates it from siblings like 'hsh-b2b-contact' and 'hsh-b2b-full'.
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 does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'hsh-b2b-contact' or 'hsh-b2b-full'). No when-to-use or when-not-to-use instructions are present, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but some closely related tools (e.g., hsh-b2b-*, hsh-esg-* variants) could cause confusion. Descriptions help differentiate, but an agent might still misselect similar products.
Naming convention is mixed: some tools use hyphens (hsh-b2b-contact), others use underscores (hsh_broker_data_request). While mostly readable, the inconsistency could be confusing for agents expecting a uniform pattern.
32 tools is on the high side for a single server, but given its purpose as a data marketplace, the large number reflects a wide catalog. However, it may be overwhelming for agents to navigate.
Covers many data domains but has obvious gaps (e.g., weather, social media). The inclusion of custom data request tools (hsh_describe_data_need, hsh_broker_data_request) mitigates these gaps, allowing agents to request missing data.
