Real-time intelligence on a startup/company by name or domain. Returns: profile, founders, tech stack, hiring signals, YC batch. Supports lookup (1 company) or discover (filtered list). Pay per call via x402 (USDC on Base).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | ||
| limit | No | ||
| query | Yes | Company name or domain, OR a discovery query. | |
| filters | No | For discover: { industry?, batch?, is_hiring?, has_email? } |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: real-time nature, return data fields, lookup vs discover modes, and payment mechanism. It does not mention rate limits, error handling, or data freshness, but covers essential aspects for a data retrieval tool.
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?
Three concise sentences, front-loaded with purpose, then listing return fields and modes. No redundant information. Every sentence provides necessary context.
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 output schema and 4 parameters (including nested filters), the description explains the two modes, return fields, and cost. It lacks details on output format or pagination but is largely sufficient for an agent to understand when and how to use the tool.
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 50% (query and filters described, mode and limit have empty descriptions). Description compensates slightly by naming the two modes and implying limit usage, but does not fully document all parameters. It adds some meaning beyond schema but not enough.
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?
Description clearly states the tool provides real-time intelligence on startups/companies, lists specific data returned (profile, founders, tech stack, hiring signals, YC batch), and distinguishes between lookup and discover modes. This differentiates it from sibling tools like hsh-crypto-intel or hsh-b2b-contact.
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?
Description explains two usage modes (lookup for one company, discover for filtered list) and mentions pay-per-call cost. However, it does not explicitly state when to use this tool over siblings or provide exclusions for certain use cases.
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.
