Batter-vs-bowler head-to-head record (111K pairs, Bayesian-adjusted): balls, runs, dismissals, dominance score. Fantasy + in-play edge. Pay per call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| batter | Yes | Batter name (partial match ok). | |
| bowler | Yes | Bowler name (partial match ok). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses pricing ('Pay per call via x402'), data quantity (111K pairs), and Bayesian adjustment. It implies a read-only query by describing output fields, but does not explicitly state side effects or safety profile. Overall provides useful behavioral context.
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?
Two sentences, no wasted words. The first sentence covers data and scope, the second sentence covers use case and billing. Efficient and front-loaded.
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 no annotations, the description adequately explains output fields and usage context. It could elaborate on the dominance score calculation, but for a simple lookup tool it is sufficiently complete.
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?
Input schema covers parameters fully with descriptions (e.g., 'partial match ok'). The tool description does not add additional parameter details beyond what schema provides, so baseline score of 3 applies.
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 it provides batter-vs-bowler head-to-head records with specific data (balls, runs, dismissals, dominance score) and use cases (fantasy, in-play). It distinguishes itself from sibling cricket tools by focusing on head-to-head matchup statistics.
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 mentions 'Fantasy + in-play edge,' indicating usage context. However, it does not explicitly state when not to use or provide alternatives among siblings, though the purpose is clear enough for agents to infer.
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.
