First-innings win-probability while a team is still setting a total (AUC 0.76, uses venue + team strength). Pay per call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| venue | No | ||
| set_elo | No | ||
| wickets | Yes | ||
| cum_runs | Yes | ||
| chase_elo | No | ||
| balls_bowled | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description adds some context (AUC 0.76, venue + team strength, pay per call) but does not disclose mutation or side effects. Since the tool is read-only, this is acceptable but minimal.
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?
Description is very short (one sentence plus cost note) and front-loads purpose. It loses a point for not separating cost or model accuracy into a clear structure.
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 6 parameters (3 required) and no output schema or annotations, the description fails to explain required parameters and output, leaving significant gaps for correct 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?
Schema description coverage is 0%; the description only mentions 'venue + team strength' without linking to specific parameters. Required parameters like balls_bowled, wickets, cum_runs are not explained.
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 computes first-innings win probability for a team setting a total, using venue and team strength. It distinguishes from sibling tools like chase-winprob.
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 for first innings but does not explicitly state when to use or when not to use, nor does it mention alternatives like chase-winprob.
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.
