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HSH Data-on-Demand

by io.github.hshintelligence

hsh_describe_data_need

FREE. Describe any data need in plain language and receive an instant firm quote from HSH Intelligence Data-on-Demand: price in USDC, scope, a frozen quote_ref, and a pay_url. Pay the quote via x402 (USDC on Base or Solana) at the pay_url to place the order; delivery in 24h. Use this BEFORE purchasing custom data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
needYesFree-text description of the data you need (type, volume, geography, freshness).
urgencyNoOptional urgency.
budget_usdNoOptional budget in USD; we may accept it within our floor.

TDQS

A4.7/5.0
Behavior5/5

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

Discloses that the tool is free, provides an instant firm quote with price in USDC, scope, quote_ref, and pay_url, and explains payment via x402 and 24h delivery. Since no annotations exist, description carries full burden and meets it well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with front-loaded key info (free, quote details). No wasted words, each sentence adds essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-param tool with no output schema, description sufficiently explains input expectations, output format (price, scope, quote_ref, pay_url), and process (pay to order, delivery in 24h). Covers agent's needs for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds clarifying examples for 'need' (type, volume, geography, freshness) and indicates budget may be accepted within a floor, adding value beyond schema defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verbs ('describe data need', 'receive quote') and clearly identifies the resource (a firm quote). Distinguishes from siblings by positioning as a pre-purchase quote tool, unlike specialized data tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use this BEFORE purchasing custom data.' Provides clear context but does not explicitly list when not to use or alternative tools.

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

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TDQS

B3.3/5.0
Disambiguation4/5

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 Consistency3/5

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.

Tool Count3/5

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.

Completeness3/5

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.

Resources