🏆 Algorand Metrics — Global Challenge: Algorand network metrics (round, price, USDC on-chain data). Algorand Global Challenge 2026 entry. Deployed by Iris. USDC settlement on Algorand via GoPlausible.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | Account to process | |
| address | No | Address to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It states the tool provides metrics, but doesn't disclose whether it performs read-only network queries, requires API keys, has rate limits, or mutates state. The mention 'USDC settlement on Algorand via GoPlausible' hints at on-chain integration but doesn't explain side effects or data freshness. For a network/challenge tool with no annotations, this is a notable transparency gap.
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 relatively short but front-loaded with promotional/marketing noise: 'Algorand Global Challenge 2026 entry. Deployed by Iris.' These sentences do not help an agent select or invoke the tool. The core functional sentence is clear, but the metadata chatter dilutes the effective message and should be removed or moved to a metadata field.
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?
For a tool with 2 optional parameters, no required params, no output schema, and no annotations, the description leaves important gaps: what the output looks like, how the parameters affect the result, what 'process' means for an account/address, and whether metric selection is automatic or parameter-driven. The description delivers enough to hint at a read-only Algorand metrics service but not enough for an agent to invoke it correctly with confidence.
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 100%, so the schema already documents both parameters as strings. However, the descriptions are unhelpfully vague ('Account to process', 'Address to process') and the tool description adds no meaning to them. The description does not clarify whether 'account' and 'address' refer to Algorand accounts, contract addresses, or wallet addresses. Since both schema and description are vague, this sits at baseline 3; the description doesn't add value beyond the schema's parameter names.
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 opens with a clear emoji-delimited title ('Algorand Metrics — Global Challenge') and states that the tool exposes Algorand network metrics: round, price, USDC on-chain data. It names a concrete verb/resource ('network metrics') and distinguishes itself as a challenge-specific Algorand metrics tool. Sibling names like get_stats and service_discovery don't overlap much, but the description's promotional text ('Global Challenge 2026 entry...') adds noise and doesn't fully articulate whether the tool computes, fetches, or aggregates metrics.
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?
There is no explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, when the tool applies (e.g., only for Algorand network questions), or when to prefer a sibling like get_stats or x402-crypto-price. The context signals show 0 required parameters and 2 vague optional params, but the description offers no usage context beyond 'Global Challenge'.
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.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.