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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

get_crypto_liquidations

Which side of the perp market is over-leveraged right now, and how exposed it is — not a raw feed. Composes Hyperliquid funding, premium and open interest with OKX open interest and the long/short account ratio into a named regime (LONG_SQUEEZE_RISK / SHORT_SQUEEZE_RISK / BALANCED), a 0-100 pressure score, and the evidence behind it. The pre-position read for a leveraged agent. Free public upstreams, no exchange key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
x_paymentNoSigned x402 payment payload

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and compensates by disclosing sources (Hyperliquid and OKX), composition logic (funding, premium, OI, long/short ratio), output format (regime, pressure score, evidence), and access requirements ('Free public upstreams, no exchange key'). It stops short of mentioning update frequency or failure behavior, but covers the key behavioral aspects for safe invocation.

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?

The description is compact and front-loaded, opening with the user-relevant question and then specifying outputs and positioning. Every sentence adds value: data sources, composition, output, and use case; no redundant metadata.

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

Completeness4/5

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

Despite having no output schema, the description enumerates the main return elements (named regime, pressure score, evidence) and the data sources, making the tool's behavior sufficiently specified. It does not detail the exact structure of the evidence, but that is minor for tool selection and invocation.

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

Parameters3/5

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

The single parameter x_payment already has a schema description ('Signed x402 payment payload'), giving 100% schema coverage. The tool description adds no additional parameter meaning, and the baseline for full schema coverage is 3.

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 clearly states the tool's function: it assesses which side of the perp market is over-leveraged and composes data into a named regime and pressure score. Phrases like 'not a raw feed' and 'pre-position read' explicitly distinguish it from sibling tools such as crypto_prices or crypto_market_metrics.

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?

The description identifies a clear intended use case ('The pre-position read for a leveraged agent') and excludes one class of use ('not a raw feed'). However, it does not name specific alternative tools or provide detailed when-not-to-use conditions beyond the raw-feed exclusion.

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

C2.7/5.0
Disambiguation2/5

Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.

Naming Consistency2/5

Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.

Tool Count1/5

At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.

Completeness3/5

The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.

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