Skip to main content
Glama

Riley Craig x402 Agent Store

by io.github.rccola990-cloud

opportunity_intelligence

$0.09 via x402: Opportunity Intelligence Signals — scored, agent-actionable signals from US federal spending: each carries value, urgency (0-1), why_it_matters, and a recommended_action, filterable by sector + US state. The intelligence layer (not a raw feed) agents call for real-world money signals. Built on USAspending.gov + proprietary scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional 2-letter US state, e.g. AZ
monthsNoLookback months 1-24 (default 6)
sectorYesSector/keyword, e.g. construction
x_paymentNo

Schema Changelog

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

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. 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 burden. It discloses pricing ($0.09 via x402), the nature of outputs (value, urgency, why_it_matters, recommended_action), and the data source. It omits edge-case behavior like empty results or pagination, but the key behavioral traits are covered.

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 two sentences with no wasted words. It front-loads pricing, purpose, output structure, and filters, and the second sentence clarifies the tool's position. Every clause earns its place.

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?

The description covers inputs (sector, state), output structure, cost, and data source, which is substantial for a tool without an output schema. It lacks information about error behavior or what happens when no signals are found, but it provides a solid overview.

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?

Schema coverage is 75%, so the schema documents most parameters. The description adds filtering semantics for sector and state but doesn't mention months or x_payment beyond the cost reference. It mainly reinforces schema rather than adding substantial new meaning.

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?

The description clearly states the tool provides scored, agent-actionable opportunity intelligence signals from US federal spending. It distinguishes itself from a raw feed, implying a value-add layer, and the resource (federal spending signals) is specific.

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 gives clear context on when to use: when agents need real-world money signals, and explicitly says it is not a raw feed, implying the alternative is raw data. It doesn't name specific sibling tools but provides enough context for differentiation.

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

All Riley Craig x402 Agent Store toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources