$0.09 via x402: an economist's read of the US curve -- classified regime (inverted/flat/steep, easing/tightening) with 2s10s, 3m10y, term premium, level vs history and cited evidence. Judgment, not a rate feed.
| Name | Required | Description | Default |
|---|---|---|---|
| lookback | No | Trailing sessions for context, default 60 | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It transparently mentions the $0.09 cost via x402 and frames the tool as a judgment/read rather than a feed, implying read-only, non-mutating behavior. It does not explicitly state side effects, but the read-only nature is strongly implied.
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 a single, compact sentence that front-loads the cost and core value proposition. It lists the key outputs without excessive detail, making it easy to scan. Slightly more structure could separate the cost from the functional description, but it remains concise.
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?
The description enumerates the main outputs: regime classification, 2s10s, 3m10y, term premium, level vs history, and cited evidence. It lacks explicit return format or units, but for a judgment-oriented macro tool, this level of detail is reasonably complete. The absence of an output schema raises the need for more clarity, though the description covers the essential content.
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?
Only one of two parameters (lookback) has a schema description, and the description does not explicitly map x_payment to a parameter. The mention of x402 in the description hints at payment mechanics, but it does not clarify the x_payment parameter's role or format. This is a notable gap given 50% schema coverage.
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 identifies the tool's purpose: providing an economist's classification of the US macro curve regime (inverted/flat/steep, easing/tightening) along with key spreads and context. It explicitly distinguishes itself from a rate feed, which sets it apart from the sibling us_treasury_rates tool.
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 phrase 'Judgment, not a rate feed' gives an explicit when-not signal, suggesting this tool is for interpreted regime analysis rather than raw rate data. It does not provide broader use-case guidance, but the contrast with a rate feed is a clear usage pointer.
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.
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.
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.
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.
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.