Crypto scanner — dynamic Top-25 by market cap, ETF flows, funding, on-chain, GEX [PAID — signal credit or x402 USDC. Cost: 1 signal credit ($1.70-$2.49/credit by pack size). Uncredentialed calls return the 402 payment envelope; set X-API-KEY on the MCP connection or pay x402 out-of-band at GET /api/scan/crypto.]
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool is paid, requires authentication, and returns a 402 payment envelope for uncredentialed calls. It does not explicitly state whether the operation is read-only, but the nature of a scanner implies it. It also explains payment methods, which is valuable beyond a simple description.
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 two sentences long, front-loaded with the purpose and features, then adding payment and auth details. Every sentence provides necessary information with no filler. The structure is efficient and easy to scan.
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 covers purpose, payment, authentication, and parameter documentation sources. However, it does not mention the expected output format or data structure, which could be important for an agent to interpret results. Given no output schema exists, this omission leaves a gap. Still, for a scanner with no defined parameters, it is fairly complete.
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?
The schema has zero defined parameters, and schema description coverage is 100% (empty). The description adds meaning by directing users to the 'instruments' tool for per-endpoint parameter documentation, which compensates for the lack of parameter details. It does not describe any specific parameters, but given there are none, this is acceptable and useful.
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 states it's a crypto scanner with specific features (dynamic Top-25 by market cap, ETF flows, funding, on-chain, GEX). The verb 'scanner' and resource 'crypto' make the purpose obvious, and the listed features distinguish it from sibling tools, though it doesn't explicitly name them.
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?
It provides clear payment/authentication usage context (uncredentialed calls return 402, set X-API-KEY, pay via x402). It also directs users to the 'instruments' tool for parameter docs. However, it does not explicitly state when to use this tool versus alternatives like scan_crypto_lite, leaving that comparison implicit.
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.
There is significant overlap between scan_* and signal_* tools for the same underlying asset classes, e.g. scan_futures vs signal_futures, scan_racing vs signal_racing, and scan_predmarket vs signal_polymarket. Broader catch-alls like analysis, scan_ask, backtest, and signal_generate also blur the boundary, forcing an agent to parse long pricing details before knowing which tool actually applies.
The overwhelming majority of tools follow a clear `scan_` or `signal_` snake_case prefix, which makes the product families easy to recognize. A small set of standalone unprefixed tools — analysis, backtest, instruments, leaderboard, quote, track_record — breaks the pattern, but the overall scheme is still consistent enough to infer.
47 tools is far beyond the practical range for an agent to reason about, even though the server's domain is broad and heavily segmented. Many specialist endpoints could be consolidated under fewer catch-all scanners and signals, but the exposed surface instead forces a large tool-selection decision on every request.
The tool surface covers discovery, cost preview, sample analysis, public track records, leaderboards, broad market scanning, asset-class-specific scanning, sports and event signals, and prediction-market verticals. There are minor gaps in explicit account/credit management and some redundant paths, but for a signal/research service the workflow is largely complete.