Tokenization-trend radar — RWA listings/flows intelligence [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/signal/tokenization-radar.]
| Name | Required | Description | Default |
|---|---|---|---|
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description discloses key behaviors: it is a paid endpoint, costs 1 signal credit, returns a 402 payment envelope for uncredentialed calls, and requires X-API-KEY or x402 out-of-band payment. The endpoint is explicitly GET, implying a read-only operation. This is meaningful behavioral context, though rate limits or response details are not mentioned.
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 one dense but purposeful sentence, front-loaded with purpose before payment/auth details. Every clause adds relevant information (cost, auth, failure behavior, endpoint). Slightly cluttered due to parentheses and multiple clauses, but efficient overall.
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?
Given no output schema and no annotations, the description should explain return values and usage scenarios more. It states the tool provides 'RWA listings/flows intelligence' but not what the response contains, how results are structured, or when to choose this over closely named siblings. Payment and auth are well covered, but output and context are incomplete.
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 properties, so the baseline is 4. The description notes 'Query parameters' and directs users to call the free 'instruments' tool for per-endpoint parameter docs, adding a practical pointer for obtaining semantics not in the schema. It does not enumerate actual parameters, but with 0 params the baseline holds.
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 as a 'Tokenization-trend radar' providing 'RWA listings/flows intelligence', which names the domain and resource distinctly from many sibling tools. It lacks a specific verb (e.g., 'list', 'scan'), but the purpose is evident and not tautological.
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 description covers payment and authentication requirements but gives no guidance on when to use this tool versus alternatives like signal_tokenized or signal_ticker. It only points to the 'instruments' tool for parameter docs, which is not usage guidance. No exclusions or use-case context are provided.
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.