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signal_ticker

The Ticker — Polymarket finance-ladder scan: every live close-above and touch (first-passage) rung priced against the options-chain CDF (crypto majors, Deribit) or a labeled realized-vol lognormal band; best interval-disjoint divergence that also fills a real position at CLOB depth; ?class=commodities|stocks|crypto (required) [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/ticker.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

B3.3/5.0
Behavior4/5

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

No annotations are provided, so the description fully carries the behavioral burden. It discloses payment requirements, cost per credit, 402 payment-envelope behavior for uncredentialed calls, and the need for X-API-KEY or x402 out-of-band payment. It also explains the algorithm's logic, which gives insight into expected behavior. However, it does not mention rate limits, error responses, or read-only nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense run-on sentence containing multiple technical clauses and payment details, making it difficult to parse. It is not front-loaded or well-structured; key information (payment, class parameter) is buried mid-sentence. The length is justified by complexity, but the lack of formatting and clear separation hurts readability.

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

Completeness3/5

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

Given the tool's complexity (finance-ladder scan, CDF pricing) and absence of output schema, the description provides substantial algorithmic detail and payment context. However, it does not describe the response shape, pagination, or error cases. It partially compensates by pointing to 'instruments' for parameter documentation, but still leaves gaps for an agent to fully understand invocation and expected output.

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

Parameters4/5

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

The input schema is empty, but the description explicitly documents that a 'class' parameter (commodities|stocks|crypto) is required. It also directs users to the 'instruments' tool for per-endpoint parameter docs, compensating for the schema's lack of structured definitions. The description adds meaning beyond the schema, though it does not cover potential additional parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a scan tool for Polymarket finance-ladder signals, specifying the algorithm (close-above/touch rungs priced against CDF or realized-vol bands) and required class parameter. It differentiates from sibling signal tools through its unique 'Ticker' name and specific scan logic, though the technical jargon may obscure the core purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus other signal scanners (e.g., signal_equities, signal_crypto). It mentions the required ?class parameter and payment requirements but lacks guidance on selection context or exclusions. There is no mention of alternatives or use cases.

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

B3.1/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources