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signal_gaia

Gaia — Polymarket climate/severe/geo scan (hottest-year rank, Arctic sea-ice min, earthquake counts/thresholds, tornado counts, bolides, VEI-6, hurricane landfalls): official-catalog floors + Poisson/empirical base-rate bands vs every book, best divergence; ?family=quake_count narrows [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/gaia.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.6/5.0
Behavior4/5

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

The description discloses important behavioral traits: it is a PAID tool (1 signal credit, $1.70-$2.49/credit), requires authentication (X-API-KEY or x402), and uncredentialed calls return a 402 payment envelope. These details go beyond the lack of annotations and inform the user about costs and error handling.

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 verbose and poorly structured, mixing purpose, parameter hints, pricing, and authentication in a run-on sentence with repeated information (e.g., the bracket repeats the paid/auth details). It is not front-loaded with the most critical info, and the redundancy detracts from clarity.

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 empty schema and no output schema, the description provides a reasonable overview of the tool's function and a single parameter hint, but it lacks full parameter documentation and expected output details. It explicitly redirects to another tool for parameter docs, so it is not fully self-contained.

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?

The schema has no defined parameters (only additionalProperties), and the description provides a single parameter hint (?family=quake_count narrows). While this adds some value, it does not fully compensate for the empty schema; the description itself points to the 'instruments' tool for parameter docs, indicating incomplete parameter semantics.

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's purpose as a Polymarket climate/severe/geo scan, listing specific data types (hottest-year rank, Arctic sea-ice min, earthquakes, tornadoes, bolides, VEI-6, hurricane landfalls) and its function of comparing official-catalog floors and base-rate bands to find the best divergence across books. This distinguishes it from sibling signal_* tools.

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

Usage Guidelines3/5

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 alternatives. It provides a parameter hint (?family=quake_count) and mentions that the 'instruments' tool has per-endpoint docs, but no clear guidance on selection criteria. The domain context (climate/severe/geo) is implicit but not framed as a usage directive.

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