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Coil Scanner

get_full_board

The complete LIVE board in one payload: every scored name (~565 across S&P 500, Nasdaq-100, macro), sector lanes, both buy-list lanes, regime and crypto books. No free counterpart — the flagship read. Every name carries its coil read (coil_score / coil_kind / coil_note) and the payload carries the awareness block (leaders, sector flows, coiled, coverage). Fields are self-described in the payload's schema block (key · tier · description · since) — a field added upstream appears there first, no doc refresh needed. $0.25 USDC per call (x402).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden and does more than just say 'read'. It reveals that the result is a single live payload, that fields are self-describing and may appear upstream before docs update, and that each call costs $0.25 USDC. It does not mention auth or rate limits, but the essential behavioral traits are covered.

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

Conciseness5/5

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

The description is dense but each sentence earns its place: scope, component list, per-name fields, dynamic schema behavior, and pricing. The key purpose is front-loaded, and no filler or structural repetition exists.

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

Completeness4/5

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

For a no-input tool with no output schema, this is nearly complete: it names all payload sections, describes embedded self-describing metadata, and states the cost. It is only lightly limited by not mentioning potential failures, latency, or payment mechanics, but those are reasonably inferable.

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?

There are zero parameters and schema coverage is 100% by default, so there is nothing to clarify about arguments. The description focuses on the payload shape instead, and the mention of a self-describing schema block is useful.

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?

Starts with 'The complete LIVE board in one payload', naming the exact resource and scope—every scored name across S&P 500, Nasdaq-100, and macro, plus sector, buy-list, regime, and crypto books. This clearly differentiates it from sibling single-read tools like get_stock_read_live or get_sector_read_live, and 'flagship read' reinforces its unique role.

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?

It gives context that this is the all-encompassing board read and says there is 'no free counterpart', implying it is the go-to for a complete snapshot. However, it never explicitly states when to choose it over the sibling tools or when not to use it, leaving the agent to infer the boundary.

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

B3.2/5.0
Disambiguation3/5

Most market-data and storefront tools are cleanly scoped, but there is meaningful overlap: get_full_board and get_engine_feed are near-duplicates, get_buy_list overlaps with get_morning_brief's candidate list, and get_orders / get_order_status are identical NotOffered stubs. The free/live twin pairs are clearly labeled so agents can distinguish latency/payment intent, but the sheer number of related reads creates selection friction.

Naming Consistency4/5

The large get_* data family is very predictable (get_stock_read, get_stock_read_live, get_rotation, get_rotation_live), and storefront ools use clear verb_noun actions (add_to_cart, remove_from_cart, save_memory). Minor deviations like checkout_handoff and the get_live_setup / get_live_rules naming (which could be misread as *_live twins) keep it from a perfect score.

Tool Count2/5

46 tools is well abovethe 25+ threshold for a coherent toset; at leat13 are direc live twins of free readable plus a 14-tool storefont/memory subdomain. Many could be consoliated into ingle tools with a delay/live param or a single storefront resource, making the surface feel bloated for agents.

Completeness4/5

The market-data surface is very comphehensive: regime, buy-list, hold-state, rotation, sector, stock, crypto day/night/trend/setups, full board, archived board, changes, and engine-feed coverage leave few dead ends for the stated trading-intelligence purpose. Storefont is adequate but has minor acknowledged gaps—orders/order-status are NotOffered stubs and session memories have no update/delete—so it is not a perfect 5.

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