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f1ow

by com.f1ow

Positioning Analysis

research_positioning
Read-only

Deterministic positioning read for one coin — the game-state companion to research_regime, answering 'who is crowded, who is paying, who is trapped, and where'. Up to nine axes, each tagged with the market-actor cohort it reads: crowding (annualized funding vs the venue-standard 0.01%/8h anchor, long/short skew, dollars/day the majority pays to hold, crowd-vs-top-trader divergence, and cross-venue funding dispersion), buildup (24h price vs open-interest direction and the funding trend — whether positions are being added into the move or closed out), liquidity (order-book spread and imbalance — makers backing off), basis (perp vs oracle premium — spot-perp froth or hedging pressure, informational), fragility (24h liquidations as a share of market-wide open interest; the axis degrades rather than substituting a single venue's OI, since the liquidation total is cross-exchange), predation (visible Hyperliquid whale positions within 2 daily sigmas of their liquidation price, with defending book depth), trap (share of tracked whale notional underwater on the funding-paying side — attrition fuel), house book (Hyperliquid HLP LP-vault inventory, summed across its child sub-vaults — the literal venue counterparty, so the inventory it carries mirrors how traders are crowded; measured as net over its own gross book, since a market maker's net is a rounding error against venue open interest; informational), and disagreement (the most liquid matching prediction market, volume-gated, informational). Each axis reports its state, the numbers, and a +1/0/-1 fragility vote (+1 clean, -1 crowded/fragile); the stance is the disclosed sum-of-votes rule. Also returns a one-line 'farmed' synthesis (which cohort the board is currently farming, or an explicit statement that none is), focal points — reachable liquidation and breakeven price levels the whole market can see — and 'would change the call' thresholds a polling agent can watch statelessly. Reports the board, not a direction: crowding says who is paying, not where price goes. Deterministic — same inputs, same read. Call research_regime first for market context, then this per coin before sizing or timing decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesCoin symbol, e.g. "BTC" or "ETH".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
axesNo
farmedNoOne-line synthesis of which cohort the board is currently farming. Always populated: when no cohort clears the trap/crowding/predation bars it says so explicitly and names who is paying the carry.
stanceNoOverall positioning-fragility label
symbolNo
axesTotalNo
stanceRuleNoThe exact rule that produced stance
thresholdsNoThe wouldChange lines in machine-readable form for stateless alerting
focalPointsNoPrice levels that are common knowledge on the board, nearest to spot first
wouldChangeNoThresholds that would flip an axis, as prose
axesComputedNo

TDQS

A4.6/5.0
Behavior5/5

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

The readOnlyHint annotation already marks it read-only, and the description adds substantial behavioral context: it is deterministic ('same inputs, same read'), axes degrade gracefully under data limitations ('the axis degrades rather than substituting a single venue's OI'), some axes are flagged 'informational,' and the stance is a disclosed 'sum-of-votes rule.' It also explains the non-directional reporting behavior. No contradiction with annotations.

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

Conciseness4/5

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

The description is long, but the tool is intrinsically complex with nine axes, and each clause is information-dense. It is front-loaded with a one-line summary and usage-before-detail ordering, though the single giant paragraph could be improved with bulleted axes.

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

Completeness5/5

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

For a one-parameter analysis tool with complex semantics, the description covers all behavior an agent needs: every axis, the vote/stance rule, farmed synthesis, focal points, threshold monitoring, determinism, and ordering relative to research_regime. The presence of an output schema lets the description focus on semantics, and nothing material is missing.

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 fully documents the only parameter with 'Coin symbol, e.g. "BTC" or "ETH"' (100% coverage), so baseline 3 applies. The description reinforces that the tool applies to a single coin ('for one coin', 'this per coin'), but it adds no new format or edge-case semantics beyond the schema.

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 opens with a specific verb and resource: 'Deterministic positioning read for one coin,' and frames it as 'the game-state companion to research_regime,' answering a precise question ('who is crowded, who is paying, who is trapped, and where'). It differentiates itself from siblings by describing nine named axes rather than generic insight, so an agent can distinguish it from research_regime and risk_position_size.

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

Usage Guidelines5/5

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

It gives an explicit invocation pattern: 'Call research_regime first for market context, then this per coin before sizing or timing decisions.' It also states what the tool does not provide — 'Reports the board, not a direction: crowding says who is paying, not where price goes' — so an agent knows not to use it as a directional signal. This is clear, actionable usage guidance.

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

A4/5.0
Disambiguation4/5

Tools are grouped by clear prefixes and mostly target distinct resources; market_quotes vs research_token_view, market_top_movers vs market_trending, and funding_current vs market_quotes have some field or purpose overlap, but descriptions draw enough scope boundaries for an agent to choose correctly in most cases. No two tools are truly interchangeable.

Naming Consistency5/5

All 30 tool names use lowercase snake_case with a consistent domain-prefix convention such as market_, flow_, research_, sentiment_, and catalysts_, making the surface predictable. Even helpers like utc_time and risk_position_size fit the same noun-oriented pattern without style mixing.

Tool Count2/5

At 30 tools this set crosses the 'too many' threshold, and several tools reproduce data already available through broader ones such as research_token_view and market_quotes. The breadth is defensible for a crypto-research platform, but the surface feels heavy and could be consolidated.

Completeness5/5

The tool set covers the read-only crypto research workflow thoroughly: market data, candles, derivatives, funding, OI, order book, whale flows, ETF flows, on-chain metrics, news, sentiment, prediction odds, technicals, regime/positioning scans, and position sizing. It also includes health and UTC helpers that close practical workflow gaps, with no obvious dead ends.

Resources