LiquidVision — Crypto Derivatives Data

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

With no annotations provided, the description carries the full transparency burden. It discloses that the answer is grounded, computed from live derivatives data, and scoped across specific venues, which is useful behavioral context. However, it does not describe output structure, request constraints, rate limits, or whether there are any side effects beyond returning an explanation.

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 a single front-loaded sentence with no filler. The example question and the parenthetical list of explanation components convey substantial meaning very efficiently.

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?

For a simple 2-parameter tool with no output schema, the description communicates purpose, expected answer content, and data sources. It omits exact return formatting and symbol parameter behavior, but these are secondary given the example and the schema default.

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?

Schema description coverage is 0%, so the description must compensate. It adds strong semantics for 'question' with a natural-language example and describes the analytical content of the result. It does not explain how or when to set 'symbol' or what valid symbols are, though the schema default partly mitigates this.

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 states this tool produces a synthesized risk-intelligence answer to a natural-language 'why' question, with concrete output components like liquidation cascades and funding extremes. It is clearly distinguished from the get_* sibling tools, which retrieve individual data slices rather than explanations.

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 gives an explicit example question ('Why did BTC drop 4%?') and describes what the returned explanation will cover, making intended usage clear. It does not explicitly name alternative tools or state when not to use it, but the contrast with raw-data sibling tools is strongly implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It describes the data coverage (open interest, funding, liquidations) but does not mention operational aspects such as data freshness, potential latency, or whether it aggregates live data. For a read-only 'get' tool, the side effects are minimal, but the description does not explicitly confirm it is read-only or disclose any limitations. This is adequate but not comprehensive.

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 concise and front-loaded with the core concept 'Arbitrum perps board'. It then enumerates the key metrics in a compact list and ends with a practical usage hint. It avoids redundancy and stays within two sentences, making it efficient while still informative.

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 the tool has no parameters and no output schema, the description needs to convey what the agent will receive. It does that by listing the included metrics (open interest, funding, liquidations) and the comparison against CEXs. It does not specify the exact response format (e.g., list of coins), but for a board-style tool this is likely evident from the metrics. The description is sufficiently complete for an agent to decide whether to call it.

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 tool has zero parameters, so the schema provides no parameter documentation. Per the baseline, with 0 params the score is 4. The description does not need to add parameter details, and it doesn't attempt to. It focuses on the content of the board, which is appropriate.

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 states it is an 'Arbitrum perps board' covering GMX v2 open interest per coin, funding, and liquidations. The verb 'get' plus the specific resource (Arbitrum perps board) makes the purpose clear. It does not explicitly contrast with sibling tools like get_funding_rates or get_liquidations, but the composite nature and the focus on Arbitrum vs CEXs differentiates it enough for an agent to infer its scope.

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 provides a clear usage scenario: 'Ask this for how is Arbitrum positioned vs CEXs'. This is explicit guidance on when to invoke the tool. It does not list exclusions or alternatives, but the use case is sufficient for an agent to decide between this and more specialized tools like get_funding_rates or get_liquidations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It does disclose a key interpretation detail—'Positive = money flowing in'—which adds useful behavioral context. However, it does not mention data source, aggregation method, or whether any filtering is applied, though for a read-only data tool this is a reasonable disclosure level.

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?

Two short sentences with zero filler. The first sentence front-loads the metric and its dimensions, the second adds a clarifying semantic note. Every word earns its place.

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 zero parameters and no output schema, the description sufficiently conveys the data's dimensions and sign interpretation. It does not specify the exact return shape (e.g., rows grouped by exchange and symbol), but for a simple data retrieval tool this is nearly complete.

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 tool has zero parameters, and schema description coverage is 100% vacuously. Per the baseline for no-parameter tools, a 4 is appropriate. The description adds context about the fixed windows (1h/4h/24h) but no parameter-specific semantics are needed.

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 identifies a specific resource—'Derivatives cash flow per exchange'—and elaborates with precise dimensions: 'net USD into/out of open interest over 1h/4h/24h windows, per core symbol.' It clearly distinguishes this from sibling tools like get_open_interest (which reports levels, not flows) and get_cvd, even without naming them.

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 gives no guidance on when to use this tool versus alternatives. It does not mention what question it answers, when to prefer a sibling, or any exclusions. The agent is left to infer the use case from the metric description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden and does add substantive behavior: it defines the size buckets and interprets divergence. It does not disclose the exact delta computation or windowing behavior, but the provided definitions and signal interpretation go meaningfully beyond the bare tool name.

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?

Two tight sentences with no filler. The core metric and thresholds are front-loaded, and the signal interpretation is a single clause at the end.

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?

The metric and its segmentation are well explained, but with no output schema and no parameter guidance, the return format and the effect of the minutes parameter remain unstated. It works as an overview but not as complete call-time guidance.

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?

Schema description coverage is 0% and the description does not mention symbol or minutes at all. The agent must infer their meaning from names and defaults; the size-bucket definitions do not clarify what time window 'minutes' controls or accepted symbol formats.

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 states a specific metric (cumulative volume delta) and its segmentation by trade size with exact thresholds (retail <$10K, mid <$100K, large <$1M, whale >$1M). It also adds an interpretive signal (whale-vs-retail divergence = smart-money signal), which clearly distinguishes it from sibling analytics tools.

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 implies a use case by framing whale-vs-retail divergence as a smart-money signal, but it never states when to prefer get_cvd over siblings such as get_microstructure or get_footprint, nor does it give explicit exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the aggregation structure (candle x price bin) and the data sources (Binance + Bybit trade streams), but it does not disclose limitations, expected latency, or how the returned footprint is shaped.

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?

One dense sentence conveys the resource, granularity, metrics, and data sources with no filler. It is appropriately front-loaded.

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?

The description is a useful summary but incomplete for invocation: all three parameters are undocumented in text, there is no output schema, and no guidance on parameter units or allowed values. For a tool with no annotations, this is a meaningful gap.

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?

Schema coverage is 0% and the description does not explain `step`, `minutes`, or acceptable `symbol` values. It only hints that symbols are 'core perp' symbols, so agents must guess the semantics of the numeric parameters.

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's output: order-flow footprint data broken down per candle and price bin, with taker buy/sell USD volume and delta. This is specific enough to stand apart from sibling market-data tools like get_cvd or get_orderbook_heatmap, though it omits an explicit action verb.

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?

No guidance is given on when to choose this tool over alternatives such as get_microstructure or get_cvd. The description does not mention use cases, prerequisites, or exclusions, leaving the agent to infer selection based on the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does well: it explains median-over-window behavior, annualization per settlement cycle, and why the latest print is avoided. It doesn't cover rate limits or data availability, but the core computational behavior is transparent.

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?

Two tight paragraphs with the core concept front-loaded and every clause adding methodological detail. No filler, no repetition of the tool name or schema fields.

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?

For a tool with no output schema, the prose describes the per-coin outputs (spread, long venue, short venue), annualization, and ranking window. It falls slightly short by not stating how `limit` or `min_venues` affect the returned list, but the essential behavior is covered.

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 description adds meaningful semantics to `window` by tying it to the median calculation and noise rationale. However, `limit` and `min_venues` are left completely unexplained despite 0% schema description coverage, so two of three parameters rely only on their names and defaults.

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 first sentence names the exact resource and metric: coins ranked by how far funding disagrees across exchanges. The level of specificity — spread in APR points, venue to hold long/short — clearly differentiates it from sibling funding tools even without naming them.

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?

No explicit 'use this when' or 'use X instead' guidance appears. The intended use is implied by the definition, but there are no exclusions or comparisons to similar funding-related tools like get_funding_rates or get_funding_history.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns 'bucketed' historical data constrained by the `minutes` range, which implies a read-only operation. However, it does not explain bucket granularity, pagination, or behavior for invalid inputs, leaving some behavioral uncertainty.

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 a single sentence with no filler. It front-loads the core concept ('bucketed funding-rate history'), then adds the concrete symbol example and the time range constraint. Every word contributes useful information.

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 the low complexity (two parameters, simple types) and the presence of an output schema, the description covers the essential use case and parameter semantics adequately. It does not explain return fields, but the output schema likely covers that. The only minor gap is lack of detail on 'bucketing' behavior, but overall it is sufficient for correct invocation.

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?

Schema description coverage is 0%, so the description must compensate. It does so by giving a concrete symbol example (BTCUSDT) and specifying the valid `minutes` range (5-20160), adding meaning the raw schema lacks. It does not fully specify formatting or constraints beyond the range, hence not a 5.

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 resource as 'Bucketed funding-rate history per exchange for a symbol' with a concrete example (BTCUSDT) and a time window. It distinguishes itself from sibling tools like get_funding_rates by emphasizing 'history' and 'per exchange', though it does not explicitly name an alternative.

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 implies usage by explaining what the tool returns (historical, bucketed funding rates over a specified recent period) but provides no explicit guidance on when to prefer this over get_funding_rates or other funding-related siblings, nor are any exclusions or alternative tools mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It discloses the sign convention ('Positive rate = longs pay shorts'), which is a non-obvious behavioral detail, and clarifies the scope ('every tracked exchange/symbol pair'). It does not mention any limitations like rate limits or pagination, but given it is a simple read-only tool with no parameters, the disclosed behavior is adequate.

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 two sentences with no fluff. The primary purpose is stated first, and the second sentence adds a valuable clarification about the sign meaning. It is front-loaded and every word earns its place.

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?

For a parameterless tool with an output schema, the description is complete. It states what data is returned and the scope, while the output schema presumably defines the response structure. Nothing needed to invoke the tool correctly is missing.

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 tool has zero parameters, and the input schema is empty. The baseline for 0-parameter tools is 4, and the description adds no parameter details because there are none to document. It correctly focuses on output semantics instead.

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 states the tool retrieves latest perpetual funding rates and mark prices for every tracked exchange/symbol pair. The verb 'get' and resource are specific, and the 'latest' qualifier distinguishes it from historical tools like get_funding_history and get_funding_dispersion, even without naming them explicitly.

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 provides no guidance on when to use this tool versus alternatives such as get_funding_history for historical data or get_funding_dispersion for distribution analysis. It simply describes what it returns, leaving the agent to infer its appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the burden. It discloses the data source (Binance and Bybit), the row granularity (one per settlement), the lookback limit (730 days), and the distinction from live data. However, it doesn't mention pagination, rate limits, or what happens when no data exists for a symbol. The description adds useful behavioral context but not exhaustive.

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?

Two sentences with no waste. The core purpose is front-loaded, and the sibling distinction is placed at the end. Every sentence earns its place.

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?

For a simple 3-parameter tool with no output schema, the description covers the main purpose, data source, time range, and sibling distinction. It doesn't explain return format or exchange parameter values, but the tool is relatively simple. The description is complete enough for an agent to select and invoke it correctly in most cases.

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?

Schema description coverage is 0%, so the description must compensate. It mentions 'any pool coin (e.g. BTCUSDT)' which clarifies the symbol parameter, and 'up to 730 days' which bounds the days parameter. However, it doesn't explain the exchange parameter's possible values or the default behavior of 'all'. The description adds some meaning but leaves gaps.

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 states the tool returns settled funding rates, one row per settlement, from Binance and Bybit history, up to 730 days for any pool coin. It also distinguishes itself from get_funding_history, which is the live 10-second archive since July 2026. This is a specific verb+resource with clear scope and sibling differentiation.

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 explicitly says this is the backtest series and names the alternative get_funding_history for live 10-second data. This gives an agent clear guidance on when to use this tool vs the sibling. It also implies usage for historical/backtest analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It discloses that the data is measured (not modeled), covers only large accounts, and lists the temporal range options. It also gives a leverage recommendation. This is transparent about scope and range, though it doesn't discuss rate limits or authentication, which are typically not critical for a read-only data tool.

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 packs substantial information into one sentence: source (Hyperliquid), type (MEASURED), coverage, contents (bins, notional, curves, positions), and range options. It is efficient and front-loads the key qualifier 'MEASURED (not modeled)'. Slight run-on but overall well-structured.

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, the description outlines the expected output: binned positions, notional per bin, cumulative curves, and largest positions. It also covers temporal range and coverage limitation. This is adequate for an agent to understand what it will receive, though it doesn't detail response format or pagination. For a tool of moderate complexity, this is fairly complete.

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?

Schema coverage is 0%, so the description must compensate. It explicitly lists valid values for 'range' (12h | 1d | 3d | 7d | max), which is helpful. However, it does not describe the 'symbol' parameter beyond implying it's a trading pair. The description adds value for range but leaves symbol semantics implied, not fully compensating for the lack of schema docs.

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 states a specific resource (Hyperliquid liquidation map) with distinctive qualifiers 'MEASURED (not modeled)' and 'Partial coverage: large accounts only'. This differentiates it from siblings like get_liquidation_map or get_liquidation_heatmap, and clearly conveys the data content: bins, notional per bin, cumulative curves, and largest positions.

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?

It provides context on when to use it: when measured rather than modeled data is needed, and notes the coverage limitation ('large accounts only'). It also gives a usage recommendation ('large accounts run low leverage'). However, it does not explicitly name alternative tools or state when not to use it, so explicit exclusions are missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full behavioral burden. It transparently discloses that the tool aggregates raw data into discrete events and explains what each event contains, including the market_wide composite. However, it does not disclose pagination, result ordering, the meaning of the 'normal minute' baseline, or any rate/auth considerations. It discloses the core transformation but not operational details.

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 front-loads the core purpose in the first sentence, then uses a tight list to enumerate return fields, and closes with the market_wide special case. Every sentence earns its place and the layout is logical. Slightly verbose in the field enumeration, but well-organized for an agent to parse.

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?

Because there is no output schema, the description correctly takes on the burden of explaining return values, and it does so thoroughly. However, the two parameters are completely undocumented in both schema and description, and with no annotations the operational context (limits, defaults, ordering) is left for the agent to guess. Reasonably complete on the output side, incomplete on the input side.

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?

Schema description coverage is 0% and the description never mentions the 'hours' or 'limit' parameters at all. While their purpose (time range and result cap) is inferable from their names, the description provides zero guidance on semantics, valid ranges, or defaults beyond what the schema's default values show. With low coverage, the description was obligated to compensate and did not.

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 uses a specific verb-resource pair ('Liquidation cascades as discrete events') and explicitly contrasts with 'a raw feed,' which is a sibling tool (get_liquidations). It enumerates the exact fields returned (coin, side, duration, USD totals, price moves), making the tool's function unmistakable and distinguishing it from its siblings.

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 phrase 'rather than a raw feed' implies the tool is the aggregated/event-level alternative to a raw liquidation feed, but it never names the sibling (get_liquidations) or states explicit conditions for when to choose this over the raw feed or the heatmap/map siblings. Usage context is implied, not explicit, and no exclusions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the behavioral disclosure burden. It discloses that the heatmap is 'modeled' and 'estimated' rather than observed, and it reveals the output shape (grid plus price path). However, it says nothing about data sourcing, update cadence, availability constraints, or how 'estimates' are derived, leaving meaningful behavioral uncertainty.

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 compact and front-loaded: it opens with the core resource, adds the key structural detail (grid and price path), then lists range options. Both sentences carry useful information with no filler.

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?

For a tool with no output schema, the description does explain the return shape, which helps. But it is incomplete in context: it does not clarify how 'modeled' differs from sibling liquidation tools, does not document symbol/exchange semantics, and provides no interpretation guidance for the grid intensity values. These are meaningful gaps for a 3-parameter tool in a crowded sibling group.

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?

Schema description coverage is 0%, so the description must compensate for all three parameters. It only explains 'range' with explicit allowed values ('12h | 1d | 3d | 7d'). 'symbol' and 'exchange' are left entirely undocumented—no format, no allowed values, no default semantics beyond the schema's own 'default: all'.

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 states a specific resource ('modeled liquidation heatmap') and adds the output structure ('estimated cluster intensity per price bin over time (grid[time][bin]) with the price path'). It is more than a tautology and conveys what the tool returns. However, it does not explicitly differentiate from siblings like get_liquidation_map or get_hl_liquidation_map, relying on 'modeled' to imply a distinction.

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?

There is no guidance on when to use this tool versus the many liquidation-related siblings, no exclusions, and no reference to alternatives. The description only lists range options; it never tells an agent what scenarios call for this modeled heatmap rather than a raw liquidation map or liquidation summary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the behavioral-transparency burden. It usefully discloses that the data is 'Modeled' and 'estimated', which warns the agent the output is approximate, but it does not mention data limitations, auth needs, rate limits, or other behavioral caveats.

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 front-loaded with the core purpose and packs parameter constraints into a short trailing line. There is no filler, though the first sentence is dense and could be structured into smaller pieces for easier parsing.

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?

For a complex analytical tool with no output schema and no annotations, the description covers the main returned components and parameter choices. However, it does not fully explain interpretation details such as how cumulative curves are computed or what 'current price' refers to, so it is callable but not fully self-contained.

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?

Schema description coverage is 0%, so the description must compensate, and it does: it gives the symbol format example (BTCUSDT), enumerates allowed range values (12h/1d/3d/7d), and lists exchange options (binance/bybit/okx/all). This provides the key value constraints that the schema omits.

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 states what the tool returns: modeled liquidation-cluster notional per price bin, split by leverage tier and exchange, plus cumulative long/short curves for a perp symbol. This is specific and largely distinguishes it from liquidation siblings, though it does not explicitly name an alternative.

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?

No guidance is given on when to use this tool versus get_hl_liquidation_map, get_liquidation_heatmap, get_liquidation_cascades, or get_liquidations_summary. The 'for a perp symbol' phrasing implies usage, but there are no when-not conditions, prerequisites, or alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the output fields (side, price, qty) and the time range, which is useful, but it does not mention aggregation details, data freshness, potential pagination, or any limitations beyond the minutes range. It is not contradictory, but it is not rich enough for a 4.

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 two sentences: the first states the primary function and the second clarifies the 'side' semantics. It is front-loaded with the action and result fields, and every sentence earns its place without redundancy. Perfectly sized for the tool's simplicity.

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 the tool's simplicity (one parameter, no required fields, and an output schema presumably covering return structure), the description covers the essential aspects: what data is returned, the time window meaning, and the interpretation of a key field. It does not elaborate on exchange coverage or data latency, but these are likely minor for an agent that can inspect the output schema. A 4 is appropriate.

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 only one parameter, 'minutes', with 0% description coverage. The description compensates by explaining that it specifies the trailing time window and constrains it to 1-1440. It also clarifies the 'side' field in the output, which is not a parameter but aids understanding of the result. This is sufficient for a simple single-parameter tool.

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 states the tool returns recent liquidation events with side, price, and qty across exchanges over a specified time window. It defines the 'side' field meaning, which distinguishes it from sibling tools like liquidation maps or cascades that focus on spatial or cascade analysis. The verb 'get' plus resource 'liquidation events' is specific and unambiguous.

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 implies usage: use this to retrieve raw liquidation events for a given number of minutes (1-1440). It clearly defines the time range and the meaning of 'side', giving context. However, it does not explicitly exclude alternative tools or mention when to prefer this over get_liquidations_summary or get_liquidation_map, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden of explaining behavior. It usefully discloses the return content: totals split by long/short, per-symbol and per-exchange breakdowns, and a 30-min time series. However, it does not clarify edge cases such as how the 'minutes' window interacts with the fixed 1h/4h/12h/24h sub-windows, or any limitations.

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 a single efficient sentence, front-loads the main purpose, and enumerates the output components without fluff. Every clause contributes useful information.

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 there is no output schema spend no annotations, the description does a reasonable job listing return categories. However, it omits enough information about the minutes parameter's boundaries and the relationship between the requested window and the fixed 1h/4h/12h/24h buckets to be fully complete for an agent deciding how to invoke it.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaning by stating stats are 'over the last `minutes`', connecting the parameter to the query window. Yet it offers no range, units beyond the name, or guidance on valid values, leaving some ambiguity about how minutes interacts with the fixed sub-windows.

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 an aggregated market-wide liquidation summary with specific breakdowns (long/short totals, per-symbol, per-exchange, time series). It partially distinguishes itself from siblings like get_liquidations or get_liquidation_map by emphasizing 'summary' and 'market-wide', though it does not explicitly name them.

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?

No explicit guidance is given about when to choose this tool over the many liquidation-related siblings. The description implies it is for broad market-wide summary use rather than maps or cascades, but it never states exclusions, prerequisites, or alternative conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It does add useful context: the result is 'latest', 'global', and broken down 'per exchange/symbol'. However, it does not explicitly state that this is a read-only operation, what aggregation window is used, or how the data is sourced. This is minimal but not entirely absent.

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 a single concise sentence that front-loads the key qualifiers: 'Latest', 'global', 'long/short account ratio', and 'per exchange/symbol'. Every word adds meaning and there is no redundant or filler content.

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?

For a zero-parameter tool with an output schema present, the description is mostly sufficient. It communicates the metric, scope, and temporal nature. It could be slightly more explicit about whether the response includes all exchanges/symbols at once, but the output schema likely resolves that.

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 tool has zero parameters and the schema has 100% coverage, so there are no parameter semantics to document. The description's mention of 'per exchange/symbol' hints at the shape of the response rather than at input parameters. This matches the baseline for a no-parameter tool.

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 resource (global long/short account ratio) and the scope (per exchange/symbol), and the 'Latest' qualifier adds temporal specificity. It does not explicitly include a verb like 'get', but the tool name supplies that. It does not differentiate from sibling market-data tools, so it stops short of a 5.

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?

No guidance is given about when to use this tool versus alternatives such as get_funding_rates or get_positioning_regime. The description implies it is used for the latest long/short ratio, but it provides no exclusions, prerequisites, or comparison to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden. It fully discloses the tool's determinism, lack of LLM involvement, and cost (free), which are key behavioral traits. It doesn't explicitly state side effects (e.g., rate limits, idempotency), but for a read-only data tool, this is sufficient.

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 a single sentence listing all data components in a comma-separated list, making it dense but efficient. It front-loads the deterministic nature and ends with an actionable use case. No wasted words.

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 a single optional parameter and no output schema, the description sufficiently outlines what data the tool returns and its purpose. It lacks explicit mention of output format or limitations, but for a data brief tool, the enumerated data points are comprehensive enough.

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?

Schema coverage is 0%, but there is only one parameter, symbol. The description clarifies that it is a perp symbol and provides a default (BTCUSDT) in the schema, but doesn't add format details (e.g., case sensitivity, supported exchanges). Baseline for low coverage but simple params.

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 states it provides a deterministic market brief for a perp symbol, enumerating specific data points (liquidations, funding, OI, flows, risk zones, positioning). It distinguishes itself from siblings by emphasizing determinism and no LLM involvement, though it doesn't name a specific sibling.

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?

It implies use as an input for 'your own reasoning' and is free, but it doesn't explicitly state when to use this over alternatives like get_liquidations or get_open_interest. No exclusions or alternatives are mentioned, but the data scope is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the behavioral disclosure burden. It explains the measurement approach (narrow band around each venue's own mid), the source limitation (only innermost levels), and warns against extrapolation. It does not cover auth, rate limits, or return envelope, which are less critical for this kind of read-only data tool.

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?

Three tight paragraphs: definition, metric detail, limitation. No filler, and the critical negative scope is placed as a deliberate boundary. Every sentence adds information.

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?

For a read-only data tool without an output schema, the description is unusually complete about the content and limitations. It falls short only by not explaining the `minutes` parameter or the exact structure of the returned per-minute history, so an agent may still be uncertain how to set the sole argument.

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 documentation coverage is 0% and the description never mentions the only input, `minutes`. An agent must infer that `minutes` controls lookback or history length from context; the description adds no explicit meaning beyond the schema. With one simple optional parameter this is not fatal, but it is a real gap.

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?

States a specific object ('cross-venue order book microstructure'), the assets (BTC, ETH, SOL), and enumerates the exact metrics returned: spread in bps, immediately-executable depth, bid share, basis, and per-minute spread history. The negative scope statement also makes clear what the tool is not, helping distinguish it from extrapolative market-impact queries.

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 provides clear context for when the tool is relevant and explicitly states a when-not: it does not answer 'what moves price 0.5%' because streams only carry innermost levels. It does not, however, name a specific sibling alternative or list conditions that should route an agent to another tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden. It transparently enumerates the response contents (OI in coins/USD, market share %, 1h/4h/24h changes, OI/volume ratio, market totals), giving a clear picture of what the tool computes. It does not disclose data freshness or sort order, but the behavioral disclosure is strong for a read-only reporting tool.

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?

A single dense sentence front-loads the tool's core purpose ('Per-venue open-interest ranking'), then efficiently lists the returned metrics. There is no filler or redundancy; every clause adds value.

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 a single parameter, no output schema, and no annotations, the description does an excellent job of enumerating the output fields and scope. It could add ordering/freshness details or explain 'market totals' more explicitly, but the essential information needed to understand and invoke the tool is present.

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?

Schema coverage is 0% and the only parameter is a bare string 'symbol'. The description compensates by specifying it is a perp symbol and providing a concrete example (BTCUSDT), which gives meaningful guidance on the expected value format.

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 states a specific verb-resource pair: 'Per-venue open-interest ranking for a perp symbol' and enumerates the exact metrics returned. The phrase 'per-venue' and 'per exchange' clearly differentiates it from sibling tools like get_open_interest and get_open_interest_history.

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 implies usage: call when you need aggregated OI broken down by exchange, with change % and USD flow. However, it does not explicitly mention when not to use it or name alternative tools, leaving some routing to inference based on sibling names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

There are no annotations, so the description carries the behavioral burden. It discloses that the output is the latest OI denominated in contracts and USD and organized by exchange/symbol, but it does not mention data coverage, update cadence, or that this is a read-only global snapshot. These omissions are minor given zero parameters, but they keep this at a middling score.

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?

One hyphenated phrase and no filler; every word contributes either the metric name, units, or granularity. The description is front-loaded and appropriately sized for the tool's simplicity.

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?

For a parameterless tool with an output schema, the description is nearly complete: it specifies what data is returned and in what units. It could add a note that no filters are applied and a pointer to get_open_interest_history for time series, but that is more of a usage-guideline enhancement than a completeness blocker.

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 input schema has zero parameters, so there is nothing for the description to explain. The description's mention of exchange/symbol granularity is consistent with a no-argument global query, giving the baseline of 4.

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 identifies the resource (open interest), the units (contracts and USD), and the granularity (per exchange/symbol), so an agent can tell it is a market-data query. 'Latest' hints at the snapshot nature, though it does not explicitly differentiate from sibling get_open_interest_history.

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?

No guidance is provided about when to choose this over get_open_interest_history, get_oi_board, or other OI tools. The 'latest' qualifier implies a current snapshot, but the description does not state exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the valid minutes range but does not disclose output shape, data granularity, ordering, or any access constraints, which is minimal for a data retrieval tool.

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 a single concise sentence with no waste. It front-loads the core resource and time window without redundant phrasing.

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?

An output schema exists, reducing the need to describe return values. However, the description omits material details such as data granularity per point, timezone handling, and whether the window is inclusive/exclusive, which are relevant for a time-series tool.

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?

Schema description coverage is 0%, so the description must compensate. It provides a symbol format example and a valid minutes range, which adds value, but it leaves the interval meaning of each minute and default behavior to be inferred from the schema.

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 states that the tool returns an open interest time series over a recent minute window, with a concrete symbol format example. It is distinguishable from 'get_open_interest' by the 'time series' framing, though it does not explicitly name sibling alternatives.

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?

No guidance is provided on when to use this tool versus siblings like get_open_interest or get_oi_board. The description implies a historical time-series use case but leaves the agent to infer the selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It does disclose the key behavioral trait: the output is a time x price heatmap grid of resting order book liquidity. It does not mention response format details, exact bucket resolution, or read-only/safety semantics, but the data-shape disclosure is meaningful and non-tautological.

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 a single concise sentence with no filler. The core artifact and scope are front-loaded, and the parenthetical examples efficiently convey the essential parameter constraints without bloating the text.

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?

With no output schema and no annotations, the description should carry more weight. It does a reasonable job of explaining the returned grid structure and the exchange/symbol scope, but it omits minutes semantics and gives only loose examples of valid values, leaving a moderately important gap for a 3-parameter tool.

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?

Schema description coverage is 0%, so the description must compensate. It gives example values for exchange (binance/bybit/okx) and symbol (BTCUSDT), but it does not specify exact allowed values or formats, and it says nothing about the minutes parameter. The description adds partial meaning for two parameters but leaves a required parameter concept unexplained.

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 states exactly what the tool returns: an order book liquidity heatmap as a time x price grid with resting quantity, for a single exchange and symbol. This scope and artifact type clearly distinguish it from sibling tools like get_liquidation_heatmap and get_hl_liquidation_map, even without naming them.

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?

There is no statement about when to choose this tool over alternatives such as get_liquidation_heatmap or get_microstructure. The one-exchange/symbol scope implies a use case, but the description gives no explicit when/when-not guidance, which is especially important given the large sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently explains the four regime combinations and states exactly what is returned: a market summary plus per-coin price change, OI change, and current OI over the window. It does not discuss rate limits or auth, but for a read-style analytics tool this is sufficient.

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 efficient and front-loaded: it states the core concept first, then explains the regime logic, then lists the return contents. Every sentence adds value, and the four regime explanations are compact despite their density.

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 the single optional parameter, no annotations, and no output schema, the description is remarkably complete. It covers the input window, the calculation logic, and the full return shape. An agent has enough information to call the tool and interpret its results without additional context.

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?

Schema coverage is 0%, so the description must compensate. The only parameter, minutes, is indirectly described as 'over the window,' which implies minutes is the lookback window. This adds some meaning beyond the raw schema but does not explicitly define units, allowed range, or how the default interacts with the window.

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 states a specific verb (get), a specific resource (positioning regime), and the exact construction (price direction crossed with open interest). It distinguishes itself from sibling tools by describing a unique analytical output that no sibling name suggests.

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 clearly implies when this tool is appropriate: whenever a positioning regime based on price and open interest direction is needed. It does not explicitly name alternatives or exclusions, but the behavior and output are specific enough that an agent can select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the disclosure burden. It does well by marking the output as heuristic and specifying the classification categories and decision criterion. It omits some details like data source and side effects, but for an analytical get tool the core behavior is clear.

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?

A single sentence with no filler; the core concept is front-loaded and the enumerated categories/criterion are dense but scannable.

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?

The description explains the heuristic logic, but with no schema descriptions, no output schema, and no usage context, it leaves parameter semantics and selection-vs-siblings incomplete. A tool with three nondescript parameters needs more to be safely invoked beyond defaults.

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?

Schema description coverage is 0% and the description mentions none of the three parameters (symbol, minutes, exchange). Defaults in the schema are the only hint, so the agent cannot know what 'minutes' means or which exchange values are valid.

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 names a specific resource ('order-book wall lifecycle') and a concrete behavior ('large levels classified as...'), even listing the four output states. This makes it easy to distinguish from sibling market-analysis tools despite not naming them.

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 mechanism 'based on whether trades executed into them before they vanished' implies use for spoofing/wall-lifecycle detection, but there is no explicit when-to-use or when-not-to-use guidance and no alternatives named among the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It discloses the ~15-min delay, which is a critical data freshness trait, and explicitly lists the data fields returned (price, day change %, range, volume). This gives the agent a clear picture of what to expect. It does not mention read-only status, but that is implied by 'get' and the nature of quotes. It also does not discuss rate limits or authentication, but those are common and not required for a basic quote tool. Overall, it provides meaningful behavioral context beyond the name.

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 a single sentence that front-loads the essential information: what is returned, the asset scope, and the key data points. Every element earns its place — the ticker examples, the list of fields, and the delay note. There is no fluff, no repetition, and no unnecessary explanation. It is tightly written and immediately readable.

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 that the tool has no parameters and no output schema, the description does a good job of conveying the return content and the asset universe. It mentions the delay, which is relevant for usage decisions. It could go slightly further by stating whether the response is a list or a single object, but the listed fields are sufficient for an agent to infer the shape. For a simple quote tool, this is reasonably complete, though not exhaustive.

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 tool has zero parameters, so there is nothing for the description to clarify beyond what the schema already shows (an empty object). Per the rubric, the baseline for 0 parameters is 4, and the description does not introduce any confusing parameter behavior. It adds no parameter-specific semantics because none exist, so the baseline is appropriate.

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 states a specific verb ('get'), a specific resource ('stocks' limited to crypto-related equities and ETFs), and enumerates the exact tickers (COIN, MSTR, miners) and asset classes. It also lists the data fields (price, day change %, range, volume), which distinguishes it from the sibling tools that focus on derivatives, funding, liquidations, etc. The scope is unambiguous and immediately differentiates it from the rest of the set.

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 gives no explicit guidance on when to use this tool versus alternatives. It does not name any sibling or state conditions like 'use this for equity quotes, not for crypto perps'. The implication is that it is for stock/ETF quotes, but there is no direct statement of when to select it over, for example, get_market_brief or get_top_traders. The agent must infer the use case from the content alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the behavioral burden. It discloses useful traits: results are limited to upcoming events, sorted nearest-first, and broken down per public vesting schedule. This goes meaningfully beyond the tool name itself, though it does not mention response shape or whether any data source caveats apply.

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?

A single, front-loaded sentence conveys scope, ordering, and output fields with no filler. Every part of the sentence earns its place.

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?

For a zero-parameter read-only listing tool, the description is complete: it states what data is returned, for which universe, and in what order. There is no output schema, but the description adequately covers return semantics.

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 tool has zero parameters, so the schema is effectively complete and there is little for the description to add. The description still clarifies what the returned data represents, which is the relevant semantic context for a parameterless endpoint.

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 names a specific resource ('token unlock events for pool coins'), states the temporal scope ('upcoming'), and specifies output contents ('next unlock timestamp and token amount per public vesting schedule'). It is clearly distinguishable from the many sibling get_* tools, even without opening schemas.

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 context is clear: use this for upcoming token unlock data for pool coins. It does not explicitly name alternatives or give when-not-to-use conditions, but no sibling appears to cover token unlocks, so the omission is not significant.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden. It states 'public data' implying a read-only operation, and lists returned fields, but it does not disclose potential limitations such as pagination, count limits, or ordering. It is adequate but not rich.

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 a single sentence that front-loads the resource ('Top lead traders') and enumerates the returned data without waste. Every element contributes to understanding what the tool provides.

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?

For a simple no-parameter tool with no output schema, the description covers the content well: it lists all major fields and specifies the data source. It lacks detail on list structure or count, but that is a minor gap given the tool's simplicity.

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?

There are zero parameters and an empty schema, so schema coverage is trivially 100%. The description adds no parameter-specific meaning because none exist. Per rubric, a zero-parameter tool gets a baseline of 4, which is appropriate here.

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 states the tool returns 'top lead traders' from OKX copy trading, listing specific metrics (total PnL, win rate, AUM, copiers, live open positions with details, and closed trades). This is a distinct resource not covered by any sibling tool, so it distinguishes itself effectively.

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 implies usage when one needs trader performance data, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. Siblings are all market metrics (funding, liquidations, etc.), so context is clear but no explicit routing is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.