PredMCP

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

The description adds value beyond annotations: it discloses that the tool is free, requires an email, returns a key and config, and includes rate limits (100 calls/day, one key per IP). Since readOnlyHint=false, the mutation is implicit, and the description confirms it. No contradictions.

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 extremely concise: three short sentences front-loading the purpose, then requirement and output, then limitations. Every sentence earns its place with zero redundancy.

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 tool with no output schema, the description covers the key return value (key and config) and constraints (rate limits). It lacks details on error handling or behavior for duplicate emails, but overall it is sufficiently complete 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?

Schema coverage is 100% for the single parameter (email), with a detailed description already in the schema. The tool description only mentions that an email is required, adding no new semantic information beyond what the schema provides. Baseline score of 3 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 the tool's purpose: 'Generate a free PredMCP API key.' It specifies the action (generate), resource (API key), and context (free PredMCP). This distinguishes it from all sibling tools, which are read-only (get_/search_ prefixes).

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 when to use (when you need an API key) and provides limits (free tier, 100 calls/day, one key per IP). However, it does not explicitly state when not to use, such as if the user already has a key or if the email is already used.

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?

Annotations indicate readOnlyHint and openWorldHint. Description adds value by specifying the data source (free Yahoo Finance) and output structure (raw values + 1d change). No contradictions 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.

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

Single sentence listing assets and output. Efficient, but the 'Pro' addition is slightly distracting. Otherwise 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 should detail return format. It mentions 'raw values + 1d change' but omits data type, structure, or limitations. Adequate but not fully 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?

With 0 parameters and 100% schema coverage, the description need not explain params. It correctly focuses on output, though it could clarify the 'Pro' reference is extraneous.

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 basic macro indicators (DXY, US10Y yield, S&P 500, gold, VIX) from free Yahoo Finance, distinguishing it from sibling tools like get_macro_context or get_macro_liquidity. However, the mention of a 'Pro' version introduces minor ambiguity.

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 on when to use this tool versus alternatives. Does not specify context, prerequisites, or when not to use it. Input schema is empty, so no parameter-related guidance.

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?

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds important behavioral context: 'each costs an orderbook call' from the param default description, revealing computational cost. It also mentions snapshots, hinting at data recency. No contradictions 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.

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

Two sentences: first defines the core output and methodology, second contrasts with sibling. No unnecessary words, efficiently front-loaded with key 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?

Despite no output schema, the description lists key outputs: annualized net carry, break-even holding period, and 7d stability score. It does not detail return structure or field names, but for a computed metric tool, this is sufficient. The open world hint and parameter cost information round out completeness.

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 100% with clear descriptions. The description adds context linking 'size_usdc' to 'at your size' and the computational cost of 'top_n' (each costs an orderbook call). This enhances understanding beyond the schema alone.

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 calculates funding carry NET of costs, including annualized funding minus spread and slippage, break-even holding period, and 7d stability score. It also explicitly contrasts with sibling get_top_funding_rates which shows gross, making the purpose 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 explicitly contrasts with get_top_funding_rates, stating 'nobody trades gross', which implies this tool should be used for net carry calculations relevant to trading. It does not provide explicit when-not-to-use scenarios, but the sibling comparison effectively guides usage.

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?

Annotations already indicate readOnlyHint and openWorldHint. Description adds behavioral traits: data source (Etherscan free tier) and interpretation (outflow bullish, inflow distribution pressure). No contradictions.

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, front-loaded with main action. Every sentence adds value: tool purpose and interpretive guidance. 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?

Parameters well-covered, annotations present, but no output schema description. For a data-retrieval tool, knowing the return format (e.g., single number, time series) would improve completeness.

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 covers both parameters with 100% coverage. Description does not add details beyond the schema, so baseline score applies.

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 'Net ETH outflows across known CEX hot wallets' over a window, with specific exchanges listed. This distinguishes it from siblings by focusing on a unique on-chain metric.

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?

Provides context on bullish/bearish interpretation and mentions 'Etherscan free tier' hinting at limitations. However, no explicit comparison to sibling tools like get_whale_flow or when to use this vs others.

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?

Annotations already provide readOnlyHint and openWorldHint. The description adds that the tool returns a clean number for LLM decisions but does not discuss data freshness, update frequency, or whether score is real-time. Adds some context beyond annotations.

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 concise sentences that front-load purpose and value, with no extraneous information. 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?

Given the tool's complexity and existence of many sibling indicators, the description positions it well as a composite. No output schema, but output is adequately described. Could add constraints like supported assets or data freshness, but overall sufficient for careful use.

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 100%, so baseline 3. Description mentions the aggregated components (funding outlier, whale imbalance, etc.) but does not elaborate on parameter details beyond what the schema already provides for asset, whale_window_minutes, and min_whale_notional_usdc.

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?

Description clearly states it aggregates four factors into a directional score (-100 to +100) and strength score (0-100), and explicitly says it replaces 6+ lookups, distinguishing it from other 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?

Implies use as a higher-level decision tool over individual lookups ('one call replaces 6+ lookups'), but does not explicitly state when not to use or name specific alternatives among the many sibling indicator 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?

Annotations declare readOnlyHint and openWorldHint true. The description adds that data comes from HL's predictedFundings feed, results are sorted by annualized spread, and outlines the delta-neutral carry strategy. 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.

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

The description is two sentences plus a final clause, all front-loaded with the core purpose. Every sentence adds value, with no wasted words. Ideal length for quick comprehension.

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 two simple parameters, readOnlyHint, and no output schema, the description adequately explains the data source, sorting, and strategy. It does not describe return format but that is mitigated by the schema and clarity of purpose.

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 100%, so the schema already describes each parameter. The description adds context about sorting and the meaning of min_spread_annual_pct but does not provide substantial new semantics beyond what the schema offers.

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 predicted funding spreads between Hyperliquid, Binance, and Bybit per asset. It uses specific verbs like 'get' and 'surfaces', and the context distinguishes it from siblings like get_funding_rates which likely yield per-venue data.

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 for delta-neutral carry trades but does not explicitly state when to use this tool versus alternatives like get_funding_rates or get_carry_scanner. No 'when not to use' guidance is provided, which leaves some ambiguity.

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?

Annotations declare readOnlyHint and openWorldHint. Description adds behavioral context: it compares timeframes and flags anomalies (spikes, regime shifts, sign contradictions). 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.

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

Two sentences, no fluff. First sentence defines the core function, second adds value. 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?

Adequate for a simple parameter tool with rich annotations, but lacks description of output format or return values. For an analysis tool, this is a minor 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?

Only one parameter (asset) with 100% schema description coverage. The tool description adds no additional parameter meaning beyond 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?

Purpose is clear: term-structure analysis of Hyperliquid funding comparing current vs averages, flagging anomalies. The description distinguishes it from raw funding rate, but does not explicitly differentiate from siblings like get_funding_outliers or get_funding_rates.

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 when-to-use or when-not-to-use guidance. The phrase 'More nuanced than raw funding rate' hints at usage but does not name alternatives or state prerequisites.

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?

The description adds a behavioral insight (spike vs baseline is stronger signal) beyond the annotations (readOnlyHint, openWorldHint). However, it does not disclose other traits like data freshness or pagination. Annotations already cover read-only safety.

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, front-loaded with the core function, followed by a valuable insight. 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?

The description is adequate for a simple filtering tool but does not specify the return format or fields. Since there is no output schema, more detail on what is returned would be helpful.

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?

Both parameters are fully described in the input schema with clear descriptions and defaults. The description adds no further meaning, so baseline score of 3 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 identifies the resource (Hyperliquid perps with outlier funding rates) and the action (get those outliers). It also adds insight that a spike vs baseline is a stronger signal than raw rate. This distinguishes it from similar tools like get_funding_rates.

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 that raw funding rate is less useful but provides no explicit guidance on when to use this tool vs alternatives. No when-not or alternative tools are 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds context about the sign interpretation (positive = bearish, negative = bullish) but does not disclose additional behavioral traits such as data freshness, rate limits, or pagination. The description adds some value but is not extensive beyond the annotations.

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 consists of two concise sentences that clearly state the purpose and provide an interpretation of the data. There is no fluff or repetition; 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?

Given the tool's simplicity (one optional parameter, read-only, open world), the description is adequately complete. It explains the core purpose and the meaning of positive/negative rates. No output schema exists, so return value details are not required. The description could mention that it returns funding rates for all assets by default, but it's implied. Overall, sufficient for an agent.

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 100%; the 'coins' parameter is fully described in the schema (list of asset tickers, omit for all). The description does not add any additional meaning or syntax details beyond what the schema already provides, so baseline 3 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 the tool retrieves current funding rates for Hyperliquid perpetuals, which is a specific verb+resource. It also explains the meaning of positive/negative rates, adding value. However, it does not explicitly differentiate from similar tools like get_top_funding_rates among many siblings, so it falls 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?

The description implies usage for getting current funding rates but does not provide explicit guidance on when to use this tool versus alternatives (e.g., get_top_funding_rates, get_funding_outliers). No when-not-to-use or prerequisite information is 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?

Annotations already indicate readOnlyHint=true and openWorldHint=true, establishing safety. The description adds behavioral context by explaining that it flags spreads above a threshold and that a spread indicates mispricing. No contradictions.

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, no redundancy. The first sentence states the purpose and action, the second explains the concept of spread. Every word is necessary and 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 simplicity (one optional parameter, no output schema, annotations present), the description covers the tool's functionality, the input's role, and the interpretation of results. It is fully adequate for an agent to select and use the tool correctly.

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 100% with a detailed description for the single parameter (min_spread_pct) including default, range, and meaning. The tool description does not add further parameter semantics beyond what the schema provides, so baseline 3 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 action ('finds the same underlying market priced on both HIP-4 and Polymarket') with a defined outcome ('flagging spreads above threshold'). It is distinguishable from sibling tools like 'get_pm_hl_divergences' by focusing on HIP-4 vs Polymarket specifically.

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 explains the context of use (finding mispricing between two venues) and defines what a spread means. It does not explicitly state when not to use or list alternatives, but the purpose is sufficiently clear for selection among siblings.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows the tool is safe and results may change. The description adds context by narrowing the scope to sports markets with high certainty and imminent closure, which is useful beyond the annotations.

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 consists of two concise sentences that front-load the purpose and add a single clarifying sentence. Every word earns its place with no redundancy or fluff.

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 filtered list tool with two optional parameters and no output schema, the description adequately explains what the tool returns and the criteria. No additional information is needed for correct use.

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 100% with descriptive parameter names and descriptions. The tool description reinforces the concept of 'a few hours' and 'high certainty' but does not add new information beyond what the schema provides. Baseline score of 3 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 the tool retrieves sports prediction markets on Polymarket that are closing within a few hours and have a high-certainty leading outcome. This specific verb-resource-scope combination distinguishes it from siblings like 'get_markets_near_resolution' or 'get_odds', which target different aspects.

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 for late-game positioning with near-certain resolution markets. It provides context but lacks explicit when-not-to-use or alternatives. Sibling tools such as 'get_markets_near_resolution' or 'get_odds' are present, so a clearer exclusion would improve this dimension.

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?

Annotations already declare readOnlyHint and openWorldHint. The description adds that the tool computes estimates from mark price and standard leverage multiples, and mentions liquidity significance. It does not disclose additional traits like accuracy, rate limits, or response format beyond the estimation nature.

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, front-loaded with the core purpose, and every sentence adds value. No unnecessary 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?

The description explains what the tool computes and hints at interpretation through liquidity context. However, without an output schema, it would benefit from specifying the return format (e.g., list of price levels). Still, for a simple tool with one clear parameter, it is reasonably 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 input schema covers parameter 'coin' with description and examples. The tool description adds no additional meaning beyond the schema, which is sufficient for the single parameter. Baseline 3 applies.

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 estimates liquidation price levels for a given Hyperliquid perp, using mark price and leverage multiples. It specifies the resource and output, distinguishing it from sibling tools like get_orderbook or get_funding_rates.

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 for analyzing liquidation clusters but does not provide explicit guidance on when to use vs. alternatives or when not to use. No context about prerequisites or exclusions.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value beyond annotations by specifying the exact data sources (Yahoo Finance, CoinGecko), the list of assets, and the regime classification. It does not contradict annotations.

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, front-loaded with 'Live macro snapshot', no wasted words. Every sentence adds value: the first enumerates data, the second adds the regime classification.

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 could mention how the regime is represented or if the data comes as separate fields. However, for a simple snapshot tool, the current description is nearly complete for an agent to understand the tool's purpose and output. It lacks only a hint about return format.

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% (none needed). The description enriches the tool by listing the constituent data points and regime output, effectively acting as its own parameter documentation.

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 specifies: 'Live macro snapshot: DXY, US10Y yield, S&P 500, gold, VIX (Yahoo Finance free) + BTC dominance, ETH/BTC, total crypto market cap (CoinGecko free). Plus a coarse RISK_ON / RISK_OFF / MIXED regime.' This is a specific verb+resource combination that distinguishes it from siblings like get_basic_macro and get_market_context.

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 for macro context but provides no explicit guidance on when to use this tool vs alternatives (e.g., get_basic_macro, get_market_context). It is self-contained but lacks differentiation instructions.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds no additional behavioral disclosures (e.g., rate limits, data freshness). It does provide context on data sources and interpretation, but does not go beyond what annotations already convey regarding safety or side effects.

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 wasted words. It front-loads the core purpose and adds valuable interpretation. 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 zero-parameter tool with no output schema, the description provides sufficient context: what data is included, how to interpret the result, and the bullish implication. It could mention data freshness or update frequency, but omission is minor 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?

The input schema has zero parameters and 100% schema coverage, so no parameter documentation is needed. The description does not reference any missing parameters. Baseline score of 4 is appropriate given no parameters require explanation.

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's purpose: a 'fiat-to-crypto liquidity gauge' with specific data sources (BTC+ETH ETF flows, USDT+USDC mint/burn) and an interpretation guideline (net inflow = bullish). This clearly distinguishes it from sibling tools like get_basic_macro or get_macro_context, which address different macroeconomic aspects.

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 for assessing liquidity direction (bullish/bearish) but provides no explicit guidance on when to use this over alternatives (e.g., get_basic_macro) or when not to use it. It lacks exclusion criteria or comparative context with siblings.

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?

Annotations indicate readOnlyHint and openWorldHint. Description adds behavioral context by specifying the data sources combined (prediction markets and perp data) and that it is a snapshot. No contradictions.

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, front-loaded with purpose. No extraneous information. Efficiently communicates 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?

With one parameter, no output schema, and rich sibling tools, the description sufficiently explains the tool's function and when to use it. Covers the needed completeness for an agent.

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?

Only one parameter (query) with schema description already providing examples. Tool description reinforces that query should be a broad topic, but does not add significant new meaning beyond 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?

Clearly states it provides a unified snapshot combining prediction markets (Polymarket, HIP-4) and Hyperliquid perp data. Distinguishes from siblings by being a one-call replacement for multiple lookups.

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?

Implies use when broad market context is needed, and mentions it replaces 3+ separate lookups. However, does not explicitly state when not to use or list specific alternative tools for narrower queries.

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?

Annotations already indicate read-only and open-world behavior. The description adds value by detailing the composite factors (BTC trend, realized vol, etc.) and the 'one-call' nature, enhancing transparency beyond the annotations.

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 efficient sentences front-load the output labels and key use-case. Every sentence adds unique value without redundancy. Highly concise and 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?

For a zero-parameter tool with clear output labels and no output schema, the description fully covers the necessary context. It explains what the tool does, what it outputs, and when to use it, leaving no gaps.

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?

No parameters exist (0 params, 100% schema coverage), so the description has no burden to explain them. Baseline of 4 applies as the description correctly implies no additional input is 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 clearly states the tool's function as a market regime classifier, lists the specific output labels (RISK_ON_TRENDING, etc.), and differentiates from sibling tools by being a unique composite indicator. The verb 'get' and resource 'market regime' are explicit.

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?

Explicitly instructs to call this FIRST each session to condition strategy choice, providing clear usage context. Lacks explicit 'when not to use' or alternatives, but the directive is strong and sufficient for an agent.

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?

Annotations provide readOnlyHint and openWorldHint. Description adds that it returns specific fields and is sorted by volume. Does not contradict annotations and provides useful behavioral context beyond structured data.

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?

Single sentence with all essential information: source, sorting, return fields. No wasted words; highly efficient.

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?

Though no output schema, description explains exactly what is returned. Tool has low complexity (3 optional params) and the description covers purpose, sources, and output fields adequately.

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 100% and each parameter is well-described in the schema. The tool description does not add new meaning to parameters beyond mentioning return fields, which is already implied by 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?

Describes specific verb 'get', resource 'markets', sources (Polymarket/HIP-4), sorting by volume, and return fields (title, prices, volume, expiry). Clearly distinguishes from siblings like get_markets_near_resolution and search_markets.

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 on when to use this tool vs alternatives like search_markets. The description implies it is for a broad list sorted by volume, but lacks explicit when/why.

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?

Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral context (returns markets resolving soon with leading probability) but does not describe additional traits like pagination or rate limits. 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.

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

Two sentences: first explains what the tool does, second explains its utility. No filler words, efficiently conveys purpose and usage context. Front-loads the key action ('Polymarket markets resolving...').

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 read-only tool with two parameters and no output schema, the description adequately covers purpose and usage. It could hint at output fields, but the context is sufficient for an agent given sibling tools like 'get_markets' likely share a similar output structure.

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 100%, so baseline is 3. The description adds meaning by linking parameters to the use case ('resolving within next N hours' for hours, 'leading probability' for min_prob), providing semantic context beyond 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 the tool retrieves Polymarket markets resolving within a time window with a probability filter. It distinguishes itself from siblings like 'get_markets' and 'get_late_game_sports' by focusing on near-resolution arbitrage.

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 mentions use cases: 'resolution arbitrage and last-minute positioning.' This provides context for when to use it, though it does not explicitly state when not to use it or name 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?

Annotations already indicate readOnly and openWorld hints. The description adds context about the ranking criteria (24h volume spike, price swing) and sources, which is useful beyond the annotations. However, it does not detail potential side effects or data freshness.

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 clearly describe the tool's function and output. No filler or redundant content. Front-loaded with the key purpose.

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, the description gives a good sense of the return format (list of markets with rankings and metrics) and sources. It is adequately complete given the tool's simplicity (one optional parameter). Minor improvement could mention if results include price or volume data.

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 covers the single parameter 'limit' with full description, default, and bounds. The description adds no new semantics beyond stating it returns top movers, so a baseline score of 3 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 the tool returns top prediction markets ranked by 24h volume spike or YES/NO price swing, and surfaces breaking news bets and momentum plays. It specifies the sources (Polymarket and HIP-4), distinguishing it from sibling tools like get_volume_spikes or get_odds.

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 for momentum plays and breaking news, but it does not explicitly state when to use this tool versus alternatives like get_volume_spikes or search_markets. No when-not-to-use guidance is provided.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds actionable context: sources (CoinDesk, The Block, etc.), time pairing (1h price move), and bias filtering capability. No contradiction.

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 redundancy. First sentence covers core function, second adds filtering capability. Efficiently sized and 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?

No output schema exists, but the description outlines the return structure (headlines paired with price moves). It could be more explicit about the exact fields (e.g., headline text, price change percentage, sentiment bias), but it provides a reasonable mental model for the agent.

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 100%, so parameters are already well-documented. The description mentions 'filter news bias' but does not clarify which parameter enables this, which could be misleading. It adds minimal value beyond 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 it fetches recent crypto news headlines mentioning an asset paired with the 1h price move. It also mentions filtering news bias, distinguishing it from siblings like get_recent_news which likely only provides headlines without correlation.

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 news with price impact is needed, but does not explicitly state when not to use or contrast with alternatives. The sibling get_recent_news provides a natural distinction but is not 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?

The description adds value beyond annotations (which already declare readonly and open-world) by specifying the output: 'prices and implied probability'. However, it does not detail any other behavioral traits such as rate limits or authentication requirements, so it is adequate but not exceptional.

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 conveys all necessary information without extraneous words. Every word is substantive.

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, the description fully covers what it does and what it returns. No output schema exists, but the description clearly states the expected output (prices and implied probability), making it complete for its purpose.

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 100%, so the schema already fully documents the two parameters. The description does not add additional meaning beyond what the schema provides, earning the baseline score.

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 ('get') and resource ('YES/NO prices and implied probability') and clearly identifies the target platforms ('Polymarket or HIP-4 market token'). This distinguishes it from sibling tools like get_price_summary or get_market_context, which serve different purposes.

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 indicates the tool is used for fetching odds for Polymarket or HIP-4 market tokens, providing clear context. However, it does not explicitly exclude other scenarios or suggest alternative tools, so it stops 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the tool is safely read-only with variable output. The description adds that it uses 'continuous snapshots' and relies on OI history unique to predmcp, but does not detail how often snapshots update or how regimes are determined. Behavior is adequately disclosed but not richly detailed.

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 concise sentences: first describes purpose and output categories, second emphasizes data uniqueness. Every word contributes value; no fluff.

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 lists the four possible regime outputs and explains usage modes. It could specify the return format (e.g., per coin object or list), but for a classification tool, this is sufficiently complete. Minor gap in data structure details.

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 100%: both 'coin' and 'hours' have descriptions. The description reinforces that 'coin' can be omitted for scanning all coins, but adds no additional meaning beyond the schema. Baseline 3 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?

Description clearly states the tool provides a price-vs-OI regime classification per coin, listing the four output categories (NEW_LONGS, SHORT_SQUEEZE, NEW_SHORTS, LONG_LIQUIDATION) and two usage modes (scan all or one coin). It also distinguishes itself by noting the unique OI history data source, differentiating it from 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?

Description indicates when to use: for OI divergence regime analysis, with options to scan all tracked coins or analyze a specific one. It implicitly suggests this tool is for specialized OI-based regime detection, but does not explicitly state when not to use or compare to alternative tools like 'get_oi_history' or 'get_signals'.

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?

Adds behavioral context beyond annotations: data comes from a continuous 5-min collector, includes price and funding per point, and is exclusive to predmcp. No contradictions with readOnlyHint and openWorldHint.

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 sentences, front-loaded with core purpose, no wasted words. Each sentence adds value: function, uniqueness, and related tool.

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?

Covers most needs for a simple time series tool: mentions data source, granularity, included fields. Lacks explicit mention of response format or limits, but given simplicity and annotations, it's 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 100%, but description adds meaning: coin parameter constrained to top ~30 coins by OI, and hours parameter implies lookback with 5-min granularity from continuous collector, which is not in 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?

Description clearly states it provides open-interest time series for a coin over the last 24h with 5-min granularity. It distinguishes from siblings by noting the data is unique to predmcp and mentions a related sibling tool get_oi_divergence.

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?

Implies when to use by stating 'Hyperliquid has NO OI-history endpoint — this data exists only on predmcp,' giving context for its exclusivity. Mentions a pro tool for classification but lacks explicit when-not-to-use instructions.

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?

Annotations already declare readOnlyHint and openWorldHint. The description adds the key behavioral fact that 'new long positions cannot be opened' on these perps, which is beyond what the annotations convey. 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.

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

Two sentences, front-loaded with purpose and immediate usage advice. No unnecessary words or repetition. Every sentence 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?

No output schema exists, but the description implies the return type (list of perps). It could be more explicit about the format (e.g., symbol names or IDs), but for a simple blacklist tool, the context is mostly sufficient.

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 covers them fully (100% coverage). The description adds meaning by explaining what the output represents (list of perps at OI cap), which aligns with the baseline of 4 for no-param tools.

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 the specific verb 'Lists' and identifies the resource as 'Hyperliquid perps that are currently at the open interest cap,' clearly distinguishing it from sibling tools like get_funding_rates or get_liquidation_clusters.

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 explicit guidance to 'Use as a blacklist to avoid getting rejected on entry,' which tells the agent when to invoke this tool. It does not explicitly state when not to use it, but the narrow purpose makes that less critical.

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?

Annotations already declare readOnlyHint and openWorldHint. Description adds context about the data source (free Deribit feed) and metrics, but does not elaborate on limitations or behavior beyond what annotations imply.

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 concise sentences: first states what the tool does, second explains how to interpret results. No wasted words, front-loaded with key 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 simple snapshot tool with one parameter and no output schema, the description fully covers purpose, data points, and usage context. No gaps given the tool's complexity.

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 covers 100% of the single parameter with enum values and description. The tool description adds minimal extra context (mention of 'free public feed' and Deribit support), so baseline 3 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?

Description clearly states it provides a snapshot of BTC or ETH options data with specific metrics (ATM IV, skew, term structure, OI, volume). It distinguishes from sibling tools like get_simple_iv by specifying the data source and richness.

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?

Directly explains that the tool is useful as a sentiment gauge, interpreting high IV and skew. While it doesn't explicitly list when not to use, the purpose is clear and distinct from siblings.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds context that it shows liquidity at each price level, which is useful but not critical beyond annotations.

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, no unnecessary words. Essential information only, front-loaded with purpose.

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 clarifies it returns bids + asks and liquidity per price level, which is sufficient for a simple tool. Could mention return structure but not necessary.

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 100% for the single parameter 'token_id', and the schema description already explains it is a Polymarket token ID. The description adds no new parameter meaning beyond what the schema provides.

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?

Clearly states the tool retrieves the full orderbook depth (bids + asks) for a specific Polymarket market token. Verb 'Get' and resource 'orderbook' are specific, distinguishing it from siblings like 'get_orderbook_depth'.

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?

Implied usage: to get orderbook depth for a market token. No explicit when-not-to-use or alternative tools, though sibling 'get_orderbook_depth' exists. Minimal guidance.

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?

Discloses return data beyond annotations: 'top of book, spread, cumulative depth at $100/$500/$1k/$5k tiers, and estimated slippage for a given order size'. Annotations already indicate read-only and open world, no contradiction.

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, no wasted words, front-loaded with purpose. Efficient and clear.

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?

Despite no output schema, description adequately describes return values (top of book, spread, depth tiers, slippage). Also covers use case and parameter linkage, sufficient for agent to select and invoke.

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 100%, so baseline 3. Description mentions 'given order size' linking to size_usdc parameter, but adds little beyond schema documentation.

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?

Description clearly states 'Full orderbook depth + slippage estimate for any Hyperliquid perp or HIP-4 market', using specific verb and resource. Distinguishes from sibling 'get_orderbook' by adding depth tiers and slippage estimation.

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?

Explicitly says 'Critical for HIP-4 farming and low-liquidity assets where market orders get destroyed by slippage', indicating when to use and providing context for avoiding it in other scenarios.

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?

Annotations already indicate the tool is read-only and open-world. The description adds behavioral context by explaining the divergence computation, which is consistent with the annotations. No contradictions, but no additional safety or side-effect details beyond what annotations provide.

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, highly concise, and immediately conveys the core purpose. Every word adds value, with no redundancy.

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?

Despite the lack of an output schema, the description gives a clear idea of the output (a list of divergences). It could elaborate on the calculation methodology or output format, but for a simple list tool, it is largely 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 100% with clear parameter descriptions (limit, min_pct). The description does not add new meaning to the parameters beyond what the schema provides, so it meets the baseline for high schema coverage.

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's purpose: finding markets where Polymarket implied probability diverges from Hyperliquid funding direction, with a concrete example. The title and name are also descriptive, making it easily distinguishable from sibling 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 the tool is useful for detecting divergence signals that are hard to compute manually, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among the many 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?

Annotations indicate readOnlyHint=true, and the description adds behavioral context by specifying the data source and time window (14d of 1h Hyperliquid candles). This goes beyond the basic annotation to inform the agent about computational scope and data freshness.

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 efficiently cover the tool's purpose, outputs, and data source with no extraneous information. Every phrase 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?

For a read-only tool with rich annotations and a well-described schema, the description fully explains what the tool computes and the data it uses. No output schema is needed as the listed outputs sufficiently inform the agent of the return structure.

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 100% with detailed parameter descriptions, so the description adds minimal extra meaning about parameters. It contextualizes that the positions are used for risk computation, but does not elaborate on constraints or formatting beyond 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 the tool computes risk analytics for a list of positions and enumerates specific outputs (beta, sector concentration, correlation matrix, volatility, VaR). This distinguishes it from sibling tools like get_market_context or get_macro_context which focus on broader market conditions.

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 a user has a portfolio of positions and needs risk metrics, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among 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?

The description discloses computation-only behavior, consistent with readOnlyHint=true. It warns about liquidation inside stop and mentions caps by orderbook liquidity, adding behavioral context beyond annotations.

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 efficient sentences: first lists outputs, second warns about liquidation, third advises on parameter grounding. No redundant words, front-loaded with key action.

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 8 parameters and no output schema, the description adequately outlines the major outputs and purpose. It could include return format or more details on liquidity capping, but it's sufficient for an agent to understand tool scope.

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 100%, and the description adds usage context (e.g., grounding win_rate, capping by liquidity). While individual parameter details are in schema, the description gives semantic meaning to the overall computation and parameter interplay.

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's purpose: turning a signal and bankroll into a concrete position, listing specific outputs like fractional-Kelly size, stop, liquidation price. It distinguishes from other get_* tools which focus on data retrieval, not position sizing.

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 advises grounding win_rate_pct with get_signal_performance or get_signal_backtest first, providing explicit prerequisite guidance. However, it does not explicitly state when not to use or give alternatives beyond that.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context that calculations use 30 days of HL hourly candles and lists all computed metrics, providing clarity on data source and scope.

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, front-loading the key metrics and adding data source context. Every sentence adds value without redundancy.

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, the description thoroughly lists all returned metrics and explains the data computation source. It provides complete context for an agent to understand what the tool returns and how it works.

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 100% with one parameter 'asset' fully described. The description does not add further semantic meaning beyond what the schema provides, earning the baseline score.

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 provides a snapshot of price summary metrics for an asset, listing specific statistics like mark price, returns, high/low, drawdown, rally, and volatility. This distinguishes it from sibling tools like get_market_context or get_simple_iv.

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 as a quick one-call snapshot but does not explicitly guide when to use this tool over alternatives or when not to use it. No exclusions or alternative references are provided.

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?

Annotations already indicate readOnlyHint and openWorldHint. The description adds valuable context about the data sources (CoinDesk, The Block, Decrypt, Cointelegraph RSS feeds) and the Pro feature (1h price move). This enhances transparency beyond annotations.

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, front-loaded with core functionality, no extraneous information. Efficient and clear.

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?

While the description covers purpose and sources, it lacks details on the output format (e.g., that it returns headlines as strings, any metadata). With no output schema, the description could be more complete about what the agent receives.

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 100%, so the description adds modest value. It mentions asset ticker format and sources, but does not add new details for limit or hours_back beyond the schema defaults and constraints.

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 recent crypto news headlines matching an asset ticker from four specific sources. It distinguishes itself from sibling news tools like get_news_correlation by focusing on headlines and asset ticker matching.

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 for getting news headlines for a specific asset, but does not provide explicit guidance on when to use this tool versus alternatives like get_news_correlation, nor does it specify when not to use it.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds cursor mechanism, time window (last hour), and specific event filters, but doesn't detail pagination direction or empty response behavior.

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 concise sentences with no fluff. Front-loaded with most important information (event types and time range).

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 3 parameters and no output schema, the description is complete: it covers purpose, usage pattern, alternative tool, and pagination. No gaps.

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 100% with basic descriptions. Description adds value by explaining since_id cursor usage and free tier limit cap, going beyond 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?

Description explicitly states verb 'get' and resource 'recent signals', enumerates specific event types (funding outliers, whale trades, OI caps), and distinguishes from sibling tool get_signal_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?

Provides clear cursor-based pagination instructions, mentions polling equivalent to /sse/signals stream, and explicitly contrasts with get_signal_history which covers 7 days with outcomes.

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?

Annotations already declare readonly=true and open-world=true. Beyond that, the description adds the scoring range (0-100, A-F), lists the factors considered (spread, slippage, orderbook depth, volatility, etc.), and clarifies the semantics of entry quality. It does not contradict annotations.

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, information-dense sentence. It front-loads the core purpose (score type and range) and includes important details (factors, differentiation) without any wasted words. Excellent conciseness.

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 three parameters and no output schema, the description adequately covers the tool's function and inputs. It mentions the output range and grade, and lists the input factors. It could be improved by specifying the exact return format (e.g., object with score and grade), but it is largely complete for an AI agent.

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 100%, so baseline is 3. The description adds marginal value by linking the 'size_usdc' parameter to 'slippage at desired size' and implying that asset and direction are used to compute the score. However, it does not add substantial semantics beyond what the schema already provides.

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 produces an execution-quality score (0-100, A-F grade) for entering a position, listing specific factors (spread, slippage, depth, etc.). It explicitly distinguishes itself from conviction/direction scoring, referencing a sibling tool (get_conviction_score) and clarifying the focus on entry conditions.

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 to use this tool: when evaluating entry quality rather than directionality. It explicitly differentiates from 'conviction' (direction scoring), which is a likely alternative. While it could mention more alternatives or when not to use it, this is sufficient for a sibling-dense environment.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating this is a safe read operation. The description adds that it computes forward returns, win rate, and Sharpe, but does not disclose any additional behavioral traits beyond what annotations provide. It does not contradict annotations.

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 functional sentences plus a marketing sentence ('Killer feature...'). The first sentence is dense and effective, but the second sentence is somewhat redundant ('Lets an agent reason about EV before trading'), and the third is promotional and not strictly necessary. It could be trimmed without losing clarity.

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 tool has 6 parameters (2 required) with full schema coverage, and the description explicitly mentions the output (forward returns, win rate, Sharpe) despite lacking an output schema. Annotations cover safety. The description is mostly complete for an agent to understand what the tool does and what it returns.

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 100% description coverage for all 6 parameters, and the description does not add new semantic meaning beyond what the schema already provides. The schema already explains parameters like z_score and min_abs_rate with defaults and constraints. Thus, the description adds marginal value.

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: 'Find historical instances of a signal type on an asset over the last N days and compute forward returns (1h/4h/24h), win rate, and Sharpe.' This provides a specific verb ('Find historical instances and compute') and resource ('signal type on an asset'), distinguishing it from sibling tools like get_signal_history or get_signal_performance which do not compute forward returns or Sharpe.

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 says 'Lets an agent reason about EV before trading,' implying this tool should be used when evaluating the expected value of a signal before executing a trade. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools provide context for other signal-related operations.

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?

The description adds details beyond the readOnlyHint and openWorldHint annotations, such as cursor-based pagination, the 7-day lookback limit, and that forward returns are only included once mature. No contradictions 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.

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

The description is extremely concise at two sentences, front-loading the core functionality and including an illustrative question. Every word 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 5 parameters, no output schema, and good annotations, the description adequately conveys the tool's purpose, constraints (7-day window, specific signal types, return windows), and pagination mechanism. Could detail the output format more, but it's sufficient for an API 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 100%, so the schema already explains each parameter. The description adds no extra parameter-specific meaning, but it is consistent. Baseline of 3 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 specifies that the tool returns server-detected signal events over up to 7 days, including types like funding outliers, whale trades, and OI caps, with joined forward returns. It distinguishes itself from siblings like get_signals or get_recent_signals by focusing on historical analysis with returns.

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 includes an illustrative use case ('What happened last time funding spiked on HYPE — and did it matter?') but does not explicitly state when to use this tool over alternatives or provide exclusion criteria. The usage guidance is implied rather than explicit.

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?

Annotations already declare readOnlyHint and openWorldHint, so safety is clear. The description adds transparency about using actual production signals (not backtest) and includes sample size warnings, which enriches behavioral context beyond the annotations.

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, front-loaded with purpose and use case. Every sentence adds value with no redundancy or 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?

No output schema exists, but the description specifies what is returned (hit rates, average forward returns, sample sizes). Could be more explicit about the exact output structure, but it's adequate for an agent to infer the return type.

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 100% and the description does not add any parameter-specific details beyond what the schema already provides. Baseline 3 is appropriate since the schema is sufficient.

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 hit rates and average forward returns per signal type, measured on actual production signals, distinguishing it from backtest reconstructions. It also mentions the primary use case of grounding win_rate inputs for get_position_size.

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?

Explicitly recommends using this tool to ground win_rate inputs for get_position_size. Provides caution about sample sizes (n < 10 as anecdotal). Does not explicitly list alternatives or when not to use, but context 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?

Annotations already declare readOnlyHint and openWorldHint; description adds output structure and reasoning example but no further behavioral traits such as rate limits or authentication needs. Adequate given annotation coverage.

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 front-loaded purpose and immediate example. No wasted words; every sentence provides 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?

Describes output (signal type and reasoning) with example. Lacks exact return structure since no output schema exists, but the example compensates. Mostly complete given tool complexity.

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 covers the single parameter 'coin' with full description. The description does not add additional parameter details; baseline score of 3 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?

Clearly states the tool detects divergence signals between Hyperliquid perpetual funding/OI sentiment and HIP-4 on-chain prediction market odds, with specific output examples. This distinguishes it from sibling tools that focus on individual data sources or other signal types.

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?

Implied usage from description but no explicit guidance on when to use this tool versus alternatives like get_oi_divergence or get_pm_hl_divergences. No when-not criteria are 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?

Annotations already declare readOnlyHint and openWorldHint, so description is not required to repeat that. It adds value by specifying the source (Deribit public feed) and the free nature, which helps understand data freshness and 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?

Two sentences with clear information; no waste. Could be slightly more structured but is efficient.

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 (1 param, no output schema), the description adequately covers what data is returned. However, it lacks details on output format or pagination, though not critical for this use case.

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 has 100% coverage for the single parameter with enum and description. The description reinforces the asset choices but adds no extra semantics beyond affirming BTC and ETH.

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?

Description clearly states it provides a snapshot of BTC or ETH options from Deribit public feed, listing specific data points (ATM IV, OI, volume). Distinguishes from a Pro version with additional features. The verb-resource combination 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?

Context implies it's for quick, free snapshots, contrasting with Pro version for advanced needs. However, it doesn't explicitly state when to avoid this tool or name alternatives among siblings like `get_options_iv`.

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?

Annotations declare readOnlyHint=true, description reinforces safe read operation. Adds context about ranking by absolute funding rate and output fields, which is not in annotations.

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, no wasted words. Purpose and use case are 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?

No output schema, but description mentions included fields (OI and annualized yield). Lacks detailed output format, but acceptable for a list tool with simple parameters.

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 100% with descriptions for both parameters. Description adds no new parameter details beyond schema, but baseline 3 is appropriate since schema is self-explanatory.

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?

Specifies verb 'get', resource 'top funding rates', and includes data fields (OI and annualized yield). Distinct from siblings like 'get_funding_rates' and 'get_funding_outliers' by focusing on top rankings by absolute rate.

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?

Explicitly states use case: finding overcrowded longs/shorts and carry opportunities. Does not contrast with sibling tools, but the context of being 'top' implies a specific selection criterion.

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?

Annotations already declare readOnlyHint and openWorldHint (true). The description adds value by specifying data sources (Tally + curated DB) and listing the types of events covered. No contradictions 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.

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

Two sentences: first states purpose, second adds usage context and data sources. No filler, highly efficient, and 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 covers purpose, usage, data sources, and event types. Missing output format details, but with only 2 simple parameters and no output schema, the description is fairly complete. Could mention what the returned list looks like.

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 100%, so baseline is 3. The description does not add meaning beyond the schema: 'asset' and 'horizon_hours' are sufficiently documented in the schema, and the description only says 'within a horizon' without extra details.

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 lists upcoming market catalysts (token unlocks, governance votes, ETF/SEC deadlines, FOMC dates) for a given asset and horizon. This specific verb+resource combination distinguishes it from sibling tools like get_market_context or get_recent_news.

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: 'Helps agents avoid blind trades into known events.' This implies use before trading. However, it lacks explicit exclusions or when-not-to-use guidance, and no alternative tools are 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?

Annotations declare readOnlyHint=true and openWorldHint=true, indicating a safe read with changing results. The description adds that volume spikes typically precede news events, offering behavioral context beyond the annotations. No contradiction.

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 (two sentences) and front-loaded with the core function. Every sentence adds value: the first defines the tool, the second explains its significance.

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 lacks details about the return format (e.g., market IDs, volume figures). Given no output schema, this information would aid completeness. However, the context signals show 100% parameter coverage and simplicity, so a score of 3 is fair.

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 100%, with both parameters (limit, min_ratio) having clear descriptions. The description mentions 'abnormal 24h volume vs their 7-day daily average', which relates to min_ratio, but adds little extra meaning beyond the schema. Baseline 3 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 the tool returns Polymarket markets with abnormal 24h volume compared to their 7-day daily average. It uses a specific verb ('get') and resource ('markets with volume spikes'), and adds context about what volume spikes signify, distinguishing it from sibling tools like 'get_movers' or 'get_market_context'.

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 context that volume spikes typically precede news events or informed positioning, helping infer when to use the tool. However, it does not explicitly state when not to use it or mention alternatives, which would improve clarity.

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?

Annotations indicate read-only and open-world. The description adds behavioral details: cumulative, restart-proof, durable tape, persistence of ≥$25k trades. No contradictions; it enhances understanding of data source and reliability.

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 sentences, front-loaded with purpose. Every sentence adds value: what it does (imbalance), output description, and comparison to sibling. 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?

With no output schema, the description explicitly states return values (imbalance ratio, verdict, largest recent prints). All 3 parameters are well-documented in schema with defaults and min/max. The description covers data source, persistence, and use case completely.

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 100%, baseline 3. The description adds context by explaining 'durable trade tape (≥$25k trades)' and that min_notional_usdc sets a threshold for the sample trades list, clarifying the parameter's purpose beyond schema descriptions.

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 cumulative whale buy/sell imbalance over hours-to-days from durable trade tape, with specific outputs (ratio, verdict, largest prints). It distinguishes from sibling get_whale_trades by noting longer horizons.

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 compares to get_whale_trades ('Longer horizons than get_whale_trades (1h buffer)'), indicating when this tool is appropriate. It does not list when not to use or other alternatives, but the comparison is sufficient for a sibling-rich 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?

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds context about the label DB contents (CEX hot wallets, etc.), which is beyond the annotations. However, it does not disclose other behavioral traits like rate limits or data freshness.

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, each serving a clear purpose: first defines the action, second explains the utility. No redundant words or 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 simple lookup tool with one parameter, the description is reasonably complete. It explains the tool's purpose and the type of output (labels). However, it could benefit from mentioning how to interpret the label or whether there are multiple label categories, but it's mostly sufficient.

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 100% coverage for the single parameter 'address', with a complete description in the schema. The description does not add extra semantics beyond what the schema provides, so a baseline of 3 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 uses a specific verb ('Look up') and clearly states the resource ('curated label DB') and the types of labels ('CEX hot wallets, known market makers, suspected funds'). It also explains the purpose (distinguishing mechanical MM flow from alpha-generating activity), which distinguishes it from sibling tools that track flows or trades.

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 when to use the tool (when you need to classify an address), but it does not explicitly state when not to use it or provide alternatives. Siblings like get_whale_flow or get_whale_trades serve different purposes, but the description doesn't guide selection.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context about the API requirement (requires a user) but does not disclose any additional behavioral traits like auth needs, rate limits, or pagination behavior.

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, front-loaded with the core purpose, and every sentence adds value. No unnecessary words or repetition.

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 could hint at return values but does not. However, the tool has only 3 parameters with full schema documentation, and annotations cover safety and open-world nature, so the description is fairly complete for usage.

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 100%, so the schema already documents all parameters. The description briefly repeats the requirement for 'user' and mentions optional 'condition_id', but adds no new meaning beyond 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 it gets positions of a specific Polymarket wallet, optionally filtered to one market. It distinguishes from siblings by focusing on positions versus trades or labels, and explains the API limitation.

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 instructs to pass the wallet address of the trader to inspect, and notes the API limitation (no public all-holders endpoint). However, it does not specify when not to use this tool or mention alternative tools for broader queries.

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?

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds the data fields included (side, size, price, timestamp) and the threshold concept, but does not disclose potential behavioral traits like result ordering, pagination, or rate limits. This adds some value beyond annotations but remains limited.

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 concise sentences that directly state purpose and output fields. No extraneous information. Well-structured and 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 covers the tool's purpose and key output fields, which is adequate given the simple resource and lack of output schema. However, it omits details like result ordering (e.g., most recent first), default limits, or pagination, leaving minor gaps for an agent.

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 100% with clear parameter descriptions. The description provides overall context (Hyperliquid perps, threshold) but does not add significant meaning beyond the schema. It offers example values for 'coin' but no additional semantic detail for 'min_notional_usdc'.

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 recent large trades on Hyperliquid perps with a notional threshold. It specifies the resource and verb, and the mention of 'perps' and 'threshold' helps differentiate from sibling tools like get_whale_flow or get_whale_positions, though it does not explicitly compare.

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. It lacks context about scenarios, prerequisites, or exclusions, leaving the agent to infer usage from the 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?

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds behavioral context beyond annotations: 'full-text search', 'returns ranked results with current odds'. No contradictions. The description enhances understanding without repeating structured fields.

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?

Single sentence that is front-loaded with verb and resource. No wasted words. Efficient and clear.

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 search tool with no output schema, the description provides useful context about result shape (ranked with odds). Parameters are well-documented. Minor gap: no mention of pagination or result format beyond 'ranked results', but overall sufficient.

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?

Both parameters (query, limit) are fully documented in the input schema with descriptions (100% coverage). The description does not add extra semantic information beyond what the schema provides. Baseline 3 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?

Description clearly states 'full-text search across all Polymarket and HIP-4 prediction markets' and 'returns ranked results with current odds'. This distinguishes it from sibling tools like get_markets (which likely lists markets without search) and get_odds (which likely fetches odds for specific markets).

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?

Description implies usage for keyword-based market search but provides no explicit guidance on when to use this tool vs alternatives. No exclusions or alternative recommendations. The context is clear but incomplete.

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