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

It explicitly states 'Fully client-side — no API call is made,' which is critical behavioral info. However, it omits details on validation or error handling for invalid inputs.

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 three sentences plus an illustrative example, front-loaded with the core function, and each sentence adds essential information 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?

It adequately explains the tool's purpose and inputs, but lacks explicit description of the return value (position size in units) and does not address edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters with descriptions (100% coverage). The description adds value by explaining the formula and providing an example that ties parameters together.

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 position size based on capital, risk, entry, and stop-loss, and differentiates it from sibling tools like get_market_summary or simulate_order by its specific calculation role.

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 clarifies that the tool is client-side and provides an example, but does not explicitly state when to avoid using it or mention alternatives 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?

With no annotations, the description explains return types (floats, percentages). No mention of authentication or rate limits, but for a read operation it is sufficiently transparent.

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

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

Three sentences: function, data format, example. No superfluous text, 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?

Given no output schema, description explains return types and gives an example. Could be slightly more explicit about the structure of the 'side-by-side' data, but largely complete for a simple 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 already describes the parameter well (100% coverage). Description adds context by listing supported quote currencies and providing an example, adding 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?

Clearly states that it returns side-by-side ticker data for a base currency across supported quote currencies. Distinguishes from siblings like get_ticker or get_markets by focusing on cross-market comparison.

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 an example question that guides when to use. Does not explicitly exclude alternatives or mention when not to use, but the purpose is well-defined.

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

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

No annotations are provided, so the description carries the full burden. It explains the internal steps (fetch tickers, convert to USDC, compute discrepancies) and the threshold logic. It also notes the fee deduction requirement. However, it omits potential error conditions or rate limits.

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, well-structured, and front-loaded. It covers purpose, method, important notes, and an example in a compact form 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?

Given no output schema, the description hints at the return format (sorted by opportunity size) and provides enough context for an agent to understand the tool's operation. Minor gaps like exact output structure are acceptable.

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 the description adds valuable context: it explains how threshold_pct relates to profitability given fees, and gives examples for base_currency. This enhances understanding 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 detects cross-country price discrepancies for a given asset across specific markets, normalized to USDC. It uses specific verbs and resources, and the example helps clarify the purpose.

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 guidance on when to use the tool (to find arbitrage opportunities) and includes important context like fee structure and the need to deduct fees. It doesn't explicitly mention alternatives but the context is sufficient.

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 non-error empty array for unsupported currencies and 60-second caching, providing useful behavioral context beyond the schema.

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 efficiently cover purpose, edge case, caching, and an example 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?

Completeness is high: explains return type (array, potentially empty) and caching, sufficient for a simple read tool with no output schema.

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?

Adds context that currency parameter refers to fiat currency for deposit/withdrawal purposes, enriching the schema's code-only description.

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 returns banks for deposits/withdrawals of a fiat currency on Buda.com, differentiating it from sibling tools focused on markets and orders.

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?

Implicitly guides usage by specifying fiat currencies and noting crypto gives empty array, but does not explicitly contrast with other tools.

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

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

With no annotations, the description fully describes what the tool returns: base/quote currencies, fees as decimals, minimum order size, and fee tiers. This covers the behavioral aspect of the tool's output, though no mention of rate limits or auth, which is acceptable for a read-only list.

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 and an example question, with no wasted words. Front-loaded with the main action. 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?

Given no output schema, the description explains the return details completely. The single parameter is well-described. The tool's purpose is fully covered for an agent to use 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 clear description of market_id as optional. The description adds an example ('BTC-CLP') and context about omitting it, enhancing understanding 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 lists trading pairs or returns details for a specific market, using the verb 'lists' and specifying the resource. It distinguishes from siblings like get_market_summary by focusing on a list of markets and detailed fees.

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 explicit guidance to omit market_id for all markets or provide it for a specific market. Includes an example question to clarify usage. No explicit when-not or alternatives mentioned, but the instruction is clear.

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

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

With no annotations, the description fully discloses the three components and their weights, output details (score, label, breakdown), and the score range, offering complete transparency.

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 convey the formula, output, and an example, with 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?

Despite missing annotations and output schema, the description thoroughly covers input, computation, and output structure, making it fully actionable.

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 meaningful context (e.g., Buda.com market, example values) beyond the schema's minimal description.

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 a composite sentiment score with a detailed formula, distinguishing it from sibling tools like get_market_summary or get_ticker.

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 an example question ("Is the BTC-CLP market currently bullish or bearish?") that implies the tool's use case, though it does not explicitly contrast with alternatives.

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

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

With no annotations, the description fully explains the output: last price, bid/ask, spread %, volume, changes, and liquidity rating with threshold logic. It does not disclose rate limits or permissions, but it adequately describes the tool's 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 three sentences long, front-loads the purpose, lists key return fields, and includes an example. No redundant information.

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

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

Given the simple input schema and no output schema, the description sufficiently covers what the tool returns and includes logic for derived fields. It omits explicit output format, but overall completeness is good.

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 single parameter (market_id) has 100% schema description coverage. The description adds value by providing an example and clarifying the parameter's format, going 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 provides a one-call summary of a market including price, bid/ask, spread %, volume, changes, and liquidity rating. It distinguishes from siblings by recommending it as the best first tool for specific market queries.

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

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

The description explicitly says 'Best first tool to call when a user asks about any specific market,' providing clear usage context. It lacks mention of when not to use it or alternatives, but the guidance is strong for the intended use.

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

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

No annotations are provided, so the description must carry the full burden. It describes the output but does not disclose behavioral aspects such as error handling, rate limits, or authentication needs. However, there is no contradiction with the implicit read-only 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 concise with two sentences plus an example, no redundant information, and the key action is front-loaded. 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?

For a simple tool with one parameter and no output schema, the description adequately explains the output format. However, it lacks usage context and behavioral transparency, leaving some gaps for an AI agent to infer.

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 already fully describes the parameter 'market_id' (100% coverage). The description adds no additional meaning beyond the schema, 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 that the tool returns 24h and 7-day transacted volume split by bid/ask side for a Buda.com market. It includes an example and mentions the base currency, making the purpose specific and distinct from sibling tools like get_market_summary or get_ticker.

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 an example query but no explicit guidance on when to use this tool versus alternatives (e.g., get_market_summary, get_orderbook). There is no mention of when-not-to-use or 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?

With no annotations, the description carries full burden. It discloses sorting order, currency meanings, and typed objects, but does not mention data freshness, rate limits, or authorization requirements.

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 an example, concise and front-loaded with the main purpose. 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?

Given no output schema, the description adequately explains return values (price, amount, sorting, typed). It includes an example query. Minor missing detail: does not explicitly state that limit is optional, but schema indicates that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so description adds little beyond schema for parameter meaning. The example provides context for the limit parameter but no new semantic 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 it returns the current order book for a Buda.com market with typed objects, sorted bids and asks, and includes an example query. This distinguishes it from sibling tools like get_ticker or get_market_summary.

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 example query implies when to use the tool (e.g., asking for top buy/sell orders), and the description's focus on order book data differentiates it from other tools. However, there is no explicit statement about when not to use it or alternatives for related data.

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

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

With no annotations, the description carries full burden. It discloses that candles are aggregated client-side from raw trades (a key behavioral detail) and explains performance implications of the limit parameter. It also specifies the return format (OHLCV floats) and valid periods.

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 slightly long but front-loaded with important warnings. Each sentence adds value: warning about aggregation, output format, valid periods, and example. Could be slightly more concise but achieves good 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?

Given no output schema, the description explains the return format thoroughly. It covers all parameters and provides usage context. It lacks discussion of error cases (e.g., invalid market_id) but is otherwise complete for a read-only 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 coverage is 100%, so baseline is 3. The description adds value by explaining the limit parameter's performance trade-off and providing an example. It also restates period options, reinforcing schema information.

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 returns OHLCV candles aggregated from raw trades for specific periods. It distinguishes itself from sibling tools like get_trades (raw trades) and get_ticker (current price) by explaining the client-side aggregation and the candle format.

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 example and explains the trade-off for the limit parameter (deeper history vs slower response). It implicitly suggests when to use this tool (for candlestick data) and when not (if raw trades are needed, use get_trades). However, it does not explicitly 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?

Discloses it is a POST (not idempotent) but does not place an order, public endpoint, no API key needed. Lacks details on response format or rate limits, but overall adds good behavioral context beyond no 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?

Four sentences, front-loaded with the core action, no fluff. Efficiently conveys 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?

Covers purpose, usage, and key behavioral traits. With no output schema, a brief mention of return fields would improve completeness, but current description is sufficient for understanding the tool's role.

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 baseline 3 is appropriate. Description lists parameters and provides an example, but adds minimal extra meaning beyond what the schema already 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 it gets a server-side price quotation, computes fill estimate including fees, and distinguishes from simulate_order by specifying it is for accurate fee-tier-aware quotes.

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 advises preferring this tool over simulate_order for accurate fee-tier-aware quotes, and notes it is a public endpoint with no API key required, providing clear when-to-use 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?

With no annotations, the description carries full weight. It explains return fields (best bid, ask, spread percentage) and their types (floats, percent). It does not mention permissions or data freshness, but for a read-only data fetch, this is sufficient. No destructive behavior implied.

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 three sentences plus an example, all front-loaded with what the tool returns, then usage guidance. Every sentence 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?

For a simple tool with one parameter and no annotations or output schema, the description covers return values, units, and use case. It is fully adequate for an agent to understand and invoke 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?

The only parameter 'market_id' has 100% schema coverage. The description adds context beyond the schema by specifying that prices are in quote currency (e.g., CLP) and that spread_percentage is a float in percent. This clarifies the format and units.

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 returns best bid, best ask, spread, and spread percentage for a Buda.com market. It uses specific verbs ('Returns') and resource ('spread'), and differentiates from siblings like get_ticker or get_orderbook by focusing on liquidity evaluation.

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 using the tool to evaluate liquidity before placing a large order and provides an example question. It implies when to use it but does not explicitly contrast with siblings like get_orderbook or get_market_summary. Still, context is clear enough.

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?

Without annotations, the description fully explains behavior: computation via walking the order book, meaning of buy_pct/sell_pct, null values for insufficient depth, and the fixed sizes, providing comprehensive transparency.

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 plus an example, front-loaded with key information, 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?

Given no parameters and no output schema, the description covers all important aspects: markets, sizes, value interpretation, and even an example query, making it sufficiently 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?

No parameters exist; schema coverage is trivially 100%. The description adds substantial meaning about the tool's behavior, going beyond schema by explaining output interpretation, which is helpful.

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 returns spread and market-impact slippage for stablecoin markets on Buda.com, specifying exact markets, fixed sizes, and interpretation of values, distinguishing it from sibling tools like get_spread.

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 with example query, but lacks explicit when-to-use vs alternatives or when-not-to-use guidance, though the specificity makes usage clear.

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

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

With no annotations, the description carries the full burden. It discloses the use of internal data only, minimum trade count (500), and that it returns indicator values plus signal interpretations. It also covers the edge case of insufficient data returning a warning. Missing details on authentication needs or potential side effects, but given the context, these disclosures are adequate.

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

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

The description is four sentences long, each with a clear purpose: core function, period list, usage tip, and output/warning. It is well-structured, front-loaded, and contains 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?

The tool has three parameters, all documented. Although there is no output schema, the description explains the return format (latest indicator values and signal interpretations, or warning). The example question provides additional context. It might lack a detailed breakdown of each indicator's output, but it is sufficiently complete 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?

All three parameters have schema descriptions (100% coverage). The description adds value by explaining the effect of limit on reliability and the purpose of periods for intraday vs. longer-term analysis. This exceeds the baseline of 3.

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 specific technical indicators (RSI, MACD, Bollinger Bands, SMA 20, SMA 50) from Buda trade history, with explicit periods. It includes an example question, making its purpose unmistakable. No sibling tool computes indicators, so it is well-distinguished.

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 using shorter periods for intraday analysis and recommends a limit of 1000 for maximum depth. It also notes that insufficient candles yield a warning. However, it does not explicitly state when not to use this tool or mention alternatives (e.g., get_price_history for raw data).

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

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

No annotations are present, so the description carries the full burden. It discloses that it returns a read-only snapshot with specific fields and explains the format of price_variation_24h. It does not mention side effects, rate limits, or authentication, but as a simple read tool, the disclosure is adequate.

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

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

The description is three sentences long, front-loaded with the core purpose and details. The example is relevant and 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?

With no output schema, the description must explain return values. It lists all key fields and explains the price_variation format. It could mention the exact JSON structure, but for a ticker, it is sufficiently 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% for the single parameter market_id. The description adds context by providing examples (e.g., 'BTC-CLP') and explaining that prices are in the quote currency, which goes beyond the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool returns: a market snapshot with specific fields (last price, bid, ask, volume, price changes). It distinguishes from siblings like get_orderbook or get_price_history by focusing on current ticker data. The example query reinforces the purpose.

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 current price queries via an example, but does not explicitly state when to use this tool over siblings like get_market_summary. There is no guidance on when not to use it or alternatives.

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

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

No annotations are provided, so the description carries the full burden. It describes the output fields but does not disclose behavioral traits such as data freshness, rate limits, authentication requirements, or pagination behavior (timestamp parameter is not fully explained). The term 'recent' is vague.

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 plus an example, with no wasted words. The purpose and output structure are front-loaded. The example is practical and 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?

For a simple list tool with no output schema, the description covers the key return fields and provides a concrete example. It lacks details on error handling, edge cases (e.g., empty trades), or pagination behavior, but is sufficient for typical 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 is 3. The description does not add significant meaning beyond the schema's parameter descriptions. The example illustrates usage but does not elaborate on parameter constraints or formats.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'recent trade history for a Buda.com market as typed objects' and specifies the exact fields (timestamp_ms, amount, price, direction). This distinguishes it from siblings like get_ticker or get_market_summary.

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 retrieving trade history but provides no explicit guidance on when to use this tool versus alternatives like get_ticker or get_price_history. No exclusions or when-not-to-use scenarios are given.

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

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

State that no real order is placed, outputs are labeled simulation:true, and lists returned estimates (fill price, fee, cost, slippage). No annotations exist, so description fully covers behavioral traits.

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 plus an example. Very concise: starts with deprecation, then core functionality, usage rules, safety guarantee, and an example. 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?

Completely covers how to use both market and limit order simulations, what outputs to expect, and reaffirms no real order is placed. No output schema, so description adequately explains return values.

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 adds value by explaining the role of the 'price' parameter (optional, omitted for market orders) and clarifies that 'amount' is in base currency. Adds context 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?

The description clearly states it simulates a buy or sell order on Buda.com using live ticker data, and explicitly distinguishes from the preferred sibling get_real_quotation by marking itself as deprecated. The verb 'simulate' and resource 'order on Buda.com' are specific.

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 explicit instructions: omit price for market order simulation, supply price for limit order simulation. Includes an example query. Does not explicitly exclude other tools but the deprecation note guides toward get_real_quotation for accuracy.

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