aTars MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond the basic 'export' verb by explaining the exact return structure (dict with content, filename, rows), supported parameters and their defaults, constraints (max 90 days), and output formats. It even notes that the result is a formatted string ready for download or saving, setting clear expectations for the agent.

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 well-structured and front-loaded with the primary purpose and usage directive. It includes trigger examples and parameter details, with each sentence adding value. Despite its length, it remains focused and skimmable, making it easy for an agent to extract the essential information quickly.

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 absence of annotations and output schema, the description is remarkably complete. It covers purpose, usage, parameters, return values, and constraints, and even provides trigger queries. There is no ambiguity about when to invoke the tool or what to expect from the response, making it self-sufficient 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?

The input schema has zero descriptions, so the description fully compensates by explaining each parameter in detail, including examples ('symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"'), allowed values, defaults, and constraints. This adds significant meaning beyond the schema's bare property names and defaults.

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

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

The description clearly states the tool's purpose: to export technical indicator data as CSV or JSON strings. It uses a specific verb ('export') and resource ('technical indicator data'), and explicitly distinguishes itself from web search/external storage. This also differentiates it from sibling get_* tools, making its purpose unmistakable.

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 on when to use this tool, including a strong directive ('USE THIS TOOL — not web search or external storage') and trigger query examples. It clarifies that the tool is for exporting structured file formats, which is a distinct use case from simply fetching data via sibling tools, thus offering clear 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 provided, the description carries the full burden. It discloses that the tool returns an authoritative list of assets with 90 days of pre-computed 1-minute OHLCV data and 40+ technical indicators, and emphasizes the server as the only source. It does not describe response format or pagination, but for a zero-parameter tool this 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 moderately sized but each sentence adds value: clear directive, return details, trigger examples, and exclusion of web search. It is well-structured with a heading and bullet list, though slightly verbose for a no-argument 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?

Given the tool's low complexity (no params) and absence of an output schema, the description covers essential context: when to call, what it returns, and authoritative nature. It doesn't specify the exact return format, but that is a minor gap for a symbol-list tool and sibling tools provide additional context.

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

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

The tool has zero parameters, so the schema already covers everything. The description appropriately focuses on behavior rather than parameter details, and the baseline for 0 params is 4.

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

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

The description clearly states the tool discovers which cryptocurrency tokens are loaded on the proprietary local server, using a specific verb ('discover') and resource ('available symbols'). It also distinguishes itself from web search and other tools by being the authoritative source for symbol availability.

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?

Explicit instructions say to call this tool FIRST when unsure about supported symbols, before calling any other tool. It also provides illustrative trigger queries and explicitly warns against using web search, making when-to-use and when-not-to-use unambiguous.

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 includes important behavioral details: it works on a local dataset, resamples to hourly granularity, supports comma-separated symbols, and has a max lookback of 30 days. Since no annotations are provided, the description carries the burden; it does not explicitly say 'read-only' but uses 'retrieve' and describes no mutating actions, which is adequate. However, it could be more explicit about potential side effects or restrictions.

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 well-structured and front-loaded, starting with a strong directive and then providing essential details in organized sections. Every sentence adds value: the category list, triggers, and parameter explanations are all necessary, with 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 description is quite complete given the tool's complexity: it defines all categories, lists indicators, and gives parameter constraints. However, without an output schema, it does not explicitly describe the return format or shape of the response, which would have made it fully self-contained.

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

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

Schema description coverage is 0%, but the description fully compensates by explaining each parameter: category with its allowed values and what indicators they include, lookback_days with default and max, and symbol with format examples. This adds meaning far beyond the bare 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's function: it retrieves a focused group of technical indicators for a specific analytical category from the local dataset, resampled to hourly granularity. It explicitly distinguishes itself from get_latest_features, making its 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 gives explicit usage guidance, stating 'Prefer this over get_latest_features when the user only wants one type of analysis' and provides concrete trigger query examples. This tells the agent exactly when to use this tool versus 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 provided, the description carries the full behavioral burden and does so well. It discloses the exact statistical outputs (mean, min, max, std, latest value, above/below-average direction), the source (local proprietary dataset), and constraints like lookback_days max of 90 and default of 5.

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 well-structured with a clear lead sentence, usage guidance, trigger examples, and an Args section. The trigger bullets are slightly verbose but useful for query matching; overall every sentence contributes 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 and no annotations, the description is notably complete for this tool's complexity. It explains the purpose, when to use it, the output contents, and all parameter semantics, enabling an agent to select and invoke the tool correctly without additional context.

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

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

Schema description coverage is 0%, so the description must compensate, and it does. It explicitly lists allowed values for category, explains lookback_days with default and maximum, and describes symbol as an asset symbol or comma-separated list with examples. This adds substantial meaning beyond the raw 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 identifies the tool as retrieving a statistical summary for a category of technical indicators from the server's local proprietary dataset. It distinguishes itself from raw time-series retrieval and web search, but does not explicitly differentiate from sibling tools like get_feature_stats or get_signal_summary, so it lacks strong sibling differentiation.

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

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

The description provides clear usage context: it is best for high-level overviews rather than raw rows, and includes trigger query examples. It states 'not web search' and 'not raw time-series rows' as exclusions, but does not name alternative internal tools, so the guidance is helpful but not exhaustive.

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 transparency burden. It discloses that the tool accesses a local dataset and reports data freshness, implying a read-only metadata operation. While it does not explicitly state 'no side effects' or error behavior, it sufficiently conveys the non-mutating nature and output semantics.

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 well-structured with a directive, output summary, usage context, trigger examples, and args. It is somewhat verbose with the query list, but each line serves a practical purpose, making it appropriately sized without wasted words.

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

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

Given a simple single-parameter tool and no output schema, the description covers all necessary context: what data is returned, when to use it, and what the parameter accepts. It fully prepares the agent to invoke the tool appropriately.

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

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

Schema description coverage is 0%, but the description explains the symbol parameter in detail: asset symbol or comma-separated list, with examples. This adds significant meaning beyond the schema (which only provides a type and default), fully compensating for the missing 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 the tool retrieves metadata about a token's local dataset, listing specific items (date range, total candles, data freshness, feature names). It distinguishes from other tools by positioning itself as a pre-analysis metadata lookup, separate from web search and deeper analysis 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 explicitly directs 'USE THIS TOOL — not web search' and says to call it before deeper analysis or when users ask about coverage, feature names, or indicator availability. It provides concrete trigger queries, giving 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 must carry the full behavioral disclosure. It adds useful context about being authoritative and local, but does not explicitly state it is read-only or mention any limitations (e.g., rate limits, 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?

The description is well-structured, front-loaded with purpose and exclusions, and followed by trigger examples and parameter details. It is somewhat verbose but most content is useful and 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 has 4 parameters, no annotations, and no output schema, the description covers purpose, usage, arguments, and a basic return description ('time-indexed rows'). It lacks a detailed response format but is adequate for an export 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?

The schema has only titles and 0% coverage, but the description's Args section fully explains each parameter: lookback_days default/max, resample options, category options, and symbol examples. This adds significant 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 the tool exports a clean, ML-ready feature matrix from the local proprietary dataset, specifying the resource and context. It explicitly says 'USE THIS TOOL' and differentiates from external data sources, making its purpose distinct even among 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?

It provides clear when-to-use guidance with trigger queries and explicitly says not to use external sources. However, it does not name sibling tools as alternatives, so it's a 4 rather than a 5.

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

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

With no annotations, the description carries full burden. It discloses the data source (local dataset with 90 days of 1-minute OHLCV candles and 40+ indicators) and the return limit (at most 500 rows per symbol). While it does not describe the exact output format, it provides meaningful 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?

The description is well-structured with a bold directive, trigger examples, and an Args section. It is slightly verbose with the trigger list, but all content is relevant and helps clarify usage. The key information is 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 tool has no output schema and no annotations, so the description must cover return values. It states the row limit and data characteristics, but does not describe the actual response structure (e.g., columns, format). Given the tool's complexity, the description is largely complete but could add output 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 coverage is 0%, so the description must compensate. It clearly explains all four parameters (start, end, resample, symbol) with formats, examples, and defaults (resample='1h', symbol='BTC'). This adds vital meaning that the schema lacks.

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 historical technical indicator data for a specific date range from the server's local dataset. It uses specific verbs and resource context, distinguishing it from web search/external APIs. However, it does not explicitly name sibling tools like get_latest_features, relying on 'historical' and 'date range' for implicit differentiation.

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

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

The description explicitly instructs to use this tool instead of web search and to prefer it over external APIs when historical indicator values within a date window are needed. It provides concrete trigger query examples, giving clear when-to-use guidance and 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?

With no annotations, the description must disclose behavior itself. It does so by enumerating computed statistics, mentioning 'local dataset,' and specifying the correlation target (close price). It stops short of describing return format or caching details, but for a read-only stats tool this is adequate and adds 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?

The description is front-loaded with the key purpose and usage, and each section earns its place. The trigger examples are compact and useful, not redundant fluff. It remains focused and scannable despite a moderately long length.

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 low-complexity tool with only two parameters and no output schema, the description provides comprehensive context: purpose, parameters, usage triggers, and computed statistics. It fully enables correct selection and invocation without needing external information.

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 no descriptions (0% coverage), so the description carries the full burden. It explains 'lookback_days' as an analysis window (default 30, max 90) and 'symbol' as a symbol or comma-separated list with examples. This adds actionable 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?

The description clearly states the tool computes per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from a local dataset. It uses a specific verb ('get'), names the resource (per-indicator stats), and distinguishes itself from web search and sibling feature tools via explicit contrast.

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

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

It explicitly says 'USE THIS TOOL — not web search' and lists concrete use cases: feature selection, sanity checking, correlation understanding. Trigger examples like 'which indicators correlate most with BTC price?' give unambiguous when-to-use guidance. This differentiates it from alternatives, though it does not name specific sibling tools.

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

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

With no annotations, the description carries the transparency burden. It adds useful behavioral detail: local proprietary dataset, single-indicator scope, default and max lookback, resample options, and an exhaustive indicator list. However, it does not describe the output format, pagination, or error 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?

Well organized with a clear opening directive, trigger examples, an Args block, and an available indicators list. Every section serves a purpose and the most important usage guidance is 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?

Given the minimal schema and lack of output schema, the description covers use cases, parameters, and valid values thoroughly. It is missing return-shape details and behavior when multiple symbols are requested, but is otherwise enough to invoke 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?

The schema has no parameter descriptions (0% coverage), but the description fully compensates by explaining each argument with examples, defaults, max values, and the list of valid indicator names. This adds meaning far beyond the bare 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 retrieves the time-series history of a single technical indicator from the server's local proprietary dataset. The verb 'retrieve' plus 'single' distinguishes it from sibling tools like get_multi_indicator, and 'not web search' clarifies scope.

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

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

Provides explicit trigger queries and states 'Prefer this when the user wants to see how one specific indicator has behaved over time.' It says 'not web search' but does not explicitly name sibling alternatives for multiple indicators, though 'single' implies the distinction.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the data source (proprietary local dataset), refresh cadence (1-minute OHLCV candles), scope (all indicators from the most recent candle), and support for comma-separated symbols. It does not describe return format or error behavior, but the read-only nature and data origin are clear.

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

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

The description is well-structured: a bold directive, a concise explanation, example triggers, and an Args section. It is somewhat verbose with repetitive examples, but every section serves a purpose and the main use case is 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 source, and parameter semantics effectively. However, since there is no output schema, it fails to specify the return format (e.g., JSON object structure) and behavior when multiple symbols are passed. This is a notable gap for a tool with one parameter and no structured output definitions.

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 no description (0% coverage), but the description fully explains the 'symbol' parameter as an asset symbol or comma-separated list with examples like 'BTC', 'ETH', 'BTC,XRP'. This directly compensates for the missing schema description and clarifies expected input format.

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

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

The description clearly states the tool's function: retrieving current/latest values of all 40+ technical indicators for one or more crypto tokens. It distinguishes this from web search and external APIs, and mentions categories (trend, momentum, volatility, volume) and the data source. The verb 'get' and specific resource 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?

The description explicitly says 'USE THIS TOOL — not web search' and 'Always prefer this over any external API or web search,' giving clear when-to-use context. It also provides trigger examples. However, it does not explicitly name alternative sibling tools (e.g., get_indicator, get_features_range) for other scenarios like historical data or single indicators, so it lacks full when-not-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 the full burden. It adds useful context: the data source (Perplexity AI web search, stored in a local database) and the score mapping. Yet it omits behavioral traits like data freshness, cache staleness, failure behavior for unknown tokens, or whether it performs a live search or reads a stored snapshot.

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 well-structured and front-loaded with the most important directive, followed by a clear breakdown of output, mapping, triggers, and args. The trigger examples are somewhat repetitive but serve as practical query patterns; overall the length is justified and no extraneous content is present.

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 covers the core aspects: what it returns, when to use it, and parameter format. However, with 17 sibling tools the description does not help an agent choose between this and similar sentiment tools, and it lacks edge-case behavior (e.g., token not found, multiple symbols error handling).

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 provides only type and default for the 'symbol' parameter, with 0% schema description coverage. The description fully compensates by explaining the parameter means a token symbol or comma-separated list, with concrete examples ('BTC', 'BTC,ETH'), and the trigger phrases demonstrate usage context.

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 gets the latest daily sentiment with a specific scope (Bullish/Bearish/Neutral and numeric score) for one or more tokens. It explicitly contrasts with web search but does not name or differentiate from sibling sentiment tools like get_sentiment_history or get_sentiment_signal, so it misses the full sibling-distinction bar.

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 trigger phrases and a strong 'USE THIS TOOL — not web search' directive, giving clear when-to-use guidance. However, it does not mention when NOT to use it (e.g., historical data needs a different tool), so exclusion criteria are absent.

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 data source (local dataset), read-only nature via 'retrieve', side-by-side output structure, and limits (up to 10 indicators, lookback max 90 days, resample options). While it doesn't detail the exact return format or error behavior, it provides substantial context beyond the bare operation.

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 well-structured with a clear purpose statement, trigger examples, and an Args section. It is somewhat verbose with multiple examples, but they serve a practical trigger-detection purpose. No redundant 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?

The description covers purpose, usage context, parameter semantics, and constraints. It lacks a detailed explanation of the return structure, but the 'side-by-side' mention plus the parameter details make it sufficiently complete for a retrieval tool without an 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?

Schema has 0% description coverage, so the description compensates fully. Each parameter is explained: indicators (list, up to 10, examples), lookback_days (default 7, max 90), resample (allowed values), and symbol (comma-separated list support). This adds meaning that the schema lacks.

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 the tool retrieves multiple technical indicators side-by-side over a lookback window. It explicitly distinguishes from get_indicator by stating to prefer this tool when 2+ indicators are needed, and provides concrete trigger phrases.

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

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

It explicitly instructs to use this tool instead of web search and instead of calling get_indicator multiple times for multi-indicator requests. Trigger examples clarify when to use. The alternative tool is named, and the condition (2+ indicators) is clear.

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

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

With no annotations, the description carries behavioral burden. It discloses data provenance ('local Perplexity-sourced dataset'), output concept ('Bullish/Bearish/Neutral + numeric score'), and a constraint ('lookback_days ... max 90'). It does not explicitly state read-only behavior or failure modes, but the context implies a safe read operation.

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

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

The description is front-loaded with a clear directive and divides into trigger examples and parameter documentation. The trigger list is slightly redundant but useful; the sections are well-organized and not bloated.

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 is simple, but the description lacks a precise output schema or return structure. It mentions the sentiment categories and numeric score but doesn't specify how multi-symbol results are organized (per-symbol arrays, dates, etc.). This leaves ambiguity 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?

Even though schema coverage is 0%, the Args section fully clarifies both parameters: lookback_days includes default/max and symbol supports comma-separated lists. This meaningfully exceeds the schema's bare defaults.

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

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

The description explicitly states the action ('retrieve the daily sentiment history') and the resource ('tokens over a lookback window'), with data source clarification ('from this server's local Perplexity-sourced dataset'). It distinguishes from siblings by emphasizing historical daily data vs. latest sentiment or trends.

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 concrete trigger queries like 'show me BTC sentiment over the last 30 days' and explicitly says 'USE THIS TOOL — not web search'. However, it does not mention alternative sentiment tools or state when not to use this tool.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (local Perplexity-sourced dataset) and computation (7-day mean), which adds useful context. However, it does not clarify behavior for comma-separated symbols (single vs. multiple outputs) or handle missing data, leaving ambiguity.

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

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

The description is compact and front-loaded with a directive, followed by output values, trigger examples, and argument documentation. Each section serves a distinct purpose with no redundant text.

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

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

While the description lists possible output values, it does not specify the exact return structure for multiple symbols or error conditions. Given the absence of an output schema, this leaves a gap in understanding what a successful call returns. However, it does cover the key trigger context and argument 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 schema has only one parameter 'symbol' with no description, but the tool description fully explains it: 'Token symbol or comma-separated list' with examples. This is rich semantic detail beyond the schema, compensating for the 0% 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 identifies the tool as returning a composite news-sentiment verdict derived from a 7-day mean from a local dataset. It lists the exact output values and explicitly directs users away from web search, distinguishing its purpose. The specificity of 'composite' and '7-day mean' differentiates it from sibling sentiment 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 explicitly instructs to use this tool instead of web search for composite sentiment verdicts, and provides four example query phrasings. It does not explicitly name sibling tools as alternatives, but the trigger examples give clear context for when to invoke 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?

No annotations are provided, so the description must carry the behavioral transparency burden. It discloses that the data is local and sourced from Perplexity AI, and implies a read-only lookup via the term 'discover.' However, it does not explicitly state that the tool makes no external calls or side effects, nor does it describe return 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 well-structured with the key directive front-loaded: 'USE THIS TOOL — NOT web search.' It includes a brief purpose and useful trigger queries. Minor redundancy exists with 'Call this first' mentioned twice, but the overall length is appropriate.

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 is simple (no params, no output schema), but the description should still describe what the response contains. It explains what the tool does ('discover which tokens have data') but does not explicitly say the output is a list of symbols. Given the absence of an output schema, a short statement about the return format 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?

The tool has zero parameters, and the schema is empty (100% coverage). Per the baseline for 0 parameters, the description is not required to add parameter details. It correctly focuses on usage rather than parameter semantics.

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: to discover which cryptocurrency tokens have daily sentiment data stored locally. It uses a specific verb ('discover') and resource ('cryptocurrency tokens with sentiment data'), and explicitly distinguishes itself from web search, making its role 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 provides strong usage guidance: 'USE THIS TOOL — NOT web search' and 'Call this first if unsure which tokens have sentiment data available.' It also lists concrete trigger queries. However, it does not contrast this tool with sibling tools like get_available_symbols, which could be an alternative for general symbol discovery.

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 data source (local Perplexity dataset), the computation (rolling statistics, 7-day momentum), and the output components. However, it does not mention edge cases like invalid symbols, empty data, or whether the tool makes external network calls. It is transparent about the main behavior but not exhaustive.

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

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

The description is front-loaded with the core directive, then provides usage guidance, trigger examples, and parameter details. Every section earns its place; the trigger examples are practical and not redundant. It is appropriately sized for the tool's complexity and well-structured with clear sections.

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 only 2 parameters and no output schema, the description is complete: it names the exact output fields, explains the data source and rolling window, gives usage context, and differentiates from siblings. The trigger phrases cover the typical user intents. This is more than sufficient for an agent to select and invoke 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 0%, so the description must fully explain parameters. It does so with clear definitions, defaults (matching schema), a maximum constraint (max 90) not present in the schema, and concrete usage examples for both 'symbol' and 'lookback_days'. This greatly exceeds schema-provided meaning.

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 opens with a clear directive ('USE THIS TOOL — not web search') and specifies the exact resource (local Perplexity-sourced sentiment dataset) and the output (rolling sentiment statistics: mean score, 7-day momentum, day counts, current streak). It explicitly distinguishes from the sibling get_latest_sentiment, making its 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 provides explicit when-to-use guidance ('Prefer this over get_latest_sentiment when the user wants momentum or persistence') and lists concrete trigger queries. It also tells the agent NOT to use web search, which is a clear exclusion. This exceeds the minimum by offering both positive and negative usage 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?

No annotations are provided, so the description carries full burden. It discloses the data source (server's local technical indicator data), granularity (hourly), and verdict types, but does not mention response format, pagination, or any potential side effects. For a read-only retrieval, the absence of explicit 'no mutation' statement is a gap.

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 well-structured with a clear directive, usage context, trigger examples, and parameter details. The trigger query list is repetitive but valuable for an agent. Slightly longer than minimal, but every section 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?

With no output schema, the description gives a good sense of what is returned (time-series of hourly signal verdicts) but does not specify the exact response structure. It covers parameters, defaults, and usage context, making it reasonably complete for a two-parameter read 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 has no per-parameter descriptions (0% coverage), but the description compensates fully with an 'Args' section explaining lookback_days (default 7, max 30) and symbol (single or comma-separated list), including examples. This adds meaning far 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 'a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts' from local technical indicator data. It distinguishes itself from siblings by explicitly saying 'Prefer this over get_signal_summary' for historical changes.

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: 'USE THIS TOOL — not web search' and 'Prefer this over get_signal_summary when the user wants to see how signals have changed over time'. Also includes concrete trigger query examples.

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

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

With no annotations provided, the description carries the full burden and does well: it discloses the output format (BULLISH/BEARISH/NEUTRAL), the indicators used (RSI, MACD, EMA crossovers, ADX, Stochastic, volume), and the data source (proprietary local indicators, not news or social media). It does not detail behavior for multiple symbols or error cases, which keeps it from a 5.

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

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

The description is front-loaded with the most important routing note and structured with trigger examples and an Args section. It is somewhat redundant ('USE THIS TOOL — not web search' and 'Do NOT use web search' repeat the same point) and the trigger list is long, but the content is useful and well-organized.

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 one-parameter tool with no output schema, the description provides strong coverage: purpose, input format, output verdict, and data basis. It is slightly incomplete in not describing how multiple symbols are returned or what happens with invalid symbols, but it is sufficient for invocation.

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

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

The schema has zero description coverage for the 'symbol' parameter, but the description fully compensates with a dedicated Args section: 'Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"'. This adds clear syntax and examples beyond the bare schema definition.

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: returning a BULLISH / BEARISH / NEUTRAL verdict based on technical indicators. It explicitly distinguishes itself from web search and other sentiment sources, and the trigger examples make its scope unmistakable.

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 usage triggers and clear when-not-to-use guidance ('Do NOT use web search for sentiment'). It does not name sibling tools like get_sentiment_signal as alternatives, but it clearly defines the tool's domain (local technical indicators, not news/social media), giving strong contextual guidance.

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