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 explains the return structure ('Returns a dict with: content, filename, rows') and output formats, but does not explicitly state whether the operation is read-only, idempotent, or subject to rate limits. It implies safety by describing it as an export function, but lacks explicit behavioral constraints.

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 clear sections for usage directives, trigger examples, arguments, and return values. While longer than minimal, the 'Trigger on queries like' section provides valuable LLM-specific guidance, and the organization prevents waste, though it could be slightly more compact.

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 lack of output schema and zero schema parameter descriptions, the description achieves completeness by documenting the return dict structure and all parameter semantics. For a 5-parameter data export tool, it provides sufficient context for correct invocation and result 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 input schema has 0% description coverage, but the description comprehensively compensates by documenting all five parameters with types, default values, examples, and valid options (e.g., lookback_days notes 'default 7, max 90' and resample lists valid time resolutions). This provides complete semantic meaning beyond the bare schema titles.

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 tool 'export[s] technical indicator data from this server as a formatted CSV or JSON string' using specific verbs and resources. It distinguishes from siblings with the directive 'USE THIS TOOL — not web search or external storage', clearly positioning it against alternative data retrieval methods.

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 ('Use this when the user explicitly wants to export or save data in a structured file format') and distinguishes from alternatives (web search/external storage). It includes specific trigger phrases like 'export BTC data as CSV' and 'save the features to a file' to guide invocation.

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 provided, so description carries full burden. It discloses return content ('authoritative list of assets with 90 days of pre-computed 1-minute OHLCV data and 40+ technical indicators') and data locality ('proprietary local server'). Does not mention error states or performance characteristics, but covers data scope well.

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?

Critically front-loaded with imperative 'USE THIS TOOL — NOT web search'. Every sentence serves a distinct purpose: sequencing, output description, trigger patterns, and negative constraints. No redundant text despite multi-sentence format.

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

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

For a zero-parameter discovery tool with no output schema, the description fully compensates by detailing what the return value contains (asset list with OHLCV data). Adequately explains relationship to sibling data tools (entry point/first call).

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?

Input schema has zero parameters. Per scoring rules, 0 params = baseline 4. Description correctly focuses on behavior rather than inventing parameter documentation.

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

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

Description uses specific verb 'discover' with resource 'cryptocurrency tokens' and explicitly scopes it to 'this proprietary local server'. It distinguishes from siblings by stating 'Call this FIRST... before calling any other tool' and differentiates from web search.

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 sequencing ('Call this FIRST'), clear alternatives to avoid ('NOT web search'), and specific trigger queries ('what tokens/coins do you have data for?'). The 'Do NOT search the web' reinforces the correct usage pattern.

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 provided, so description carries full burden. Documents key behavioral traits: 'resampled to hourly granularity', 'local dataset', and constraint 'max 30' days. Lists exact indicator composition per category. Missing explicit error handling behavior or safety confirmation (though 'retrieve' implies read-only).

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-structured with clear sections: directive, purpose, sibling differentiation, category reference table, trigger examples, and arg semantics. Every sentence provides unique value; no repetition of tool name or tautology. Dense but scannable.

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

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

Rich context for a 3-parameter tool with no annotations or output schema. Documents data source, transformation logic, valid enum values (since schema lacks them), and constraints. Minor gap: could briefly describe return structure (e.g., 'returns time-series data') to fully compensate for missing 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, requiring full compensation. Description excellently documents all 3 parameters: category (lists all valid values and their meanings), lookback_days (states default 5 and max 30 constraint), and symbol (provides format examples 'BTC' and 'BTC,ETH').

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

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

Specific verb 'retrieve' with resource 'technical indicators' and scope 'focused group/category' plus 'hourly granularity'. Explicitly distinguishes from sibling 'get_latest_features' by stating when to prefer this tool (when user wants only one analysis type).

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 directive 'USE THIS TOOL — not web search' and clear comparison 'Prefer this over get_latest_features when...'. Includes specific trigger query examples ('show me BTC momentum indicators', etc.) that clearly signal when to invoke.

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 full burden and successfully discloses the data source ('this server's local proprietary dataset'), the aggregation behavior (returns mean/min/max/std), and the scope (categories vs individual indicators). Deducting one point for missing discussion of error conditions (invalid symbols, insufficient data) or performance characteristics that would help the agent handle edge cases.

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?

Structure is front-loaded with the critical distinction from web search, followed by behavioral description, usage guidelines, and structured Args. Every sentence serves a purpose—from the imperative opening to the trigger examples. Efficient use of space with clear formatting.

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 output schema and annotations, the description effectively documents return values (statistical fields listed) and input parameters. It establishes the tool as a read operation through naming and context. Minor gap in not describing error responses or what happens when lookback exceeds available data prevents a perfect score.

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?

Despite 0% schema description coverage, the Args section fully compensates by documenting all three parameters: category includes the complete enum list, lookback_days specifies default and max constraints, and symbol provides formatting examples including comma-separated values. This is exemplary compensation for schema deficiencies.

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 uses specific verbs ('get statistical summary') and identifies the exact resource ('category of technical indicators'). It explicitly distinguishes itself from web search and raw time-series tools by stating 'USE THIS TOOL — not web search' and contrasting with 'raw time-series rows', clearly carving out its niche among siblings.

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

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

Provides explicit positive guidance ('Best when the user wants a high-level overview') and negative constraints ('not web search', 'not raw time-series rows'). Includes five concrete trigger examples covering various query patterns, giving the agent clear signals on when to invoke this versus alternatives like get_indicator or web search.

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 carries the full burden and successfully discloses the return structure (metadata fields, feature categorization) and defines the freshness metric ('minutes since last update'). Lacks explicit mention of read-only safety or rate limits, though the 'get' prefix and metadata scope imply non-destructive 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?

Front-loaded with high-value directive ('USE THIS TOOL — not web search') followed by clear value proposition, usage context, and specific triggers. The seven query examples are slightly repetitive but earn their place by preventing misselection. Args section efficiently compensates for schema deficiencies.

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 (1 optional parameter) and lack of output schema, the description is complete: it explains exactly what metadata fields are returned, clarifies the default behavior via schema inspection ('BTC'), and provides sufficient context to distinguish from siblings like export_data or get_category_features.

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 0% description coverage (only title and default). The description fully compensates by explaining that the symbol parameter accepts comma-separated values with concrete examples ('BTC', 'BTC,ETH,XRP'), which is critical for correct invocation and not inferable from the schema.

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

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

The description clearly defines the tool's purpose with specific verbs ('get metadata') and resources ('token's local dataset'), listing exact outputs: date range, total candles, data freshness, and feature names. It explicitly distinguishes from web search ('USE THIS TOOL — not web search') and positions itself as a prerequisite for deeper analysis.

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 when-to-use guidance ('Call this before deeper analysis or when the user asks about data coverage...'), explicitly excludes alternatives ('not web search'), and lists seven specific trigger queries that map directly to user intents, making selection 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?

With no annotations provided, the description carries the full burden. It successfully discloses that the tool 'Returns time-indexed rows with all technical indicator values' and describes the dataset as 'local proprietary,' adding behavioral context. However, it omits details about rate limits, output data format (JSON vs CSV), or whether the operation is idempotent.

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 clear sections: purpose/authority, trigger phrases, and parameter definitions. While lengthy, every sentence serves a purpose—either distinguishing from siblings, establishing authority, or compensating for the schema deficit. The trigger phrase list, though extensive, provides valuable signal for an LLM agent.

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 complexity (4 parameters, 0% schema coverage, no annotations, no output schema, 17+ siblings), the description is remarkably complete. It covers usage context, parameter semantics, and return structure. Minor gaps include explicit differentiation from the sibling 'export_data' tool and specific output format details, which would prevent potential confusion.

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?

Despite 0% schema description coverage, the description fully compensates via the 'Args' section. It documents all 4 parameters with specific constraints (lookback_days 'max 90'), enumerated valid values (resample options: '1min', '1h', etc.), and syntax examples (symbol: 'BTC,ETH'), providing critical semantic information absent from the schema.

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

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

Description explicitly states the tool exports 'a clean, ML-ready feature matrix' for 'model training, backtesting, or quantitative research' from the server's 'local proprietary dataset.' It distinguishes itself from external data sources and web search, clearly positioning it as the authoritative technical indicator source versus sibling sentiment or single-indicator 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?

Provides explicit when-to-use guidance ('USE THIS TOOL — not any external data source'), explicit exclusions ('Do not use web search'), and concrete trigger phrases ('give me feature data for training', 'export BTC indicator matrix'). This effectively steers the agent away from alternatives like get_indicator or external knowledge.

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 successfully discloses data scope (90 days retention, 40+ indicators), provenance (local dataset), and operational limits (500 rows per symbol). Minor gap: does not specify error behavior for invalid date ranges or missing symbols.

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?

Front-loaded with the critical directive 'USE THIS TOOL' followed by purpose, trigger examples, and parameter specifications. Structure is logical with clear visual separation. Slightly verbose due to four example queries, but they serve a valid pattern-matching purpose for the agent.

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 adequately covers the tool's purpose, constraints, and parameters. Mentions return data characteristics (40+ indicators, 500-row limit) but could improve by briefly describing the output structure (e.g., 'returns JSON with timestamp and indicator columns').

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?

Despite 0% schema description coverage, the Args section fully compensates by documenting all 4 parameters with data types (YYYY-MM-DD format for dates), enumerated values for resample ('1min', '1h', '4h', '1d'), and formatting guidance for symbols (comma-separated list).

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 tool 'retrieve[s] historical technical indicator data for a specific date range' from a 'local dataset (90 days of 1-minute OHLCV candles with 40+ indicators)'. It clearly distinguishes itself from siblings by mandating 'USE THIS TOOL — not web search' and specifying 'Prefer this over any external API'.

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 on when to use ('when the user needs historical indicator values within a date window') and when not to use (explicitly names 'web search' and 'external API' as alternatives to avoid). Includes concrete trigger query patterns for pattern matching.

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 full burden and discloses the statistical computation details (Pearson correlation, null rates, percentiles) and data source ('server's local dataset'). However, it lacks operational details like side effects, caching behavior, or error conditions, though 'get' implies read-only safety.

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

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

Well-structured with front-loaded imperative guidance, followed by purpose, use cases, trigger examples, and parameter definitions. The five trigger examples add length but earn their place by clarifying intent. Args section is efficiently formatted. Minor deduction for slight verbosity in trigger list.

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

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

No output schema exists, but the description comprehensively lists returned statistical fields (mean, std, min, p25, p75, max, null rate, Pearson correlation). For a 2-parameter tool, this is sufficient, though explicit mention of return structure (e.g., nested JSON format) would elevate it to 5.

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 (only titles). The description fully compensates by defining lookback_days as 'Analysis window in days (default 30, max 90)' and symbol as 'Asset symbol or comma-separated list' with concrete examples. Syntax and constraints are clearly documented.

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 tool provides 'per-indicator statistical profiling' including specific measures (mean, std, Pearson correlation, etc.) from a local dataset. It clearly distinguishes this from siblings by emphasizing statistical analysis and correlation with close price, rather than raw data retrieval like get_indicator or get_latest_features.

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?

Exceptional guidance with explicit directive 'USE THIS TOOL — not web search' to differentiate from external knowledge sources. Provides five concrete trigger phrases and three specific use cases (feature selection, sanity checking, correlation analysis). Clearly signals when to invoke based on query patterns.

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 full disclosure burden and succeeds well: it identifies the data source ('local proprietary dataset'), discloses constraints ('max 90' days), lists all 35+ valid indicator values (critical since schema lacks enums), and notes default values. Minor gap: no mention of rate limits, error behavior for invalid symbols, or exact return structure beyond 'time-series'.

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-structured and front-loaded with the most critical distinction (vs web search) first. The Args section uses a clear visual format. The extensive indicator list, while long, is necessary given the lack of schema enums. Only minor deduction for the dense block of available indicators which could theoretically be truncated with 'etc.' but is actually valuable here.

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?

Highly complete given constraints: no output schema exists, yet the description conveys the return type ('time-series history'). It adequately covers all parameters despite zero schema coverage. Minor gap: lacks description of error states (e.g., invalid indicator names) or data freshness guarantees, but sufficient for effective agent 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?

Given 0% schema description coverage, the description fully compensates by providing rich semantics for all 4 parameters: 'indicator' includes valid column names and a complete enum list; 'lookback_days' specifies default and max; 'resample' lists valid time resolutions; 'symbol' explains comma-separated formatting with examples. This exceeds the baseline requirement for undocumented schemas.

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 tool 'retrieve[s] the time-series history of a single technical indicator' with a specific verb and resource. It effectively distinguishes from siblings by emphasizing 'single' technical indicator (contrasting with get_multi_indicator) and explicitly directing 'USE THIS TOOL — not web search' to differentiate from external search 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?

Provides explicit when-to-use ('Prefer this when the user wants to see how one specific indicator has behaved over time'), when-not-to-use (explicitly excludes web search), and a comprehensive bulleted list of trigger query patterns that function as clear invocation 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 delivers substantial context: data freshness ('continuously refreshed 1-minute'), computation basis ('most recent candle'), indicator categories ('trend, momentum, volatility, volume'), and locality ('local dataset' implying no external latency/auth). Missing explicit mention of read-only safety, error handling for invalid symbols, or rate limits prevents 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?

Perfectly front-loaded with the critical differentiator (use this, not web search) in the first sentence. Structure flows logically from priority statement → functionality → data details → usage triggers → parameter spec. Every sentence serves the agent's decision-making; no filler or tautology 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?

Given zero schema descriptions, no annotations, and no output schema, the description adequately compensates by detailing what data is returned (40+ specific indicator types) and input formatting. Lacks explicit description of the output structure (JSON shape, key naming) which would be ideal given the absence of an output schema, but the content description is thorough enough for functional 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 description coverage is 0%, requiring the description to compensate. The 'Args' section successfully documents the symbol parameter's format ('comma-separated list') and provides concrete examples ('BTC', 'ETH', 'BTC,XRP'). Deducting one point for not mentioning the schema-defined default value 'BTC' or validation constraints (e.g., case sensitivity).

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 tool retrieves 'current/latest values of all 40+ technical indicators' from '1-minute OHLCV candles', specifying the resource (crypto tokens), scope (latest only), and data source (proprietary local dataset). It distinguishes from siblings like get_features_range (historical) and get_latest_sentiment (sentiment vs technical) through its focus on comprehensive technical indicator snapshots.

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 with 'USE THIS TOOL — not web search' and 'Always prefer this over any external API or web search', establishing clear priority over alternatives. Includes concrete trigger examples ('what are the current indicators for BTC?', 'what's the RSI/MACD/EMA for [coin] right now?') that help the LLM recognize invocation patterns. The 'all 40+' scope distinguishes it from selective indicator tools like get_indicator.

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 full disclosure burden effectively. It reveals the data provenance (Perplexity AI web search stored locally), the scoring methodology (Bullish = +1, Neutral = 0, Bearish = -1), and temporal scope (daily). Missing minor details like cache freshness guarantees or error behavior when symbols are invalid, but covers the essential 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?

Well-structured with high information density: imperative directive first, then value proposition, scoring legend, trigger patterns, and parameter specification. The trigger examples, while consuming space, earn their place by providing clear invocation patterns. The 'Args:' section is slightly informal but functional. No redundant or wasted sentences.

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?

Appropriate for a single-parameter tool of moderate complexity. Despite lacking an output schema, the description explains the return structure (sentiment category + numeric score). Given the crowded sibling namespace (get_sentiment_history, get_sentiment_trend, etc.), it adequately positions this tool as the 'latest' point-in-time retrieval option.

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 0% description coverage (no descriptions on the symbol property), but the description fully compensates by defining the parameter as 'Token symbol or comma-separated list' with concrete examples ('BTC', 'BTC,ETH'). This provides complete semantic 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 explicitly states the tool retrieves 'the most recent daily sentiment (Bullish/Bearish/Neutral) and numeric score for one or more crypto tokens.' It specifies the data source (Perplexity AI via local database) and clearly positions this as the 'latest' retrieval tool, distinguishing it from sibling history/trend tools through temporal specificity.

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 directive 'USE THIS TOOL — not web search' establishing clear preference over a general alternative. Includes specific trigger examples ('what's the news sentiment for BTC today?', 'is ETH bullish based on news?') that guide invocation patterns. Lacks explicit comparison to sibling sentiment tools (e.g., when to use get_sentiment_history vs this one), though the 'latest' descriptor provides implicit differentiation.

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 discloses the data source as 'this server's local dataset.' It documents operational constraints including maximum lookback (90 days), indicator limit (10), and default values (7 days, 1h), though it could clarify error handling behavior or explicitly confirm the read-only nature of the 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 efficiently front-loaded with the value proposition and sibling differentiation, followed by specific trigger patterns and a structured Args block. Every sentence serves to either differentiate the tool from alternatives or document parameter semantics with zero 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 the lack of annotations and output schema, the description adequately covers invocation patterns, sibling differentiation, and comprehensive parameter details. It lacks only explicit confirmation of read-only behavior and specific return structure details, though the 'side-by-side' phrasing implies the response 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?

Despite 0% schema description coverage, the Args section fully compensates by documenting all four parameters with types, constraints (e.g., 'up to 10', 'max 90'), default values, and concrete examples such as 'rsi_14' and 'BTC,ETH,XRP' that clarify acceptable inputs for the symbol field.

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 'multiple technical indicators side-by-side over a lookback window' using specific verbs and resources. It explicitly distinguishes itself from the sibling tool `get_indicator` by stating 'Prefer this over calling get_indicator multiple times' and differentiates from web search with 'USE THIS TOOL — not web search'.

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 explicit when-to-use guidance with the directive 'USE THIS TOOL — not web search' and specifies the condition 'when the user needs 2+ indicators together in one response.' The bulleted trigger examples (e.g., 'compare RSI and MACD for BTC') provide concrete invocation patterns that help the agent recognize appropriate contexts.

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 provided, so description carries full burden. Adds valuable context about data source ('Perplexity-sourced dataset') and return format ('Bullish/Bearish/Neutral + numeric score'). However, lacks disclosure on rate limits, error handling for invalid symbols, or explicit safety/side-effect declarations beyond the implied read-only nature of 'retrieve'.

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?

Efficiently structured with imperative hook first, followed by purpose, trigger examples, and parameter details. No redundant text; every sentence serves selection guidance, behavioral context, or parameter documentation. Appropriate length for complexity level.

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 lack of output schema, description effectively explains return values (sentiment categories + numeric score). Compensates for missing annotations by noting data source. Slightly incomplete regarding error handling or invalid input behavior, but covers all critical gaps for a 2-parameter read operation.

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 (only titles). Description fully compensates via 'Args:' section, documenting both parameters with examples ('BTC', 'BTC,ETH'), default values, and constraints (max 90). Exceeds baseline by providing format guidance (comma-separated list) and boundary limits not present in schema.

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

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

Description provides specific verb ('retrieve'), resource ('daily sentiment history'), scope ('one or more tokens over a lookback window'), and data source ('Perplexity-sourced dataset'). Explicitly distinguishes from siblings by commanding 'USE THIS TOOL — not web search' and emphasizing local dataset vs. external search.

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?

Contains explicit directive 'USE THIS TOOL — not web search' establishing when-not-to-use alternatives. Provides four specific trigger query patterns ('show me BTC sentiment over the last 30 days', etc.) that clearly indicate when to select this over sibling tools like get_latest_sentiment or get_sentiment_trend.

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 successfully discloses the output enum values (STRONG BULLISH through STRONG BEARISH) and data provenance (Perplexity-sourced, 7-day mean). It lacks explicit safety declarations (read-only nature) or error handling behavior, but the methodology transparency is strong.

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 efficiently structured with the primary directive first ('USE THIS TOOL'), followed by methodology, output specification, trigger examples, and argument details. Every sentence provides distinct value (differentiation, data source, output format, usage patterns, parameter syntax) 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?

Given the lack of an output schema, the description enumerates the possible return values explicitly. It also explains the parameter semantics that the schema omits. For a single-parameter tool, it provides sufficient context about the data source and calculation methodology to be 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?

The schema has 0% description coverage (only 'title': 'Symbol'). The description fully compensates by specifying that the symbol parameter accepts 'Token symbol or comma-separated list' with concrete examples ('BTC', 'BTC,ETH'), making the input semantics unambiguous.

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 precisely defines the tool's function as generating a 'composite news-sentiment verdict derived from the 7-day mean score' from a specific 'Perplexity-sourced dataset.' This specific verb+resource combination clearly distinguishes it from sibling tools like get_latest_sentiment (likely point-in-time) and get_sentiment_trend (likely directional).

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 explicit guidance with 'USE THIS TOOL — not web search' and lists specific trigger query patterns ('overall news sentiment signal for BTC'). However, it lacks explicit differentiation from sibling sentiment tools (get_latest_sentiment, get_sentiment_trend) on the same server, relying only on implicit distinction through the '7-day mean' description.

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 adds valuable behavioral context about data provenance ('sourced from Perplexity AI') and locality ('stored on this local server'), plus data frequency ('daily'). However, it omits return format details (list vs object structure) which is a significant gap given the absence of an output 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 appropriately front-loaded with the critical 'USE THIS TOOL — NOT web search' directive. The structure is logical: primary instruction, usage timing, then trigger examples. The quoted bullet points for trigger queries are slightly verbose in formatting but earn their place by providing concrete pattern matching guidance.

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

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

For a zero-parameter discovery tool without output schema, the description adequately covers purpose, prerequisites, and data source. It implies the return value (available symbols) through the description of what it discovers. A minor gap exists in not explicitly sketching the return structure (e.g., 'returns a list of token symbols'), but overall sufficient for correct agent operation.

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

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

The input schema has zero parameters, which per the rubric establishes a baseline of 4. The description correctly implies no filtering parameters are needed for this discovery operation, though it doesn't explicitly state 'no parameters required'.

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 tool 'discover[s] which cryptocurrency tokens have daily sentiment data' using specific verbs and resources. It clearly distinguishes this discovery function from sibling tools like get_latest_sentiment or get_sentiment_history that presumably require specific symbols.

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?

Excellent explicit guidance: 'USE THIS TOOL — NOT web search' provides clear negative constraints, and 'Call this first if unsure which tokens have sentiment data available' establishes the prerequisite workflow relative to other sentiment tools. The trigger query examples further clarify invocation context.

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

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

No annotations provided, so description carries full burden. It discloses data source (Perplexity-sourced local dataset) and specific return values (mean score, momentum, counts, streak). Could improve by explicitly stating read-only/safe nature and any rate limiting, but adequately covers data provenance and output structure.

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?

Efficiently structured with clear sections: direct call-to-action, sibling differentiation, trigger examples, and parameter documentation. No wasted words; every sentence provides selection or invocation guidance. Front-loaded with explicit usage directive.

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

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

Despite no output schema and no annotations, description comprehensively covers return values (specific statistics listed), data source, sibling relationships, parameter constraints, and usage triggers. Complete for a 2-parameter analytical 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 0%, but description fully compensates via the Args section. Defines 'lookback_days' with constraints (default 30, max 90) and semantics ('Analysis window'), and 'symbol' with format guidance (comma-separated list) and examples ('BTC', 'BTC,ETH').

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

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

Description explicitly states it retrieves 'rolling sentiment statistics (mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak)' from a 'Perplexity-sourced sentiment dataset'. It clearly distinguishes from sibling 'get_latest_sentiment' by contrasting momentum/persistence with single-day readings.

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_latest_sentiment when...'. Includes specific trigger examples covering momentum, streaks, and trend queries, making invocation conditions 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?

With no annotations provided, the description carries the full burden and discloses key behavioral traits: hourly granularity, local data source (not external), and the 30-day maximum constraint. However, it does not explicitly confirm the read-only nature (implied by 'retrieve' but not stated as safe) or describe the return format/shape since no output schema exists.

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-structured and front-loaded with the directive and purpose. The trigger examples section, while lengthy, serves a valid pattern-matching function for LLM tool selection. The Args section is necessary given the schema lacks descriptions. No tautological or redundant sentences.

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 2-parameter retrieval tool with no output schema, the description adequately covers functionality and parameters. It explains what data is returned (verdicts) but could improve by describing the return structure (e.g., array of objects with timestamps) since no output schema is available to document this.

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% (titles only), but the description fully compensates by documenting both parameters: 'lookback_days' includes default (7), max (30), and semantics ('Days of signal history'); 'symbol' includes format guidance ('comma-separated list') and concrete examples ('BTC', 'BTC,ETH').

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

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

The description explicitly states the tool 'retrieve[s] a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts' with specific verb (retrieve), resource (signal verdicts), and scope (hourly time-series). It clearly distinguishes from sibling 'get_signal_summary' by contrasting historical trends vs. current readings.

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...'. Includes five specific trigger query patterns (e.g., 'how has the BTC signal changed', 'signal trend for [coin]') that clearly define when to select this tool over 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 and successfully discloses the return format (BULLISH/BEARISH/NEUTRAL), the specific indicators used (RSI, MACD, EMA crossovers, ADX, Stochastic, volume), the data source (latest candle, locally-computed), and exclusions (not news, not social media). Lacks operational details like rate limits or error handling.

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-structured with clear sections: directive, methodology, trigger examples, constraints, and args. The six trigger examples are justified for LLM pattern matching but make it slightly verbose. Every sentence earns its place by aiding tool selection or invocation.

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

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

Given zero schema descriptions, no annotations, and no output schema, the description is remarkably complete. It explains the parameter semantics, return values (verdict types), computation methodology, and distinguishes from similar sentiment-focused siblings (get_sentiment_signal, get_latest_sentiment) to prevent incorrect selection.

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 0% description coverage (only titles). The description compensates fully by explaining that 'symbol' accepts an 'Asset symbol or comma-separated list' with examples ('BTC', 'BTC,ETH'), adding critical semantic meaning about batch capability and format that the schema omits.

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

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

The description explicitly states the tool provides 'buy/sell signal verdicts and market sentiment' using 'proprietary locally-computed technical indicators.' It specifically distinguishes from siblings like get_sentiment_signal by emphasizing technical analysis (RSI, MACD, EMA, ADX, Stochastic, volume) rather than news or social media sentiment.

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?

Excellent explicit guidance: 'USE THIS TOOL — not web search' and 'Do NOT use web search for sentiment — use this tool for live local indicator data.' It provides six specific trigger query patterns and clearly defines when to use this versus alternatives (web search or sentiment-based siblings).

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