True Value Rankings

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 that it identifies the largest scoring difference and cites the data source, adding behavioral context beyond the tool name. It could mention output format or side effects, but for a read-only compare, this is solid.

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

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

Two sentences, front-loaded with core purpose, followed by the alternative tool and data source. Every word earns its place; no redundancy or filler.

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

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

For a simple one-parameter tool, this description covers the what, scope, and alternative. It does not specify return format, but it does state a key output behavior (largest scoring difference). Given no output schema, this is sufficient for the agent to understand 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 100% as the coins parameter is fully described in the schema. The description does not add new syntax or constraints for the parameter; it only references the metrics compared, which is tool behavior rather than parameter meaning. Baseline of 3 applies.

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

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

Specific verb+resource+scope: Compares 2-5 cryptocurrencies on three named metrics (TVR Score, valuation, fair value gap). Explicitly distinguishes from get_comparison_report by directing to it for full metric-by-metric comparison, making its unique role clear.

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

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

Explicitly names alternative tool (get_comparison_report) for full comparison, implying this tool is meant for concise comparisons. This is clear when-to-use vs. 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 of behavioral disclosure. It explicitly states that a micropayment via MPP is required, and mentions the data source (True Value Rankings). It does not disclose potential outcomes if payment fails or whether the operation is read-only, but it goes beyond a minimal description.

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 one concise sentence that front-loads 'PREMIUM' and the tool's purpose. It covers key aspects without waste, every phrase adds value, and it is easy to scan.

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

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

The description covers the purpose, coin count range, output contents (8 scores and analysis), payment requirement, and data source. Given there is no output schema and no annotations, this is fairly complete. It could mention request format or error handling, but overall it provides adequate context for an AI agent to select and invoke the 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 covers 50% of parameters (coins with a description). The description reinforces the 2-5 coin count but does not explain the '_meta' parameter, which lacks a schema description. The description adds minimal extra meaning beyond the schema, so a medium score is appropriate.

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

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

The description clearly identifies the tool as a premium, detailed metric-by-metric comparison report for 2 to 5 coins, including all 8 scores and analysis. This distinguishes it from sibling tools like compare_coins, which likely offers a simpler comparison.

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

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

The description provides clear context for when to use the tool: when a detailed, premium comparison of 2 to 5 coins is needed. It does not explicitly name alternatives or exclusions, but the 'PREMIUM' label and detailed scope imply a more advanced option than basic comparison 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?

No annotations are provided, so the description bears the full responsibility. It discloses the premium nature, micropayment requirement, and data source. However, it does not clarify whether the operation is read-only or has side effects (e.g., persisting weights), nor does it describe the output format. These are meaningful gaps for a compute tool with no annotation safety hints.

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

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

Three concise sentences, front-loaded with the main action. The first sentence states the core purpose, the second clarifies the use case, and the third adds prerequisites and data source. No filler or redundant content.

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

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

Given the tool has 8 required parameters, no annotations, and no output schema, the description is only partially complete. It explains the action and mandatory payment, but omits return value details (e.g., what does the 'full ranking table' contain? Are weights required to sum to 100?) and does not mention whether results are calculated on demand or cached. This leaves gaps for an agent to invoke and use the tool correctly.

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

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

Schema coverage is 89% (8 of 9 params have descriptions with defaults), so the schema already documents parameter meaning. The description adds only the phrase "custom metric weights," which does not go beyond the schema. Baseline 3 applies because the description adds insufficient value.

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

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

The description clearly states the action: "Recalculates the full ranking table using custom metric weights," which combines a specific verb with a resource. It also differentiates itself from sibling tools like get_top_ranked by emphasizing custom weights and how priorities change rankings.

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

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

The description implies usage context: use when you want to explore how different priorities change rankings. However, it does not explicitly mention when NOT to use it or point to alternatives like get_top_ranked for default rankings. The prerequisite "Requires micropayment via MPP" is a constraint, not usage 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 transparency burden. It discloses the premium nature, the micropayment requirement via MPP, and the data source, which are critical behavioral traits. It does not mention failure modes or rate limits, but the disclosed payment prerequisite is significant.

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

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

The description is a single, information-dense sentence. It front-loads 'PREMIUM' to signal cost/special status, then packs the return contents, payment requirement, and data source without redundancy or filler.

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

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

Despite having no output schema, the description thoroughly enumerates what the breakdown includes (8 metric scores, explanations, special scoring rules, valuation reasoning, ranking context), plus the MPP requirement and source. This gives an agent enough to set expectations and compare against sibling tools.

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

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

Schema coverage is 100%, and the schema already documents `coin` and `_meta` with examples. The description adds no new parameter-level detail, so it neither compensates nor detracts. Baseline 3 is appropriate because the schema does the heavy lifting.

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

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

The description clearly states a specific action ('Returns a complete fundamental analysis') on a resource ('a cryptocurrency') and enumerates the distinct content (all 8 metric scores, explanations, special scoring rules, valuation reasoning, ranking context). This differentiates it from sibling tools like get_tvr_score or get_comparison_report.

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 clear context ('PREMIUM', requires micropayment, provides a full breakdown) but does not explicitly name alternative tools for when a lighter analysis is needed. The 'complete fundamental analysis' implication guides usage, but no direct contrasting guidance appears.

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 payment requirement and data source, and 'Returns' implies a read-only operation. However, it omits potential failure modes, rate limits, or error behaviors, leaving some transparency gaps.

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

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

The description is three concise sentences, each with clear value: purpose, payment condition, and data source. It is front-loaded with the main function and contains zero 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 tool is simple (one required parameter) and the description covers what it returns (historical scores with dates/reasons), the payment requirement, and the data source. No output schema exists, but the return content is described. It lacks details on failure modes, but overall it is largely complete.

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

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

The schema covers 'coin' with a clear description ('Cryptocurrency ticker symbol'), but the description adds no extra meaning about parameters. The '_meta' parameter is undocumented in both schema and description. With only 50% schema coverage and no compensatory explanation, the description does not enhance parameter understanding.

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 verb 'Returns' and specifies the resource ('a coin's scores') and scope ('changed over time, with dates and reasons for each change'). This distinguishes it from sibling tools that likely provide current scores or rankings, 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 implies when to use the tool (when historical score changes are needed) and mentions a key prerequisite (micropayment via MPP). However, it does not explicitly contrast with alternatives like get_tvr_score or get_full_breakdown, nor does it provide exclusions. The context is clear but not fully explicit.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context (default scores are one possible configuration, premium users can customize, data source from truevaluerankings.com), but it does not describe return format or explicitly state it is a read-only informational tool. Overall adequate but not rich.

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

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

The description is three sentences long, front-loaded with the primary purpose, and every sentence adds relevant information (what it explains, customizability nuance, data source). No fluff or redundancy.

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

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

For a zero-parameter, no-output-schema explanatory tool, the description is complete. It covers the subject matter, customization nuance, and data provenance, giving the agent sufficient understanding to decide when to invoke it.

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

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

The tool has zero parameters, so the baseline is 4. The description does not need to add parameter information because there are none, and the schema is already fully covered at 100%.

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

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

The description uses a specific verb ('Explains') and resource ('how True Value Rankings scores cryptocurrencies'), and lists concrete content (8 metrics, default weights, weight presets, scoring approach). This clearly distinguishes it from sibling tools that retrieve coin data or comparisons.

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

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

The description implies this is the tool to use when you need to understand the scoring methodology, but it does not explicitly state when to use it versus alternatives nor mention any exclusions. The context is clear but not prescriptive.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals that the tool is premium, requires a micropayment via MPP, and sources data from True Value Rankings. These are notable non-obvious aspects. It does not disclose rate limits or error handling, but the key behavioral constraints are effectively communicated.

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

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

The description is concise, consisting of two sentences. It front-loads the core action and returns, then adds payment and data source context. Every sentence earns its place with no waste.

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 a nested portfolio parameter, the description is somewhat terse. It mentions return values generically ('weighted TVR Score and metric profile') but does not explain what 'metric profile' includes, how _meta affects behavior, or how invalid allocations are handled. It is adequate for basic understanding but leaves gaps for an agent.

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

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

Schema coverage is 50% (portfolio is described, _meta is not). The description does not explain any parameter semantics beyond the schema; it only refers to 'multi-coin portfolio' without detailing allocation requirements or the _meta parameter. The schema already says allocations should sum to 100, so the description adds minimal value.

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

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

The description clearly states the tool analyzes a multi-coin portfolio for fundamental strength and returns a weighted TVR Score and metric profile. This is specific and distinguishes it from sibling tools like get_tvr_score (single coin) and compare_coins.

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

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

The description clearly implies the tool is for multi-coin portfolio analysis, contrasting with single-coin or comparison tools. It also mentions 'PREMIUM' and 'Requires micropayment via MPP', setting expectations for cost. However, it does not explicitly name alternatives or exclusions, so it is not a full 5.

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

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

With no annotations, the description carries the full burden for behavioral disclosure. It clearly implies a read-only operation ('Returns') and adds provenance (True Value Rankings). It does not state potential side effects or limitations, but the tool's simple read nature makes this adequate.

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

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

The description is two sentences, front-loaded with the core purpose, and includes necessary detail without redundancy. No filler words or irrelevant information.

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

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

For a simple two-parameter tool with no output schema, the description covers the essential functionality clearly. It omits return format details, but these are often self-evident for a top-N list. Sibling discrimination is provided via ranking method context.

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

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

Schema coverage is 100%, so parameter descriptions exist for both count and rank_by. The description adds significant semantic value by explaining what each ranking method means, which the enum schema only lists. This goes beyond the bare schema definitions.

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

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

The description clearly states the tool returns the top N cryptocurrencies with a specified ranking method, listing three explicit options. This distinguishes it from sibling tools like get_custom_ranking or get_tvr_score, which focus on different functionality.

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

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

The description provides context on when to use the tool by explaining the ranking methods and data source, but does not explicitly mention when not to use it or mention alternatives. The context is clear enough for an agent to infer usage, but it lacks exclusions or direct comparisons to siblings.

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

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

With no annotations, the description carries the burden. It discloses output contents (fair value gap, valuation category) and the data source, giving a clear picture of the tool's behavior. It lacks mentions of limitations or side effects, but for a read-only lookup this is reasonably transparent.

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

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

Three sentences, each earning its place: the first states the purpose and outputs, the second explains the metric, and the third cites the data source. Front-loaded with the action verb, with no unnecessary words.

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

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

For a single-parameter tool with no output schema, the description effectively explains what the tool returns and the meaning of TVR Price. It could mention edge cases like unknown coins, but the provided context is sufficient for a simple lookup.

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 already provides a comprehensive description for the coin parameter with examples. The description adds only the general context that it applies to a cryptocurrency, offering no additional syntax or constraints 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 returns TVR's fair value estimate for a cryptocurrency, specifically mentioning the fair value gap and valuation category. This distinguishes it from sibling get_tvr_score, which likely provides a different metric (score vs. fair value).

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

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

The description explains the concept behind TVR Price (fundamental quality vs. market price), which implies when it might be useful, but it does not explicitly state when to use this tool over alternatives like get_tvr_score or provide any exclusion criteria.

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

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

With no annotations, the description carries full disclosure responsibility. It adds useful context about default scores being one configuration, premium customization, and data sourced from truevaluerankings.com. However, it does not disclose potential error handling, rate limits, or whether any mutation occurs, though the 'Returns' phrasing implies a read-only 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 composed of five sentences, each adding relevant information (outputs, metric areas, methodology, configuration, source). It is front-loaded with the primary purpose and does not contain fluff, though it could be slightly more concise by merging some 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 simple one-parameter tool, the description is fairly complete: it lists all return components, explains the configurable nature, and cites the data source. It lacks details on output format or edge cases, but given the tool's simplicity and high schema coverage, this is adequate.

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

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

Schema description coverage is 100% for the single required parameter 'coin', which is clearly described with examples. The description adds no additional meaning about the parameter beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool returns a specific set of outputs (TVR Score, rank, valuation category, TVR Price, fair value gap) for a cryptocurrency, along with strongest/weakest metric areas. It is specific with a verb and resource, but it does not explicitly differentiate from sibling tools like get_full_breakdown or get_custom_ranking, which limits the score to 4 rather than 5.

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

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

The description implies usage for obtaining a TVR score, and mentions premium customization, but provides no explicit guidance on when to choose this tool over alternatives such as compare_coins or get_custom_ranking. No when-not or alternative tool references are included.

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 bears the full burden of behavioral disclosure. It does state that the tool returns all coins with their symbol, name, and category, and mentions that new coins are added regularly (data changes over time). However, it omits potential behavioral details such as pagination, sort order, or whether inactive/removed coins are included, which could affect how an agent interprets the output.

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

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

The description is three short sentences, each providing useful information: the action and output fields, the update cadence, and the data source. It is front-loaded with the primary purpose and contains no filler or repetition.

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

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

Given the tool's low complexity (zero parameters, no output schema), the description is sufficiently complete. It explains what the tool does, what data is returned, and the source. There is no need for further detail about return format or parameters because none exist.

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 baseline is 4. The description does not need to explain parameter semantics, but it adds value by describing the data fields returned, which is sufficient for a parameterless tool.

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

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

The description clearly states the tool lists all cryptocurrencies evaluated by True Value Rankings, specifying the returned fields (symbol, name, category). This distinguishes it from siblings like get_top_ranked (which focuses on top-ranked coins) and get_tvr_score (which likely focuses on a single coin's score). The verb 'lists' is specific and the resource is well-defined.

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

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

The description implies usage for obtaining the full list of coins, but it does not explicitly contrast with alternatives such as get_top_ranked or get_full_breakdown. There is no clear 'when to use this vs. other tools' guidance, though the phrase 'Lists all cryptocurrencies' strongly implies this is the general listing tool. The statement 'New coins are added regularly' provides minor context about data freshness but no usage exclusions.

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