Stratalize Crypto and DeFi Intelligence

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention rate limits, data freshness, authentication needs, or any side effects. The description only states what data is returned, not how the tool behaves or any 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 a single sentence of about 25 words, front-loading the core purpose 'Live TVL by blockchain'. It is free of unnecessary words and efficiently communicates the tool's scope and key outputs.

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 high-level output (chains and metrics) but lacks context on parameters and data sources. Given the complexity of a tool that aggregates TVL from many chains, and that there is no output schema, the description should provide more detail on what 'rankings' and 'change' mean, and how to use the parameters effectively.

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%, meaning the input schema has no descriptions for its two parameters (limit, sort_by). The description does not explain these parameters or how they affect results. It adds no meaning beyond what the schema provides, which is minimal. The listing of metrics in the description does not directly relate to the parameters.

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

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

The description clearly states the tool provides 'Live TVL by blockchain' and lists specific chains and metrics (rankings, 1D/7D change, protocol counts, Ethereum dominance, Base vs ETH TVL). It distinguishes from siblings by focusing on blockchain TVL, which is unique among the many benchmark 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?

No explicit when-to-use or when-not-to-use instructions. The description implies it is for retrieving current TVL data across multiple chains, but does not mention alternatives or exclusions. Among siblings, it is clearly the tool for blockchain TVL benchmarks, but guidance is only implied.

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

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

With no annotations, the description must fully disclose behavior. It lists outputs but omits details like default period, data freshness, side effects, or authentication needs. The inconsistency between '30-day' and the period parameter further reduces transparency.

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

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

The description is very concise—three sentences with no fluff. The core functionality is front-loaded in the first sentence, and each sentence adds value (outputs, source, use case).

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 one parameter and no output schema, the description provides some context (source, use case, output components) but lacks critical details like default behavior, return format, or data recency. It's adequate for a simple tool but not fully comprehensive.

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

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

The single parameter 'period' is not described in the description, and schema coverage is 0%. The description's claim of '30-day rolling' contradicts the parameter's enum options, adding confusion rather than clarity.

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 '30-day rolling correlation matrix for BTC, ETH, and SOL' with specific outputs like Pearson pairs and beta to BTC. However, the mention of '30-day' conflicts with the input schema allowing periods of 7d, 30d, and 90d, causing slight ambiguity.

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

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

The description says 'For crypto portfolio agents,' implying use case, but provides no guidance on when to use this tool versus alternatives or when not to use it. No sibling differentiation or context on limitations.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the data source (DeepDAO public data) but does not specify read-only nature, side effects, authentication needs, rate limits, or response behavior when parameters are omitted.

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

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

The description is concise with two sentences, front-loading the purpose and providing concrete example values. Every sentence adds value without 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 the tool has two optional parameters and no output schema, the description provides an overview of output content but lacks details on default sorting, filtering behavior, response format, and number of results. It covers the high-level content but leaves gaps in usage and output expectations.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'top DAOs by treasury size, stablecoin percentage' which maps to the sort_by enum values, but fails to explain the min_treasury_usd parameter. The addition of example median values adds some context but does not fully clarify parameter usage.

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

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

The description clearly states the tool provides DAO treasury benchmarks, listing specific metrics (treasury size, stablecoin percentage, runway, governance token concentration) and giving median example values. It distinguishes the tool from siblings by focusing on DAO-specific benchmarks.

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

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

No explicit when-to-use or when-not-to-use guidance is provided. With many sibling benchmark tools, the description does not help the agent decide between this and similar tools like get_chain_tvl_benchmark or get_defi_yield_benchmark.

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 covers critical behavioral aspects: data source (DeFiLlama Yields), output contents (APY bands, TVL, chain, pool ID), filter options, default scope (stablecoin-marked pools), and security (Ed25519-signed). It lacks mention of rate limits or data freshness, but overall disclosure 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 concise (three sentences) and front-loaded: first sentence states core function, second details optional filters, third adds important context (default, free, signed). Each sentence contributes useful information without redundancy.

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

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

The description adequately explains data content and filtering, but lacks details on the output format (e.g., expected JSON structure) since there is no output schema. It also does not mention update frequency or data latency. These gaps reduce completeness for an agent needing to process results.

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 100% coverage with parameter descriptions. The description adds valuable context by explaining that filters are substring matches and specifying default behavior when no filters are applied (stablecoin pools). This goes beyond mere repetition of the schema.

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

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

The description clearly states the tool retrieves DeFi lending and stable yield benchmarks from DeFiLlama Yields, specifying data includes APY percentiles, TVL, chain, and pool ID. It accurately describes the verb and resource. However, it does not differentiate from a sibling tool 'get_stablecoin_yield_benchmark', which may overlap in purpose.

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

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

The description provides no guidance on when to use this tool over alternatives like 'get_stablecoin_yield_benchmark'. It mentions optional filters and default behavior but does not address trade-offs or exclusions.

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

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

With no annotations, the description must disclose behavioral traits. It mentions live data, source (public chain RPCs), and no API key needed. However, it omits potential rate limits, caching, or output structure details. Sufficient for a simple read-only tool but not fully comprehensive.

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, front-loaded with the main purpose, followed by specific return details and source. Every sentence adds value with no redundancy or 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 low complexity (one optional parameter, no output schema), the description is fairly complete. It explains the tool's output categories and source. However, it lacks details on output format or data types, which would be beneficial 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?

The input schema has one optional enum parameter (chain) with values ethereum, base, solana, all. The description lists the chains but does not explicitly mention the parameter name or the 'all' option. Schema coverage is 0%, so the description partially compensates but could be more explicit.

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

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

The description clearly states the tool provides live gas price benchmarks for Ethereum, Base, and Solana, and lists specific data returned (Gwei, USD cost, congestion category, x402 context). It distinguishes itself from numerous sibling benchmark tools by focusing on gas prices.

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 gas benchmarks but does not explicitly state when to use or not use this tool, nor does it mention alternatives. The 'Zero API key required' note provides some context, but guidelines are not direct.

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

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

The description mentions data sources (Deribit, FRED) and target users, but does not disclose behavioral traits such as whether it is read-only, authentication needs, rate limits, or update frequency. Annotations are absent, so the description carries the full burden but falls short.

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

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

The description is concise with two sentences, front-loading the core purpose and listing specific outputs, data sources, and intended audience. No redundant or unnecessary information.

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

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

Given the complexity of the output (multiple metrics) and no output schema, the description provides a high-level list but lacks details on output structure, format, or whether data is historical/real-time. It is adequate for a basic understanding but not fully complete for an agent to anticipate the exact response.

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%. The description does not explain the 'asset' parameter beyond the implied BTC/ETH focus from the listed metrics. The 'all' enum option is not mentioned, leaving the agent to infer meaning from context. The description adds minimal value beyond the schema's enum values.

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 crypto options implied volatility benchmarks for BTC and ETH, listing specific metrics. It distinguishes itself from sibling tools (e.g., credit spread benchmarks) by its crypto options focus.

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 options traders and volatility agents, but does not explicitly state when to use this tool over alternatives or provide any when-not guidance. Lacks exclusions or comparison to other benchmark tools.

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

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

With no annotations, the description must carry the behavioral burden. It conveys a read-only data retrieval with specified data types and source (DeFiLlama + public data). No destructive actions or auth needs are mentioned, but it appropriately indicates it is a static data lookup. Could mention update frequency for added transparency.

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

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

Two sentences with front-loaded purpose, specific examples, data source, and a key market statistic. No fluff or redundancy; every sentence adds value.

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

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

Given the simple input (one optional parameter) and no output schema, the description covers purpose, data types, source, and total market size. It adequately conveys what the tool returns and the scope of benchmarks, making it complete for an agent to understand and invoke.

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

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

Schema description coverage is 0%, so the description must compensate. It partially explains the category parameter by listing examples (yields, TVL) and implying filtering, but it does not explicitly map enum values to these examples. The context helps but is not fully explicit.

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

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

The description clearly states it provides 'Real-world asset tokenization benchmarks' and lists specific examples (tokenized T-bill yields, RWA market TVL, YoY growth). It is specific about the resource and scope, distinguishing it from numerous sibling benchmark tools.

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

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

The description implies usage for RWA benchmarks but does not explicitly state when to use this tool vs alternatives, nor does it provide when-not or exclusionary guidance. Given the large sibling list, more explicit guidance would be beneficial.

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 sources (DeFiLlama + FRED), metrics (APY percentiles, spread), and TVL filter, but does not mention whether data is real-time or historical, or any rate limits or read-only nature.

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

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

Single sentence packed with all essential information: assets, protocols, metrics, filter, spread, sources. No wasted words, front-loaded with core purpose.

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

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

Given moderate complexity and no output schema, description covers assets, protocols, chains, metrics (p25/p50/p75), TVL filter, spread vs T-bill, and sources. Lacks specifics on TVL threshold, spread calculation, and output format, but provides a strong overview.

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

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

Schema coverage is 0%, so description must compensate. It mentions USDC/USDT/DAI which matches the enum, and implies 'all' via the list but does not explicitly explain it. Adds partial semantic value beyond the schema but not fully comprehensive.

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

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

Description clearly specifies verb (benchmark), resources (USDC/USDT/DAI supply APY), and context (Aave, Compound, Morpho, Spark by chain, percentiles, TVL filter, spread vs T-bill). It distinguishes from siblings like get_defi_yield_benchmark by being specific to stablecoins and lending.

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

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

Description implies usage for stablecoin yield comparisons but does not explicitly state when to use or when not to use, nor mention alternatives. Usage context is clear from the description but no exclusions or conditions.

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 fully carries the burden. It discloses that no authentication is required, indicates it returns a catalog with recommendations, and implies a read-only operation. Missing details like response format or rate limits are acceptable given the tool's simplicity.

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: first defines the tool's purpose and output, second gives usage direction and examples. No filler words, front-loaded with essential information.

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

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

Given the tool has no parameters, no output schema, and simple behavior, the description is complete. It covers what it returns, who it's for (role-based), and the prerequisite to call it first.

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

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

The input schema has zero parameters and 100% coverage. The description correctly notes nothing about parameters, as none exist. No compensation needed; baseline 4 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 the complete Stratalize tool catalog with role-based recommendations, including specific counts (107 public, 69 org) and mentions types like C-suite briefs. It distinguishes itself from sibling tools (all get_* tools) by being the discovery entry point.

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

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

The description explicitly instructs 'START HERE' and 'Call this first', providing clear when-to-use guidance for discovering tools. While it does not exclude other uses or state when not to use it, the context makes its purpose obvious.

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