Solana Delta

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

With no annotations, the description carries the full burden. It discloses that the tool requires payment (0.001 Base USDC via x402 v2), is a two-step process (unpaid call for requirements, then paid retry), and lists the data returned. It does not detail authentication or exact response format, but the payment and retry flow are important behavioral traits that are disclosed.

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 tool's output and price, followed by a concise usage flow. Every sentence earns its place with 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?

The description conveys the tool's purpose, payment requirement, and call sequence without an output schema. It lists the specific data products, which adequately sets expectations. However, it doesn't describe the machine-readable requirements format or provide an example, which would make it more complete for an agent making an unpriced 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?

The input schema has zero parameters, so the baseline for parameter semantics is 4. The description adds no parameter-level detail because none are needed; it instead explains the payment retry flow, which is the relevant 'input' for this 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 what the tool does: it sells fee quantiles, pressure trend, compute-unit bids, cost estimates, and a submit-or-defer decision. This specific verb ('Buy') and the detailed list of outputs distinguish it from sibling tools like get_fee_intelligence_preview, which likely provides a preview rather than the full paid result.

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 the agent to first call without payment to receive machine-readable requirements, then retry using @x402/mcp. This is concrete usage guidance. It doesn't explicitly name alternatives or when-not-to-use, but the payment flow and the paid nature imply the preview sibling is for free exploration, so usage context is clear.

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

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

With no annotations, the description carries the burden. It discloses that the tool is free, provides coarse metrics (freshness, congestion, direction), and is aimed at pre-screening. The verb 'Check' implies read-only behavior, but it doesn't explicitly mention output format or operational limits. Still, it adds meaningful context beyond the name.

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 with high information density. The opening 'FREE.' immediately communicates a key distinction, and every word serves a 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?

For a simple parameterless preview tool, the description fully explains its purpose, cost, and what it checks. It complements the sibling tools without needing to detail return types or side effects.

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?

There are zero parameters, so the description doesn't need to explain parameter meaning. The empty schema is fully covered, and the description conveys what the tool evaluates, 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 'Check freshness, coarse congestion, and fee-pressure direction' with a specific verb and resource. It also distinguishes itself from detailed transaction bids, implying a preview role compared to a more detailed tool like get_fee_intelligence.

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: use this free preview before deciding whether detailed transaction bids are worth the cost. However, it doesn't explicitly name the alternative tool or state when not to use it, so it falls short of full explicitness.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool is 'FREE' and that it returns comparison data. However, it does not explicitly state whether it is read-only, mention rate limits, or clarify data source freshness. The word 'Inspect' in the title implies non-mutating behavior, but this is not made explicit in the description. This adds some context but lacks full 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 a single, concise sentence that front-loads the key benefit ('FREE.') and then lists exactly what is compared. Every phrase adds value: 'prices, freshness, examples, and the economic decision.' There is no waste, and the structure is clear and immediately informative.

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 no annotations, the description effectively covers the main value proposition and the content of the returned data. It lacks explicit details about return format or how the 'economic decision' is computed, but for a catalog/list tool, this is adequately complete. It earns a 4 due to these minor gaps.

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 adds no parameter-specific information, as there are none. It does explain the kind of data returned, which indirectly clarifies why no parameters are needed. The schema itself is empty, so the description's coverage of the tool's behavior is sufficient for parameter semantics.

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

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

The description clearly states the tool's function: 'Compare prices, freshness, examples, and the economic decision enabled by each paid Solana signal before committing funds.' It specifies a verb (Compare), a resource (paid Solana signals), and scope (catalog). The title 'Inspect Solana Delta products' reinforces the purpose and distinguishes it from sibling tools like fee intelligence or network state.

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 on when to use the tool: 'before committing funds.' It implies this is the initial step for evaluating paid signals. However, it does not explicitly mention alternatives or when not to use it, though sibling tool names suggest specialized alternatives. This earns a 4 (clear context, no 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 carries the disclosure burden and does well: it reveals this is a paid tool (price and payment protocol), explains that unpaid calls return machine-readable requirements, and clarifies that it only surfaces meaningful changes. It does not cover error behavior or response structure, but what's provided goes beyond the tool name and 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?

Two concise sentences front-load the purpose and include essential payment/retry information without tangents. Each sentence contributes distinct 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?

The description explains why and how to pay, but does not specify the response format, how to obtain the next cursor, or any error/edge cases. Given the simple one-parameter schema and lack of output schema, some of these gaps are acceptable, but return behavior is under-specified.

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 100% coverage with a clear description of the cursor parameter, so the baseline applies. The tool description adds no additional semantic meaning to the cursor beyond the schema.

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

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

Description clearly identifies the tool's function: retrieving meaningful state changes after a cursor, replacing repeated RPC polling. It differentiates this from sibling state/pulse tools by emphasizing cursor-based change feeds and automation use case. The leading word 'Buy' is slightly unusual but context makes the 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?

It states when to use: for automation wanting to avoid repeated polling and be woken only on network changes. Payment flow is explicitly described with retry instructions. It doesn't mention alternatives or exclusions, but the use case is clear enough.

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

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

No annotations are provided, so the description carries full weight. It discloses that data is cached, that payment is required via x402, and what to expect when calling without payment (machine-readable requirements). This adds meaningful behavioral context beyond the raw 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?

Two compact sentences deliver purpose, use cases, pricing, and payment flow. No fluff, no repetition. Information is front-loaded with the core purpose first.

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 conveys what the tool does, why to use it, and how to invoke it (including payment flow). It does not detail the exact response shape, but the phrase 'health and congestion state' gives a reasonable expectation. Overall, it is sufficiently complete for selection and invocation.

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

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

The tool has zero parameters, so the description need not explain parameter meaning. Schema coverage is trivially complete. A baseline of 4 is appropriate given the absence of 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 a specific resource ('complete cached Solana health and congestion state') and a verb ('Buy') with intended uses (routing, risk controls, execution timing). It is clear on what the tool returns, though it does not explicitly contrast itself against sibling tools beyond the domain-specific resource.

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 use context ('for routing, risk controls, and execution timing') and practical payment/retry instructions. It does not mention when to prefer this over sibling tools, but the intended use cases are explicit enough to guide selection.

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 transparently reveals the payment requirement (0.001 Base USDC via x402 v2), the fact that the data is cached, and the fallback behavior of calling without payment. It could add error handling or rate limits, but the key behaviors are covered.

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: the first states purpose and output, the second covers pricing and retry instructions. Every word earns its place, with no fluff 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?

For a zero-param, no-output-schema tool, this description is complete. It covers what data is returned, the cost, how to pay, and what to do if unpurchased. It even hints at the machine-readable requirements 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?

There are zero parameters, so no parameter explanations are needed. The baseline for 0-param tools is 4, and the description appropriately focuses on outputs rather than inputs.

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

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

The description clearly states the tool's function: to buy a fresh cached snapshot of slot, throughput, fee, and health. It uses a specific verb ('Buy') and lists the exact data provided, distinguishing it from sibling tools like get_fee_intelligence or get_solana_network_state.

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 explains when to use this tool ('to decide whether to proceed, delay, or reroute a Solana-dependent operation'), giving clear decision-making context. It does not explicitly name alternatives or exclusions, but the use case is well-defined.

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