Pyfile LLM & Data API

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

No annotations are present, so the description carries the full burden. It discloses the output units (wei and gwei) and that the value is 'current,' implying a live read. However, it does not describe whether this is a read-only operation, how the value is obtained, or any freshness/error behavior. For a simple query tool this is acceptable, though slightly sparse.

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?

One short, well-structured sentence that conveys the resource, the value, and the units. Every word contributes meaning, and there is no filler 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 simple, parameterless read tool, the description is largely sufficient: it names the network, the gas price, and both output units. Since there is no output schema, explicitly stating that the result includes both wei and gwei values is helpful. A minor gap is that it doesn't specify the result shape (e.g., separate fields vs. a formatted string), but this is unlikely to confuse 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 tool has zero parameters and the schema is empty (100% coverage), so there is nothing to clarify. Per the baseline for 0-parameter tools, the description adds sufficient meaning by stating what is returned.

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 states a specific query ('gas price') on a specific resource ('Base L2') and specifies the units returned (wei and gwei). It clearly differentiates from the sibling tools, which are about chat, crypto price, DNS, and Wikipedia – none of which overlap with an L2 gas price lookup.

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 the exact use case: retrieving the current Base L2 gas price. It doesn't explicitly name alternatives or exclusions, but the tool is so specialized and siblings are unrelated that the context is fully clear. No additional exclusions are needed.

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 behavioral disclosure burden. It does state the output type ('text completion'), which is a useful behavioral trait. However, it omits other behavioral context such as latency, cost, determinism, or that the model parameter affects results. The description is not misleading, but it is minimal.

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 short sentences with no filler. The core action is front-loaded ('Ask an LLM a question') and the return type follows immediately. Every word earns its place.

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

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

For a simple two-parameter tool with no output schema, the description covers the basic contract. However, with no annotations and no guidance on model selection or prompt expectations, the agent has to infer some behavior. It is adequate but not rich.

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 50%: only 'prompt' is described ('The user prompt'), while 'model' has an enum but no explanation. The description itself adds no parameter semantics—it does not clarify what 'model' means, what the options represent, or what the default does. Since coverage is low, the description needed to compensate but did not.

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 ('Ask') with a clear resource ('an LLM'), and explicitly states the result ('Returns a text completion'). This clearly distinguishes it from the sibling tools, which are all domain-specific lookup utilities like crypto_price and dns_lookup.

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 guidance, exclusions, or alternative routing is provided. The sibling tool names imply these are non-LLM tools, and the phrasing 'Ask an LLM a question' gives an implicit usage context, but the description never states when this tool should be chosen 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?

The description clearly frames the operation as a read-only price lookup and identifies CoinGecko as the data source, so an agent can infer it is safe and non-mutating. It does not disclose limitations such as invalid-ID behavior, rate limits, or response format, and no annotations are available to cover those 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 a single front-loaded clause that leads with the result ('current USD prices') and then adds the only necessary input-format detail. Every part earns its place, and there is no repetition 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 tool with one optional parameter, a default value, no nested objects, and no output schema, the description plus schema provides enough to invoke the tool correctly. The main omission is explicit output-shape detail, but 'current USD prices' adequately signals the expected return for this straightforward 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 fully describes the single parameter, including type, default, and comma-separated CoinGecko ID format, so the baseline is 3. The description only reinforces that one or more IDs are accepted without adding meaningful new semantics beyond the schema.

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

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

The description names the resource (coins) and the value delivered (current USD prices), and the CoinGecko-id note makes the input domain concrete. It lacks an explicit verb such as 'get' or 'return' and does not explicitly contrast with sibling tools, but the intent is 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 use for current USD cryptocurrency price lookups and makes the input format clear. It provides no explicit when-to-use/when-not-to-use guidance or alternatives, though the sibling tools are distinct enough that mis-selection is unlikely.

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 clearly communicates a read-only network lookup behavior, but does not mention possible failures, timeouts, or what the returned records look like. This is acceptable for a simple lookup but not richly 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?

A single sentence with no filler, front-loading the action and resource. It is appropriately sized for a two-parameter tool.

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

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

The tool is simple and the schema covers both parameters, but the description omits CNAME, does not mention the default type of A, and provides no information about return values or error behavior. Adequate for straightforward usage, but with clear 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?

Schema coverage is 50%, and the description partially compensates by listing acceptable record types. However, it omits CNAME even though the schema permits it, and it adds no explanation of how the name parameter is used beyond what the schema already states.

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 states a specific verb ('Resolve') and resource ('a DNS record'), and lists the main record types. It does not explicitly differentiate from sibling tools, but the sibling tools are clearly unrelated domains, so there is no real 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?

Usage is implied by the verb and resource: use when a DNS record for a domain is needed. There is no explicit guidance on when to prefer this tool over alternatives, but no competing lookup sibling exists.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the output is a short summary, but does not describe missing-article handling, redirects, language behavior, or failure modes.

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 with no filler. Both the purpose and the required input are immediately clear.

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 lookup tool, the description is mostly adequate, but the absence of an output schema or any mention of response format and error behavior leaves moderate 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 schema covers 100% of parameters and describes 'title' with an example, so the baseline is 3. The description adds no further meaning beyond the parameter name.

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 resource (Wikipedia article), the input (title), and the output (a short summary). It distinguishes the tool from unrelated siblings, though it lacks an explicit verb like 'retrieves' or 'returns'.

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 conveys when to use the tool (when a Wikipedia article summary is needed by title), but there is no explicit guidance about alternatives, edge cases, or when not to use it.

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