FabTally Guard

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

With no annotations provided, the description carries the full burden. It discloses that this is a PAID operation ($0.003, x402, USDC on Base), lists the return data, and explains the 402 challenge behavior. It also notes that without payment you get a challenge and with x_payment you get the real result—transparent about the payment gating.

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 dense but every sentence earns its place: price, purpose, return data, args, and payment flow. It is front-loaded with the most critical info (PAID) and avoids filler. The list of return signals is comprehensive without being verbose.

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

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

Given the absence of an output schema, the description compensates by fully enumerating the returned signals. It also covers the payment intricacies and the optional version parameter. For a moderately complex tool, this is complete and actionable.

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

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

The input schema already describes all four parameters with 100% coverage, including the enum for ecosystem and the default for version. The description enumerates the required args but adds little beyond the schema, so baseline 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's purpose with a specific verb: 'Vet a software package BEFORE installing it.' It enumerates the exact signals returned (existence, CVEs, deprecation, typosquat, license, age/download) and distinguishes it from siblings (x402_preflight, json_repair, url_read) which are unrelated.

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 gives a clear usage context: use before installing a package, and it explains how to handle the payment flow (omit x_payment to get a challenge, pass it to settle). It does not explicitly name alternatives or exclusions, but the tool is unique among siblings, so clear context suffices.

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 payment requirement ($0.002 via x402), the sequence of repair operations, return payloads (repaired object or errors), and the unpaid challenge response. This is comprehensive for a tool of this complexity.

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?

Compact yet information-dense. The paid warning is front-loaded, followed by a precise list of transformations and return behavior. Every clause adds value 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?

Covers payment flow, transformation steps, outputs, and parameter purposes. Despite no output schema, the return types are explicitly described. The description fully equips an agent to invoke and interpret 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 already provides detailed descriptions for all three parameters (100% coverage). The description merely restates json/schema args, adding no new semantic value over the schema. 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?

Clearly states the tool repairs near-miss LLM JSON to a JSON-Schema-conformant object, with explicit operations (strip fences, fix quotes, coerce types). It is distinct from sibling tools (deps_check, x402_preflight, url_read) which serve different purposes.

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

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

Provides clear context: use for near-miss LLM JSON that needs coercion to a schema. Mentions the payment prerequisite and fallback behavior. Does not explicitly name alternatives or exclusions, but context is unambiguous given 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 full burden. It discloses the x402 payment flow, robots.txt respect, the no-login/no-paywall policy, and the 'nothing stored' privacy guarantee. It also explains the challenge/billing behavior. However, it omits error handling details and what happens for non-HTML content.

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 moderately concise and front-loaded with the key paid aspect. It uses a few sentences but avoids fluff. The explanation of the x402 payment flow adds necessary complexity, but each sentence contributes meaning. Could be slightly tighter, but it's well organized.

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 paid fetch tool without an output schema, the description covers the essential return types (Markdown + token estimate, x402 challenge), the payment workflow, and constraints. It lacks information about failure modes (e.g., robots.txt blocks, network errors) and the exact challenge format, but overall it is quite complete for an agent to use successfully.

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%, so the baseline is 3. The description adds minimal value beyond the schema: it summarizes 'Args: url' and echoes the x_payment settlement flow, but does not provide additional semantic details not already in 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 verb ('Fetch a PUBLIC url'), the resource (public web page), and the output ('clean Markdown + a token estimate'). It also notes the stripping of boilerplate, making it distinct from siblings like deps_check or json_repair.

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 explicit context for when to use: for RAG ingestion of public pages. It also states exclusions: 'public pages only, no login/paywall bypass'. It doesn't explicitly mention alternative tools, but the sibling set is unrelated, so this is sufficient 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 provided, the description carries the burden of transparency. It discloses that the tool is paid ($0.002), what actions it performs (multiple verification checks), the return value (pass/fail + reasons + parsed price), and the effect of the x_payment header. It does not mention rate limits or authentication beyond the payment, but key behavioral traits 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?

The description is dense but reasonably concise, conveying paid status, purpose, verification checks, return value, and argument behavior in a single paragraph. It uses punctuation effectively to separate ideas, though the initial '(paid $0.002)' could be interpreted as clutter; overall it 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?

No output schema exists, so the description clarifies that the tool 'Returns pass/fail + per-check reasons + the parsed price'. It also explains the x_payment flow, giving enough context for an agent to invoke the tool correctly in both modes. Given the tool's moderate complexity, the description is reasonably 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?

Input schema describes both parameters with 100% coverage. The description adds context on how the parameters are used: 'url' is the endpoint to verify, and 'x_payment' is optional to 'settle the call' and 'return the real result instead of a 402 challenge'. This reinforces the schema and clarifies the conditional semantics of the optional parameter.

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 opens with 'Verify another x402 endpoint BEFORE paying it', which clearly identifies the verb (verify) and resource (x402 endpoint). It then enumerates specific checks (challenge validity, well-known config match, TLS, host reachability, OpenAPI), making the tool's purpose unambiguous and distinct from the sibling 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 states when to use the tool: before paying an x402 endpoint, and explains two usage modes (omit x_payment to receive the challenge, supply x_payment to settle). It does not explicitly contrast with alternatives, but the sibling tools are unrelated, and the 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.