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

With no annotations, the description carries the full burden. It discloses all return fields (status, repo, reportUrl, findingsCount), mentions the 404 error case, and provides realistic timeframes. The mention of 'Al-Mizaan-validated' adds context. No contradictions.

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?

Well-structured: main action first, then timing, return info, and error handling. Each sentence adds value, though slightly verbose for the content. No 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?

For a single-parameter polling tool with no output schema, the description provides all necessary information: what it returns, when to call, and error behavior. Nothing essential is missing.

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 schema fully documents job_id. The description repeats that it comes from scan_contract but adds no new format or validation details. 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 states a specific verb ('Poll') and resource ('job_id returned by scan_contract'), and clearly distinguishes from siblings by referencing scan_contract as the source of the job_id. It leaves no ambiguity about what this tool does.

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 explicit timing guidance ('every minute or two'), expected durations (25 min to 24h), and warns against polling faster. It also advises on 404 handling, telling the agent to verify the job_id rather than retry blindly. This is complete 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It does well by explaining data provenance, exact reconciliation limits, and that no estimated or unverifiable figures are included, plus noting the tool is free. It does not cover return format, but for a read-only list tool this is sufficient context.

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 compact and front-loaded with the primary action. The following sentences earn their place by clarifying data reliability, use case, and cost. The 'FREE' note is slightly tangential but still useful for agent decision-making.

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 low-complexity tool with one optional parameter and no output schema, the description covers what the tool returns, the scope of the data, a use case, and pricing. It could specify the exact output shape, but the absence of an output schema and the simplicity of the tool make this a minor gap.

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%, with a single optional enum parameter that already documents 'protocol_type' and its default. The description adds only a passing implication of filtering by protocol type, so it does not meaningfully supplement what the schema already 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 uses a specific verb ('Returns') and a clear resource ('historical vulnerability patterns and acceptance rates from Sherlock audit-competition findings'). The 'historical' qualifier and mention of statistical patterns clearly distinguish this analytics tool from the sibling scan_contract and get_scan_report 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 a clear use case: 'useful for understanding what types of bugs are most likely to be valid in a given protocol type.' It does not explicitly list exclusions or alternatives, but the analytical framing and sibling tool names make the intended selection obvious.

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 — and it delivers comprehensively: disclosed cost ($5 USDC on Base via x402), guaranteed 24h delivery with a ~25min sample, async job semantics (returns job_id), the payment retry flow, and the false-positive filtering behavior of the framework. Nothing about side effects or requirements is hidden. No contradiction with annotations (none exist).

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 actionable purpose is front-loaded, but the description carries meaningful extraneous weight — the Slither 100%-FP benchmark stat and the 27,681/1,032 corpus reconciliation figures are trust-building context rather than invocation-critical information. It's dense and well-organized, but not every sentence earns its place for the core task of calling the 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?

For a payment-gated, asynchronous tool with no output schema and no annotations, the description covers every invocation concern: cost and payment rail, expected delivery time, return value (job_id), the polling sibling, and the retry-after-payment branch. The get_scan_report sibling fills the result-format gap. Nothing an agent needs to invoke correctly is missing.

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 schema already documents both parameters (repo_url with an example, protocol_type with its enum and default). The description adds a repo URL example and global workflow context but no per-parameter semantics beyond what the schema gives, so the baseline 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 opens with a specific verb+resource: 'Submit a public GitHub repository for an automated smart contract security analysis.' It names the 7-gate framework and references both siblings (get_scan_report for polling, list_vulnerability_patterns for pattern data), so an agent can distinguish this from alternatives without reopening schemas.

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 explicitly instructs 'Use get_scan_report to poll for results' and lays out the end-to-end workflow including the payment-and-retry path when payment_required is true. It references list_vulnerability_patterns to disambiguate acceptance-rate numbers. It falls short only by not explicitly stating conditions under which this tool should NOT be used.

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