ProofNexa B2B and Quant Risk Gate

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

With no annotations provided, the description must carry the full behavioral burden. It discloses that the tool inspects live DNS records and evaluates DMARC policy levels (none/quarantine/reject), but it does not state that the operation is read-only, whether it makes external DNS queries, what permissions are needed, or what the response contains.

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 efficient sentence that front-loads the core action and the audited resources. It contains no filler, though it could be slightly more structured by separating what is checked from when to use it.

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 audit tool, the description covers what gets inspected but not what is returned. Because there is no output schema and no annotations, the definition should ideally say more about the result format or the fact that it performs read-only DNS lookups.

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 single domains parameter is already well documented, including examples and the exact checks performed. The description does not add syntax, formatting, or validation details beyond what the schema provides, so the 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?

The description uses a specific verb and resource: 'Audit corporate domains' and then names the exact security checks (SPF, DMARC policy enforcement, MX infrastructure). It is clear what the tool does, but it does not distinguish itself from sibling tools such as audit_llmo_ai_readiness or verify_bulk_emails.

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 purpose implies when the tool is useful: auditing corporate domains for email spoofing and B2B cyber hygiene. However, there is no explicit when-to-use guidance, no exclusions, and no reference to sibling alternatives like verify_bulk_emails.

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 exist, so the description carries the full burden — and it does disclose the operation's substance: which endpoints and crawler directives are fetched, and that the result includes a 0-100 score and a deployable /llms.txt template. It stops short of stating read-only nature, rate limits, or handling of unreachable hosts.

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 front-loaded sentences with no filler; the check list is dense but every item is informative. Slightly long enumeration keeps it from a 5.

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, annotation-free audit tool with no output schema, the description covers what is inspected and what is returned, which is what an agent needs. Only edge-case behavior (invalid domain, fetch failures) is unaddressed.

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 domain parameter's format (root domain, no protocol) is already fully documented in the schema. The description adds nothing about the parameter, so the baseline 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?

States a specific verb (audit) plus the exact resource and scope: LLMO/GEO readiness for a domain across ChatGPT, Perplexity, and Claude. It also enumerates the concrete artifacts checked and the deliverables, so an agent can distinguish this from the email-security or broker-audit siblings at a glance.

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 when to use it (assessing AI-search crawlability of a domain) but gives no explicit when-not conditions and never names or routes to a sibling. Usage is inferable rather than spelled out.

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, and it does disclose the substantive analysis scope (three named risk categories) plus the fact that source-code scanning is static. However, it says nothing about whether the tool is read-only, what the verdict output looks like, whether findings are heuristic/confidence-scored, or how the code path and metrics path differ in behavior.

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 front-loaded sentence with no filler; each clause (EA source code, backtest metrics, three defect classes) adds distinct information. Slightly dense as a run-on enumeration, but nothing could be cut without losing scope.

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 six-parameter, zero-required, default-heavy tool with no output schema, the description should explain what happens when mql5Code is omitted (metrics-only audit) and roughly what the audit returns. Neither is covered, leaving the agent unsure how to interpret results or which input mode it is triggering.

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 all six parameters are already documented, establishing a baseline of 3. The description adds a modest value: it clarifies that the tool accepts either source code or backtest metrics, which hints that mql5Code is an alternate input path rather than a mandatory one, but it gives no thresholds or interpretation guidance for profitFactor/maxDrawdownPct.

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?

States a specific verb ('Audit') applied to a specific resource ('MetaTrader 5 (MQL5) Expert Advisor source code or strategy backtest metrics') and enumerates the exact defect classes it looks for: martingale patterns, missing stop-loss guards, overfitting probability. An agent can distinguish this from sibling 'simulate_grid_martingale_survival' (simulation vs. static audit) without opening the schema.

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 only implied: the two input paths (source code vs. backtest metrics) are visible in the description, but there is no explicit 'use this when...' framing, no prerequisites, and no named alternative among the six siblings. The agent must infer that code auditing versus metric auditing are the two supported modes.

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 full burden. It never says whether this is a pure read-only computation with no side effects, nor where swap rates/carry inputs come from (live broker feed vs. hardcoded assumptions), which materially affects trust in the result. Only the domain framing (Wednesday 3x rollover) adds behavioral 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?

A single dense sentence that front-loads the verb and enumerates the three outputs with no filler. Nothing in it is redundant.

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?

There is no output schema, yet the tool produces three distinct result sets (carry, Kelly size, lot size), and the description does not indicate the shape of the returned values or units. With 100% parameter coverage and sensible defaults the inputs are well covered, but the output side leaves the agent guessing.

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% and every parameter is documented in-schema with defaults, so the baseline is 3. The description adds no per-parameter meaning (e.g. how holdingDays interacts with the Wednesday triple-swap counting) 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?

States a specific verb (Calculate) and three concrete outputs: Wednesday 3x triple-swap rollover carry cost/yield, Half-Kelly position sizing, and fixed-fractional lot size, scoped to FX, XAUUSD and BTCUSD. The 'calculate_' verb and quant-domain framing distinguish it cleanly from the sibling audit_/compare_/simulate_ 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 guidance on when to use this versus alternatives, no prerequisites (e.g. that swap rates are assumed, or which broker's swap figures are implicit), and no exclusions. The agent must infer usage entirely from the output names.

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, and it does disclose the underlying assumptions that shape results (FTMO 5% daily / 10% total drawdown, USD cost basis, monthly effective spread + commission). However, it never states that this is a pure computation with no side effects, nor that the panel of 8 brokers is hardcoded, nor what the output looks like — all relevant to a safe, correct call.

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, front-loaded with what is compared and followed by the calculation methodology, with no filler. The parenthetical broker slug list (Pro/Zero/Raw, KIWAMI, Blade) is dense but does contribute scoping 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?

With no output schema, the description is expected to say what comes back, and it names the three computed outputs (spread+commission cost in USD, swap-free carry savings, 0% stop-out survival buffer). It is adequate to call the tool correctly, though it omits the fixed-universe caveat and the shape of the comparison result.

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 all four parameters (symbol, lotSize, monthlyTrades, accountBalanceUsd) already carry defaults and explanations. The description adds no parameter-level meaning beyond the schema, so it sits at the baseline rather than earning credit.

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 a specific verb (Compare) and an enumerable resource set (8 named FX/Gold ECN brokers plus named prop firms), and lists the exact metrics computed. It is far from tautological and clearly scoped to broker/prop-firm cost comparison. It stops short of 5 only because it never distinguishes itself from the adjacent sibling calculate_swap_carry_and_kelly_lot_size, which overlaps on swap/carry math.

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 only implied by the content — an agent can infer it is for evaluating broker economics before choosing one. There is no explicit 'use this when...' clause, no statement about when the sibling cost/swap calculator is the better choice, and no exclusions (e.g., that the broker list is fixed and cannot be extended).

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 supplied, so the description carries the full burden and does reasonably well: it declares the computation is deterministic, bounds it to 1-to-12 grid steps, and names the two stop-out thresholds (20% vs 0% broker margin) driving the result. It stops short of stating assumptions such as leverage, contract size, swap/commission treatment, or that it is a pure offline calculation with no live account access.

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, front-loaded with the purpose before enumerating outputs; no filler. The second sentence is a dense enumeration of computed quantities, but every clause corresponds to a distinct output.

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?

With no output schema, the description must enumerate what comes back, and it does so concretely (lots, margin per level, USD drawdown, pips to stop-out). Remaining gaps are the response structure/field names and modeling assumptions, minor for a pure computation tool.

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%, so each of the five inputs already has a documented default and meaning; the description adds no parameter-level syntax or constraints. It does implicitly reveal that the 20%/0% margin stop-out levels are fixed rather than configurable, which is mildly useful context.

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?

States a precise verb (run) and resource (deterministic 1-to-12 step Grid/Martingale drawdown survival simulation) with the supported instrument scope (XAUUSD or FX pairs). It also names the exact outputs (cumulative lots, margin, floating drawdown, stop-out distance), which cleanly separates it from the audit/calculation siblings.

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 modeled scenario implies when it applies (stress-testing a grid/martingale position against margin stop-out), but there is no explicit 'use this when...' clause and no routing versus the kelly/swap calculator sibling. Prerequisites and exclusions are left to inference.

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 full burden and does disclose substantial behavior: live DNS MX lookup, SPF/DMARC checks, disposable-provider detection, role-account classification, and per-email scoring/verdict output. It omits error handling, rate limits, and behavior for malformed or invalid email addresses, so it is not fully 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?

Two tightly written sentences with no wasted wording. The core capability and limit are front-loaded, followed by the return value, making it easy to scan.

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 one parameter and no output schema, the description explains the input limit, processing methods, and return values sufficiently for selection. It still lacks error/rate-limit behavior and explicit sibling differentiation, which would matter more if annotations or output schema were present.

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 the single emails parameter; baseline would be 3. The description adds meaningful constraints not in the schema: up to 50 addresses, B2B focus, and the verification methods applied to each address.

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?

States a specific verb, resource, scope, and method: verify up to 50 B2B email addresses via live DNS MX lookup, SPF/DMARC posture, disposable detection, and role-account classification. It is clear enough to distinguish from a general domain-security audit, but it does not explicitly name or differentiate any sibling tool.

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 bulk B2B email verification and cold-outreach safety, but it gives no explicit when-to-use/when-not guidance or named alternatives. An agent must infer the appropriate context from the description alone.

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