MCPFax Reliability

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

The description goes far beyond the annotations by disclosing that the grade ranks observed risk rather than calibrated failure probability, is not a security audit, that coverage is young, that some risks are 'not rated' until coverage floors are met, and that the call costs $0.008 USDC. These caveats meaningfully shape how an agent interprets and uses the result.

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: it defines the output, gives the intended use, states critical limitations, explains coverage caveats, and discloses cost. No filler or repetition exists.

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, read-only, idempotent tool with no output schema, the description is complete: it specifies inputs, outputs, caveats, cost, and interpretation guidance. An agent has enough to decide when to call it and how to interpret the 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?

Schema description coverage is 100%, and the schema already explains the 'endpoint' parameter with examples and accepted formats. The tool description adds context about what the grade covers but does not need to repeat parameter syntax, 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 names a specific verb-resource pair ('Creditworthiness grade for one x402 endpoint or MCP server') and enumerates the concrete outputs: letter grade, 0-1000 score, subscores, confidence, flags, and dated evidence. This readily distinguishes it from sibling tools like revenue estimation or wallet authenticity.

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 an explicit usage context: 'Use this before trusting or paying an unfamiliar endpoint.' It does not explicitly state when not to use it or name an alternative tool, but the when-to-use guidance is clear and actionable.

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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses key behavioral caveats: inflow is observed on-chain and not proven to be x402 sales revenue, trend windows are limited by actual scanned span with per-window flags, and each call costs $0.005 USDC. This is exactly the kind of additional context that helps an agent set expectations. No contradiction with annotations.

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 but information-dense: the first sentence enumerates outputs, the second gives the intended use, and the remaining sentences convey necessary caveats and cost. No filler or repetition; each sentence 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?

Even without an output schema, the description details the response contents (including field names like trends.scanned_days and per-window flags) and caveats about data validity. The parameter is fully documented and cost is disclosed. An agent has enough information to invoke the tool correctly 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?

The input schema provides 100% coverage of the single 'endpoint' parameter, including examples and format guidance ('host:<hostname>' vs 'mcp:<publisher>/<name>'). The description reinforces that it applies to 'one listing' but does not add meaning beyond the schema. With high schema coverage, 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 clearly states the tool observes on-chain revenue signals for a single listing and enumerates the exact metrics returned (USDC inflow, distinct payers, modal transfer size, payment rate, trend windows, cohort percentile). It also gives a direct use case: sizing a seller's real traction, which distinguishes it from sibling tools focused on credit, drift, payer profiles, and authenticity.

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 explicit guidance on when to use the tool: 'Use this to size a seller's real traction.' It does not name alternatives or state when not to use it, but the usage context is clear and actionable enough to route selection among the listed 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?

Annotations already declare readOnly/idempotent/closed-world, but the description adds substantial behavior beyond them: exact pricing ('$0.008 USDC per call via x402 on Base'), the honest-answer billing nuance ('observed-unchanged ... is a real answer and is billed'), the no-verdict policy for `not_observed` with its rationale, and the watermark semantic that it is 'the last date MCPFax actually observed, never the current clock' so no window is skipped. No contradiction with annotations.

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 roughly 150 words, well above typical length, but nearly every sentence earns its place: scope, anti-discovery disclaimers, call pattern, differential-return semantics, billing, and the not_observed caveat each carry distinct information. It is front-loaded with the core purpose before caveats, and the density is justified by the domain complexity.

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 carries the burden of setting response expectations, and it names the key response elements: changes after the watermark, a fresh watermark, observed-unchanged entries with dates, the not_observed list with no verdict, and archive span. It also covers billing and the first-call archive behavior via schema. The remaining gap is the precise structure of an individual change record, which an agent would need to reliably parse 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?

Schema coverage is 100%, so the baseline is 3; the description still adds real meaning to both parameters. It explains that `subjects` is limited to 50 and how the `since` watermark drives differential results, and it deepens the watermark contract (observation date, not current clock). The schema already documented first-call behavior ('Omit on a first call to receive the whole archive'), so the description complements rather than replaces it.

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-and-resource purpose: detecting changes (payTo rotations, price, asset/network, terms, tool-schema) under an x402 endpoint or MCP server that is ALREADY integrated. It explicitly disclaims what it is not ('not a discovery tool and will not help you choose a server'), which distinguishes it sharply from siblings like preflight_check and the scoring 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?

It gives clear when-to-use context ('the one you wired in still matches what you wired in') and a strong when-not exclusion ('will not help you choose a server'). The call pattern for repeat checks is explicit: pass up to 50 subjects plus the `since` watermark to keep checks cheap. It does not name a specific sibling tool to use instead for discovery, which is the only gap preventing a 5.

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds meaningful context beyond that: screening signals are coverage-gated and returned as 'not_rated' until the 14-day/20-payment floor, measured graph facts are always returned and carry no intent inference, and the call costs $0.008 USDC via x402. This gives the agent a solid model of what the call will and will not reveal.

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?

Every sentence earns its place: the first lists the returned data, the second states the intended use, the third explains the coverage-gating caveat and intent-neutrality, and the fourth gives pricing. Information is front-loaded and dense without being bloated.

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 read-only tool with no output schema, the description covers what data is returned, how screening signals behave under insufficient data, what the caller should use the data for, and the cost. Nothing essential for correct invocation or interpretation 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?

The schema already fully documents the single address parameter with a regex pattern, example, and description, so the description carries little additional parameter burden. It does reinforce that the address is the paying wallet, but this adds only marginal meaning 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 a specific resource (a paying wallet's know-your-agent profile) and enumerates the concrete facts it returns: distinct services paid, observed spend, contract vs. externally owned account, and screening signals. It also states the intended action, 'vet a counterparty that is paying you,' which clearly separates it from sibling tools like wallet_authenticity or revenue_estimate.

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 tells the agent when to use the tool: to vet a counterparty that is paying you. It does not explicitly name alternatives or when-not-to-use conditions, but the use case is specific and clearly scoped relative to the sibling list.

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?

Annotations already declare readOnly, idempotent, and non-destructive hints, and the description adds material non-obvious behavior: events before the observatory archive started cannot be returned, the response reports coverage fields and whether the window predates the archive, and the call costs $0.005 USDC via x402 on Base. This is genuinely useful context beyond the structured annotations.

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 three sentences with each carrying distinct information: what the feed contains, when to use it, and critical operational caveats (archive boundary, response coverage fields, cost). It is front-loaded with the most decision-relevant facts and contains no 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 single-optional-parameter tool with no output schema, the description covers the event content, the intended monitoring workflow, the response-level coverage information, the historical limitation, and the cost. An agent has enough context to decide whether to call it and what the response will convey.

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 is only one optional parameter, since, and the schema already describes its format, default, inclusiveness, and archive-start limitation, so description coverage is effectively 100%. The tool description mainly restates that events are returned 'since a date' and the archive caveat, adding no new parameter-level 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 opens with a specific verb and resource: 'Chronological feed of rating actions since a date,' then enumerates the covered event types (grade upgrades/downgrades, payTo wallet changes, listing removals, returns). This clearly differentiates it from sibling tools like endpoint_credit_score or revenue_estimate, which are point-in-time scoring tools rather than a historical feed.

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 an explicit use case: 'Use this to monitor a portfolio of endpoints for adverse changes rather than re-scoring each one,' which names the alternative approach (re-scoring) and the condition that chooses this tool. The archive-start caveat also informs when the feed can be used for historical windows.

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?

Despite readOnlyHint=true and destructiveHint=false already being present, the description adds substantial behavioral context: it guarantees the probe is unpaid and side-effect-free, never changes target state, is bounded at ~2.2 seconds, costs $0.008 USDC via x402 on Base, and returns two distinct halves. It also honestly warns that the verdict is advisory and not a warranty, which is valuable beyond the annotations.

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 long but every sentence carries information needed for correct invocation: purpose, cost-benefit reasoning, return structure, parameter usage, verdict semantics, and billing behavior. The key caution is front-loaded, and the pricing detail comes at the end without burying the essential guidance.

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 six parameters, a complex pricing model, no output schema, and several sibling tools, the description is remarkably complete. It explains the two return halves, the meaning of UNKNOWN, the advisory nature of the verdict, the fallback to stored observations, and the exact conditions under which the user is charged nothing. An agent has enough information to decide when and how to call this tool safely.

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 the baseline is 3; the description goes further by explaining how expected_price, expected_payto, and expected_schema_hash are used in comparisons and how max_age_seconds interacts with the live half and verdicts. It does not exhaustively document every parameter, but it adds meaningful operational context 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 first sentence states a specific verb and resource: 'Check that an endpoint is live and that its terms have not changed, BEFORE you send it an expensive request.' It clearly differentiates this from siblings by focusing on preflight reachability and term-change detection rather than credit scoring, revenue estimates, or wallet profiles.

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 says when to use the tool (before expensive requests), when not to use it (a cheap $0.001 lookup, where the fee is eight times the call price), and how to reason about cost versus expected loss. It also defines when UNKNOWN is the appropriate result and when no charge applies, giving an agent clear decision rules.

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?

Beyond the readOnlyHint/idempotent annotations, the description discloses important conditional behavior: the haircut and adjusted inflow are null with status 'not_rated' until 14 days and 20 payments are observed, with a rationale. It also states the $0.01 USDC fee via x402 and the caveat that concentration is not proof of common control. This is rich, useful 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?

Every sentence carries load-bearing information: definition, use case, null-status condition and rationale, caveat, and cost. The most important framing is front-loaded, and there is no 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?

With only one parameter and no output schema, the description still enumerates the key result elements, explains when outputs are withheld, and clarifies interpretation limits. An agent can predict both invocation and expected response shape well enough to use the tool correctly.

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 the schema already fully documents the single 'wallet' parameter including format and an example. The description adds context that the wallet is a 'receiving' wallet and that calls happen on Base, but it does not need to compensate for schema gaps. 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 opens with a specific verb-object statement: 'Payer-concentration and wash-risk assessment for a receiving wallet,' then enumerates the concrete outputs (concentration, coverage, haircut, listings, USDC inflow). This clearly distinguishes it from revenue or credit scoring siblings even without naming them.

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 a direct use case: 'Use this to judge whether a seller's apparent revenue reflects real independent demand.' This gives the agent clear context for when to invoke the tool, though it does not explicitly mention sibling tools or state 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.