AARF Utility Network

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

Annotations already establish read-only, idempotent, open-world, non-destructive behavior. The description adds useful behavioral context beyond that: results are evidence-ranked and source-linked, and the operation costs $0.35. It does not contradict 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?

Three short sentences, with the core action first, a clear usage trigger second, and pricing last. There is minor repetition of 'supplied public sources', but no wasted 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?

For a two-parameter read-only tool with a full output schema, the description is nearly complete: it states the input requirement and return shape at a high level, plus pricing. The only real gap is that it doesn't spell out parameter semantics, which is partly covered by the schema names.

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 0%, so the description must carry the burden of explaining 'question' and 'sources'. It mentions 'supplied public sources' but never clarifies that the question parameter is the natural-language query, that sources must be an array of URLs, or the max/min limits encoded in the schema. Property names are clear enough to guess, but the description adds almost no parameter-level meaning.

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 action ('Collect relevant evidence'), the input source ('supplied public sources'), and the deliverable ('source-linked snippets that agents can cite and verify'). It is clear, but it does not explicitly contrast with sibling tools, so an agent must infer why it would pick this over document_structured_extractor or url_to_agent_markdown.

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?

'Use when an agent needs evidence-ranked excerpts from supplied public sources' gives a clear selection condition for this tool. It does not list exclusions or name alternative tools for cases where this tool is not appropriate, so it misses a bit of 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?

Annotations already state readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the agent knows it's safe. The description adds behavior about being a 'quick static check' and mentions cost, but does not disclose the exact nature of findings (e.g., false positives, confidence levels, token limits, diff size constraints) beyond what output schema might imply. Doesn't contradict 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 concise (two sentences) and front-loads the purpose, then adds usage hint and cost. It is efficient but could be more organized with explicit sections (e.g., when to use, limitations). Minor waste on cost which might be better in metadata.

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 a complex tool with 6 params and 0% schema coverage, the description omits critical guidance on parameters like 'focus', 'context', 'language', 'testOutput'—how they affect analysis. It also doesn't mention output format despite an output schema existing (raw JSON vs human-readable). It's a static analysis tool, so higher risk of false positives, but not disclosed. It's adequate for a simple call but not complete for optimal use.

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 0%, so the description must compensate. It does not elaborate on any parameters (diff, focus, context, etc.), but the schema itself provides names and types, and the enum for 'focus' is specified. Since the description doesn't add parameter guidance, it fails to compensate, but it does give high-level purpose. Score 4 would be too high; I set 4 because without any coverage, the description should have explained key params, but it doesn't. I'll re-evaluate: coverage is 0%, so baseline is low; description is insufficient. I'll score 1 because it provides no parameter semantics beyond the raw schema. Actually, I need to be strict: description has zero param details, so score 1.

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 reviews source-code diffs for bugs, missing tests, compatibility, unsafe migrations, and security issues, and notes it returns structured findings with evidence. It distinguishes from siblings by focusing on diff review, though it doesn't explicitly name a sibling like 'migration_safety_auditor' or 'semantic_web_diff' to differentiate.

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 says 'use before merging a patch when you want a quick static check' but provides no explicit guidance on when NOT to use it or alternatives (e.g., migration_safety_auditor for deep migration safety, dependency_risk_scanner for dependencies). It implies usage but doesn't clarify boundaries or integration with CI.

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 readOnlyHint, idempotentHint, and destructiveHint as false, so the agent knows this is a safe, non-mutating operation. The description adds a price tag ($0.08 per dataset), which is useful cost context, but doesn't elaborate on how validation behaves or whether it modifies the input. No contradiction with annotations, and the description adds some value beyond them, hence a 3.

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 plus a price note. The purpose and usage are front-loaded, and there is no redundancy. The price is a useful addition for cost-aware agents, and the description is efficiently structured.

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 four parameters and zero schema coverage, the description is incomplete. It doesn't explain what format the 'data' string should be (raw CSV text? JSON string?), how to pass it, what the output looks like, or how validation handles errors. The output schema exists but is not referenced. For an agent to call this correctly, it would need to guess at parameter semantics.

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 0%, so the description must explain the parameters. It mentions operations like 'deduplicate' but doesn't map them to the 'deduplicateBy' parameter or explain 'requiredColumns'. The only inference is that these operations exist, but the agent cannot know how to configure them. This is a significant gap.

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 normalizes, trims, validates, and deduplicates tabular CSV or JSON data. It names the specific operations and data types, which distinguishes it from general validators like document_validator. However, it doesn't explicitly contrast with sibling tools, so it falls short of a 5.

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 use condition: 'Use when JSON or CSV-like data needs normalization, trimming, or duplicate removal.' This gives clear context for when to invoke the tool. But it doesn't mention when NOT to use it or suggest alternatives, so it's only minimally adequate.

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 signal readOnly, idempotent, and non-destructive behavior tournaments. The description adds useful behavioral context by clarifying that this is a static analysis tool and explicitly disclaiming live CVE data, which aligns with openWorldHint=false. No contradictions with annotations are present.

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?

Three short sentences deliver scope, limitations, usage context, and pricing with no fluff. The verb and target resource are front-loaded, and every sentence adds a distinct piece of decision-relevant 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?

Given the input schema, output schema, and annotations, the description is mostly complete for correct invocation: it states the domain, the static nature, the limitation, and pricing. A slight gap is the lack of guidance on how 'files' content should be structured and how ecosystem selection works, but these are partially inferable from the schema.

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 0%, so the description must compensate. It conveys that the scanner works on dependency manifests and what kinds of issues it detects, which helps infer the role of 'files'. However, it does not explain 'ecosystem' or 'allowedLicenses' beyond what their names and the enum values imply, leaving some parameter semantics to 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: 'Inspect dependency manifests', and enumerates the concrete checks performed (missing versions, risky install scripts, unknown licenses, policy issues). It also distinguishes itself from a live CVE database, which separates it from any vulnerability-focused 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 explicitly says 'Use when a manifest needs static checks for dependency, license, and policy risk,' giving a clear trigger condition. It does not name alternative tools or provide when-not-to-use cases, but it does add a limitation by disclaiming live CVE replacement.

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 read-only, open-world, idempotent, and non-destructive behavior. The description adds meaningful context: output is schema-validated JSON with confidence scores and page/source references, and there is a per-document price. This goes beyond what annotations provide and does not contradict 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?

Three sentences with no padding; the core action and output behavior are front-loaded. The price and usage guidance add useful information rather than fluff. Minor vagueness like 'supported documents' prevents a perfect score.

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 detailed JSON schema covers parameter shapes and an output schema exists, so return-value explanation is unnecessary. However, with zero parameter descriptions in the schema and the description omitting how to specify source and fields, key input semantics are left to inference. The tool remains callable using just the schema, but the description alone is not fully self-sufficient.

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 0%, so the description must compensate by explaining parameters. It mentions task-specific profiles and source evidence, but it doesn't name the three parameters, explain the source accepted types (url/text/base64), or describe the optional 'fields' array. The profile enum is only partially illustrated with examples, leaving meaning for the agent to infer.

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 'Extract' plus resource: 'structured fields and source evidence' from supported documents using task-specific profiles. Lists representative profiles (tenders, invoices, contract dates, privacy policies) and mentions schema-validated output, so an agent can identify the tool's role. It doesn't explicitly differentiate from sibling tools like document_table_extractor, but the profile-based extraction concept is clear enough.

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?

Explicitly instructs 'Use when you need named fields extracted from an invoice or other document with evidence.' This provides a clear trigger condition. It doesn't specify when not to use the tool or direct to alternatives among siblings, so it stops short of full 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds pricing and the notion of 'clean structured rows,' but does not disclose details like format auto-detection behavior, header handling, or potential limitations.

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 short and front-loaded with the core purpose. The phrase 'for agents and business workflows' is somewhat generic, but the overall structure is efficient and the pricing note is useful operational context.

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 output schema and annotations cover return structure and safety, so the description is not required to explain every detail. However, given the 0% schema coverage and lack of parameter explanations, the description leaves an agent without enough guidance on how to provide the document or configure headerRow.

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 0%, and the description does not explain the 'document' or 'headerRow' parameters. It only echoes the format enum values (markdown, csv, text), leaving important parameter semantics undocumented.

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 action ('Extract Markdown, CSV, and plain-text tables into clean structured rows') and names the resource and result. It is clearly distinguished from sibling tools like document_structured_extractor by focusing specifically on table extraction.

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 clear usage context: 'Use when a text or Markdown document contains tables that an agent needs as structured rows.' It does not explicitly list exclusions or alternative sibling tools, but the guidance is sufficient for typical selection.

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 the tool read-only, idempotent, and non-destructive, so the description does not need to restate those. It adds useful behavioral context beyond the annotations by positioning the tool as a pre-automation validation step and disclosing the cost ('$0.25 per document'). 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?

Two sentences with no filler: the first states the action and scope, the second gives the usage condition and cost. Every sentence earns its place, and the key information is front-loaded.

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 description is strong for selection and basic invocation, but with 0% schema coverage it leaves gaps around the requiredFields parameter and the exact meaning of profile-specific validation. The output schema covers return values, so that omission is acceptable, but input semantics are only partially explained.

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 0%, so the description must compensate. It maps concepts like 'invoices, contracts, tenders' to the profile enum and 'required fields' to the requiredFields parameter, but it does not explain the full enum set (including 'generic'), how requiredFields should be formatted, or what 'source evidence' means operationally.

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 ('Check'), a concrete resource scope ('invoices, contracts, tenders, and text documents'), and a clear outcome ('required fields and source evidence'). It also distinguishes this tool from extraction-oriented siblings by framing it as validation against a known profile before downstream automation.

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 an explicit usage condition: 'Use when a text document must be checked against a known profile such as an invoice.' This is clear context for when to invoke the tool, though it does not explicitly name alternatives 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.

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations: it costs $0.20 per match and supports a configurable tolerance. No contradiction with annotations exists.

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 concise sentences: what it does, when to use it, and its price. The core function is front-loaded and every sentence contributes essential information without fluff.

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 description covers purpose, usage context, cost, and tolerances. Since an output schema exists, return values do not need to be explained, and annotations already cover side-effect safety. The definition is complete for agent selection and invocation.

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 0%, so the description must compensate. It explicitly maps all three parameters: 'invoice text' corresponds to invoiceText, 'receipt text' to receiptText, and 'configurable amount tolerance' to amountTolerance. This adds meaning that the bare schema does not provide.

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 ('Compare'), a specific resource ('invoice and receipt text'), and concrete match criteria ('document numbers and totals'). This clearly differentiates it from sibling tools like document_validator or semantic_web_diff, which serve different comparison or validation 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?

The description explicitly says 'Use when an invoice and receipt must be compared for amount and identifier mismatches,' giving clear conditions for invocation. It does not name alternative tools or explicitly state when not to use it, so it stops just short of 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 establish read-only, idempotent, non-destructive behavior Mend. The description adds useful behavioral context beyond annotations: it returns structured findings with evidence and confidence, and it discloses a price point of $0.25 per migration. This informs agent decisions about cost and expected output without contradicting 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 compact and front-loaded: the first sentence states what the tool analyzes and what it detects, the second describes the output, and the third gives usage timing plus cost. Every sentence contributes information without redundancy or fluff.

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 an output schema present)Skip and readOnly/idempotent annotations, the description covers the main invocation needs: required parameters, dialect choices, expected findings, use case, and cost. The only notable gap is lack of guidance on how or why to provide the optional context object, which is a minor omission given it is not required.

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 0%, so the description must compensate. It does clarify that 'migration' is SQL and that 'dialect' refers to Postgres, Supabase Postgres, or Prisma/Postgres variants. However, it provides no explanation of the optional 'context' parameter or its subfields (tableSizes, postgresVersion, applicationCompatibilityNotes), leaving part of the schema semantically under-specified.

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 ('Analyze') with a clear resource ('Postgres, Supabase Postgres, or Prisma/Postgres migration SQL') and enumerates the exact analysis concerns: destructive operations, lock/data-loss risk, backwards-compatibility hazards, rollback issues, and safer sequencing. This strongly differentiates the tool from siblings like dependency_risk_scanner or code_diff_verifier.

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 states when to use the tool: 'Use before applying a database migration to identify destructive or risky steps.' It provides clear context for invocation, though it does not mention when not to use it or name alternative tools.

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 cover readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile needs no repetition. The description contributes non-obvious behavioral facts instead: the audit's focus areas (schemas, examples, operation IDs, discoverability) and a monetary cost of $0.20 per specification, neither of which appears in annotations or schema. No contradiction with annotations is present.

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, each earning its place: what the tool does, when to use it, and the cost. The most decision-relevant information (purpose) is front-loaded before the usage context and pricing. There is no filler, repetition, or schema duplication.

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?

An output schema exists, so return values need not be described. Annotations cover safety, and the description provides usage timing and price. Still, given 0% schema coverage for parameters, the description should fully define both inputs: it identifies `spec` as an OpenAPI document but leaves `baseUrl` undefined and does not state expected string formats (e.g., YAML, JSON, URL-to-spec, or raw content).

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 0%, so the description must compensate. 'Audit an OpenAPI document' clarifies that the required `spec` parameter is an OpenAPI specification, and the price phrasing ('per API specification') reinforces that meaning. However, the optional `baseUrl` parameter is completely unexplained, leaving unclear whether the parameter is a location, a base URL for the API, or a URL to fetch the spec from; this gap is not filled anywhere else.

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 opening sentence uses a specific verb and resource ('Audit an OpenAPI document') and enumerates the specific dimensions checked (schemas, examples, operation IDs, descriptions, payment/auth discoverability). It is clearly distinct from sibling tools like document_validator or website_accessibility_auditor, though it does not explicitly name a sibling alternative.

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 'Use before publishing an API to find missing schemas, examples, operation IDs, and agent-facing guidance' gives a clear context and trigger for when to invoke the tool. It does not, however, mention exclusionary conditions or name alternative tools for different scenarios, so it stops short of a 5. The price note also adds an operational precondition.

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 readOnlyHint, idempotentHint, and non-destructive, so the description doesn't need to repeat that. It adds the key behavioral trait: it processes locally without sending content to a model, which is a meaningful privacy benefit and goes beyond the annotations. It also mentions pricing but lacks details like potential rate limits or what happens to edge cases (unsupported formats). However, given annotations cover safety, a 3 is appropriate.

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 paragraph with no filler. It states purpose, usage context, and pricing in three sentences. Every sentence adds value, and the key constraint (no model send) is placed early. Highly efficient.

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 presence of an output schema and that annotations cover safety, the description provides sufficient context for an agent to decide when to call it. It covers the main use case and the local processing guarantee. It doesn't detail return format or error cases, but the output schema likely covers return details. A small gap is the lack of example usage, but it's adequate.

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 0% and no parameter details are in the description. The description mentions formats (text, JSON, source code) but doesn't map to the 'format' parameter or explain 'redactionStyle' options. With zero coverage, the description should compensate, so this is a gap. Also, 'text' parameter is required but not described beyond its type. However, the enum values are probably self-explanatory to an agent, so a baseline 3 is somewhat generous.

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: detecting and redacting personal data and credentials from various formats. It uses a specific verb (detect/redact) and resource (personal data, credentials). However, it doesn't explicitly differentiate from siblings like 'data_cleaner' or 'document_validator' – though the PII focus is implied by the name.

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 clear use case: 'Use before sharing logs, documents, tickets, or code that may contain personal data or secrets.' This gives the agent a clear trigger for when to use it, but it doesn't mention alternatives or exclusions (e.g., 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.

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering safety and side effects. The description adds useful behavioral context (filters cosmetic churn, noise) and pricing, which is beyond annotations. No contradiction found.

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 concise—three sentences, each adding value: purpose, filtering behavior, and pricing. It is front-loaded with a clear action verb and resource. Minor non-critical information (pricing) is present but acceptable.

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 tool has no parameters and an output schema exists, the description covers the core purpose and noise-filtering behavior. It seems complete for an agent to invoke it, though it doesn't describe return format details, but the output schema likely covers that. The pricing and use case are included, making it fairly 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?

The tool has zero parameters, so the schema is empty and there is nothing to describe. The description compensates by explaining the high-level inputs ('two webpage versions') but doesn't specify how inputs are passed. With zero parameters, a baseline of 4 would be reasonable, but the lack of detail on how inputs are provided reduces it to 3.

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 compares two webpage versions and returns meaningful content changes as structured JSON, which is a specific verb and resource. However, it does not explicitly differentiate from siblings like website_change_monitor, though context signals imply it focuses on content-level diffing.

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 provides a clear use case ('when you need to distinguish meaningful webpage changes from layout noise') and indicates pricing, which helps in decision-making. It does not explicitly name alternative tools or state when not to use it, but the guidance is sufficient for typical scenarios.

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 readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it removes clutter (navigation, scripts, cookie UI) and discloses a cost of $0.01 per page, which is important for agent decision-making. No contradiction with annotations. It could mention error handling or rate limits, but given the annotation coverage, a 4 is appropriate.

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, each earning its place: the first states the core action and output, the second gives the usage condition, and the third discloses cost. Information is front-loaded and there is zero fluff. It is appropriately sized for the tool's simplicity.

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 has an output schema (not shown but indicated), so return values need no explanation. The description covers purpose, usage, behavior (cleaning), and cost. It does not mention authentication or rate limits, but for a public-page fetch with read-only semantics and a single parameter, these are minor gaps. Overall, an agent can correctly decide to call this tool without needing additional details.

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 0% (no description for the 'url' parameter), but there is only one parameter with format 'uri' which is self-explanatory. The description implies that the URL must be public and is the target of fetching. It adds the context that the page must be publicly accessible, which goes beyond the schema. For a single trivial parameter, the description sufficiently compensates for the lack of schema-level documentation.

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 explicitly states the action (fetch a public webpage) and the output (compact agent-ready Markdown, canonical metadata, tables, important links) while also listing what it removes (navigation, scripts, cookie UI, layout clutter). This distinguishes it clearly from sibling tools like website_accessibility_auditor or website_change_monitor, which have different purposes. The verb+resource is specific and 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 gives a clear usage condition: 'Use when you need readable content from a public webpage without navigation or page clutter.' This tells the agent when to invoke the tool. However, it does not explicitly mention alternatives or when not to use it, though the sibling list implies different tools for other tasks. Since it provides a clear trigger without exclusions, it earns a 4.

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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the pricing ($0.20 per page), the heuristic nature ('fast heuristic'), and the specific checks performed. This goes beyond annotations without contradicting them.

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 two concise sentences: the first states purpose and checks, the second gives usage context and pricing. It is front-loaded and contains no fluff.

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 description is mostly complete for a simple tool with annotations covering safety and an output schema available. However, the ambiguity around the 'source' parameter (URL vs. raw HTML) is a notable gap that could mislead an agent. The tool's simplicity and schema help, but this key detail 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 input schema has one parameter 'source' with two variants (url or html), but the description says 'public HTML' which implies URL-only, failing to clarify that raw HTML can be passed directly. With schema description coverage at 0%, the description needed to explain the parameter options, but it does 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 clearly states the tool checks public HTML for specific accessibility failures, listing concrete checks (missing image text, labels, page language, headings, link names). This distinguishes it from sibling tools like openapi_agent_auditor or dependency_risk_scanner, which target different domains.

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 an explicit usage context: 'Use when HTML needs a fast heuristic accessibility review before publishing.' It gives a clear scenario but does not mention alternatives or exclusions, so it misses a full comparison with other audit tools.

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 mark the tool as read-only, idempotent, and non-destructive, so the description adds useful behavioral context rather than replacing the annotations. It adds that content is normalized to readable text and that comparison uses a baseline, plus the per-check cost of $0.03.

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 two sentences, front-loaded with the core action and use case, and the price is the only extra detail. Every sentence contributes to an agent's ability to decide whether to call this 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?

An output schema exists, so explaining return values is not required. Nevertheless, for a five-parameter tool with no parameter documentation, the description leaves unclear how baselines are stored or keyed, how focusTerms affects comparison, and what occurs when no baseline is provided.

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 0%, so the description needed to compensate for five undocumented parameters. It only implies url via 'fetch a public webpage' and vaguely refers to a baseline; focusTerms, baselineKey, saveBaseline, and previousContent receive no explanation.

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 operation: fetch a public webpage, normalize its readable text, and compare it with a caller-supplied baseline. This clearly distinguishes it from generic page fetchers and gives it a unique monitoring purpose.

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 'Use when an agent needs to fetch a public page and compare it with a previous baseline,' providing a clear trigger condition. However, it does not mention when to prefer semantically similar sibling tools such as semantic_web_diff, so the exclusion guidance is incomplete.

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