Coinslot — the vending machine for machines

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

With no annotations, the description carries the burden of disclosing behavioral traits. It adds useful context such as pricing ($0.002/call), free demo with 2KB cap, and nested-key flattening behavior. It does not mention output format details or error handling, but covers the key operational constraints.

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 core conversion purpose, followed by the request format and pricing cap. Every word adds value, no fluff or repetition.

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 bidirectional converter tool, the description covers direction, data format, nested key handling, and pricing constraints. It lacks explicit output format and error scenarios, but given the tool's simplicity and the absence of an output schema, it is reasonably complete.

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

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

Schema covers direction with an enum and data with a type description, but only 50% of parameters have explicit descriptions. The description adds the dot-path column behavior for json-to-csv and reinforces the POST structure, yet it does not fully compensate for the missing direction description and edge-case parameter behaviors.

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 it converts between JSON and CSV, with specific direction handling and nested-key dot-path behavior. This is a precise verb+resource pairing that distinguishes it from sibling conversion tools like html_to_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?

The description implies when to use the tool by specifying the two conversion modes and giving a POST example. It does not explicitly name alternatives or exclusions, but the context of JSON/CSV conversion is clear, and pricing/demo notes add practical guidance on when paid vs free usage applies.

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 burden. It discloses the HTTP method (POST), output (plain-English explanation and next N run times), timezone (UTC), pricing ($0.002/call with credits key), free demo without, and a 2KB input cap. This adds meaningful operational context, though it lacks error-handling or rate-limit details.

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 concise sentences, front-loaded with the core purpose. Includes relevant operational details (pricing, input cap) without excess. No wasted words.

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

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

Given the tool's simplicity and no output schema, the description adequately covers inputs, outputs, and constraints. It could mention error responses or additional output structure, but the high-level return description ('plain-English explanation and next N run times') is sufficient for basic usage.

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 67% (from and expression have descriptions; count lacks one). The description adds value by indicating the request body structure and clarifies that 'count' controls the 'N' in 'next N run times'. This partially compensates for the missing count description while not fully explaining all parameter nuances.

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 function: 'Parse a 5-field cron expression' and the outputs: 'plain-English explanation and the next N run times'. This is a specific verb+resource pairing that distinguishes it from siblings (convert, diff, etc.) which are unrelated.

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

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

Provides clear context on how to use the tool (POST with { expression, count?, from? } and explains the purpose of decoding cron expressions). It does not explicitly mention when-not-to-use or alternatives, but the sibling tools are unrelated, so this is not a significant gap.

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

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

With no annotations provided, the description carries the full behavioral transparency burden. It discloses that the diff is line-based, the output includes unified diff and structured changes, and there are size and pricing limits. No contradiction with annotations exists since there are none.

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 extremely concise, using three sentences to cover purpose, output, request format, limits, and pricing. Every clause contributes meaningful information with no redundancy.

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

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

For a simple diff tool with no output schema, the description covers the essential aspects: what it does, what it returns, how to call it, and constraints. It is nearly complete, but the unexplained context parameter leaves a minor gap in off-the-shelf usability.

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 parameter semantics. It names a, b, and context?, and 'two texts' implies a and b are strings, but it never explains what context means, its default, or its effect. This is a clear gap for such a minimal parameter set.

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 begins with 'Line-based diff of two texts,' which clearly states the verb and resource. It additionally describes the return format, making it unmistakable what the tool does and distinguishing it from sibling tools.

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

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

The description provides clear context for use: comparing two texts line-by-line, and includes practical constraints (max 200KB each, demo cap). It does not explicitly mention alternatives or exclusions, but the sibling tools are unrelated, so this is not a significant gap.

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

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

With no annotations provided, the description carries the full burden and does disclose meaningful behavioral details: live network checks via DNS-over-HTTPS, pricing per call, the free demo mode, and the 2KB input cap. However, it does not disclose the response format, possible error behavior, or how authentication works beyond mentioning a 'credits key.'

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 compact sentences with all information front-loaded. The first sentence states purpose and scope; the second covers request format, pricing, and demo limitations. No sentence is wasted.

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 read-type verification tool with no annotations and no output schema, the description covers purpose, input mechanics, and cost constraints well. However, it never explains what the tool returns after checking, which is a notable gap given no output schema exists to fill in that information.

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 only describes 'email' as a single address and 'emails' as an array with maxItems 20. The description adds significant semantics by specifying the POST body format, that the two parameters are alternatives, the maximum batch size, and the 2KB input cap, compensating for the 50% schema coverage.

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 the specific verb 'Check' and clearly identifies the resource (an email address) and the specific checks performed: RFC syntax, live MX records via DNS-over-HTTPS, and disposable-domain screening. This distinguishes it from all sibling tools, which are unrelated conversion/scheduling utilities.

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 concrete calling patterns (POST either { email } or { emails: [...] }) and constraints, making the intended usage immediately clear. It does not explicitly mention alternative tools or when-not-to-use, but none of the sibling tools overlap in purpose, so the lack of exclusions is acceptable.

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 description carries the disclosure burden. It discloses deduplication with counts, input size caps, and pricing model. However, it lacks details about the response structure or error behavior, which is important given no output schema.

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 dense sentences with no filler. Front-loaded with purpose, then behavior, then cost. 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?

Good coverage of purpose, input, and cost. Missing explicit return format details, though 'deduplicated with counts' hints at it. For a single-param tool with no output schema, more detail on output fields would improve completeness. Still, context is largely 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 covers 100% (text required). Description adds value by noting the demo cap (2KB) vs full 500KB, and by confirming the POST body format. This goes beyond the schema's static max size.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states action 'Pull structured data out of raw text' and lists specific entity types (emails, URLs, IPs, phone numbers, dates). This distinguishes it from sibling tools like convert or html_to_markdown. Title 'Extract Pack' is vague but description clarifies.

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

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

Provides clear context: for raw text input, with size limits and pricing. It doesn't explicitly exclude alternative tools, but no sibling performs entity extraction, so the context is sufficient. However, it doesn't say 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?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly mentions input limits (1MB for xml, 2KB demo cap), input modes (xml or url fetch), output content, and pricing. While it doesn't cover failure modes or auth details, it provides substantial transparency beyond what a schema would convey.

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, front-loaded with the core purpose, then input methods, then pricing/limits. Every sentence earns its place with no redundancy or filler, making it efficient and easy to parse.

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 is simple (2 optional params, no output schema), and the description covers purpose, inputs, limits, and output shape. However, it does not explicitly state what happens if both xml and url are provided, or whether they are mutually exclusive. This minor gap prevents a 5, but the description is otherwise complete enough for an agent to invoke 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 description coverage is 100%, so the baseline is 3. The description reinforces that xml and url are alternative inputs, but the schema already conveys this via the url description ('Feed URL to fetch instead'). The pricing and demo cap are not parameter-specific, so the description adds minimal extra semantic value 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 clearly states the tool's function with a specific verb (Parse) and resource (RSS 2.0 or Atom feed), and specifies the output (clean JSON with title, items, link/date/summary, HTML stripped). This distinguishes it from sibling tools like extract or html_to_markdown, which serve different purposes.

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

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

The description provides clear context for when to use the tool (when you need to parse an RSS/Atom feed) and explains how to invoke it (POST xml or url). It notes a demo cap and pricing, which are usage constraints, but it does not explicitly discuss alternatives or when not to use the tool. This aligns with 'clear context, no exclusions'.

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 present, so the description carries the burden. It discloses key behavioral traits: strips scripts/styles/comments, size limits (500KB, 2KB demo), and pricing. This goes beyond a basic statement and informs the agent about side effects and constraints. However, it does not mention error handling or output format, so 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 two sentences and front-loads the core function, then provides essential usage details. Every word adds value—no redundancy or filler. Perfectly concise and well-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?

The tool is simple (one parameter, no output schema), and the description covers input, function, side effects, size limits, and pricing. It lacks explicit return value description but the purpose implies the output is Markdown. Given the simplicity, this is complete enough, but not exhaustive, so a 4.

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 fully describes the 'html' parameter, so baseline is 3. The description adds value by specifying the POST body structure '{ html }' and the demo 2KB cap, which are not in the schema. This enhances parameter semantics beyond the schema, warranting a 4.

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 'Convert HTML to clean Markdown' with specific element coverage (headings, links, images, etc.), which is a specific verb+resource and distinguishes from the sibling markdown_to_html tool. This is a clear, unambiguous purpose statement.

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 clear context about when to use (converting HTML to Markdown) and includes invocation details (POST, size limits, pricing), but it does not explicitly mention alternatives or when not to use. This is 'clear context, no exclusions', hence 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?

With no annotations, the description carries the burden of disclosing behavior. It states the HTTP method (POST), the accepted body shape (schema, data), the return format (all violations with paths), and constraints (draft-07 subset, 2KB input cap for demo). This is useful, though it does not detail error handling or the exact structure of the violations.

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 purpose, followed by return details and operational notes. Every sentence provides value with no redundancy.

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

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

Given no output schema, the description explains what is returned (all violations with paths) and covers usage constraints (draft-07 subset, pricing, input cap). It is sufficiently complete for a validation tool with only two parameters.

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

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

The input schema already describes both parameters (data as 'Document to validate', schema as 'JSON Schema (draft-07 core subset)'), so coverage is 100%. The description reinforces these meanings and adds the POST payload format, but does not significantly elaborate on parameter 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 clear verb+resource: 'Validate a JSON document against a JSON Schema.' It further specifies the draft-07 core subset and lists supported features, making the tool's function unambiguous and distinct from siblings like 'validate_ids'.

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 clear context for when to use this tool: validating JSON documents against a schema, with support for a specific subset of draft-07. It also notes pricing and a free demo, but does not explicitly state when not to use it or mention alternative tools, so it lacks explicit exclusions.

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

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

With no annotations, the description carries the full burden. It discloses that raw inline HTML is stripped (a security/behavioral trait), specifies input size limits (500KB, 2KB demo), and mentions pricing/authentication (credits key). It does not describe the output format or error behavior, but the key behavioral traits an agent needs are covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences with no fluff. It front-loads the primary action and packs in necessary details (GFM features, stripping, limits, pricing) without redundancy. Every 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?

For a single-parameter conversion tool with no output schema, the description is sufficiently complete. It covers input constraints, processing details, and cost/access. While it doesn't explicitly state the return format, 'to clean HTML' implies the output. Given the simplicity, it lacks only error-handling details, but overall it is 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?

The schema already documents the 'markdown' parameter with its type and max size, so the baseline is 3. The description adds value by stating the HTTP method (POST), repeating the 500KB limit, and introducing the demo 2KB cap—an extra constraint not in the schema. This provides additional semantic clarity 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 clearly states the tool's function: rendering Markdown to clean HTML, with specific mention of GitHub-flavored elements (tables, fenced code, task lists). It distinguishes from sibling tools like html_to_markdown by its direction, and the title 'Markdown → HTML' reinforces the 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 provides clear context on when to use the tool (converting Markdown to HTML, specifically GitHub-flavored) and notes important constraints (max 500KB, stripping inline HTML, demo vs paid tiers). It does not explicitly mention alternatives or exclusions, but the main use case is evident from the description despite not naming sibling 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the HTTP method (POST), input size cap (2KB), output format (standalone SVG), and crucial operational details such as pricing ($0.005/call), authentication requirement (credits key), and a free demo option with a 2KB cap. This exceeds typical transparency, though it omits error/status codes and potential failure modes, so a perfect score is not warranted.

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 exceptionally concise: three sentences that front-load the core purpose, then present the payload shape, and finally pricing/auth. Every sentence adds essential information with zero wasted words, making it easy to scan and understand.

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 the tool's purpose, input constraints, cost, and authentication, which is reasonable for a simple generator. However, it doesn't explain the semantics of ecLevel and dark/light, nor describe the response format beyond 'SVG', so an agent might misselect parameter values without additional knowledge. The lack of an output schema and annotations further raises the description's responsibility, and it only partially meets that bar.

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 only 40% (dark and light as 'hex color'), and the description merely restates parameter constraints (e.g., text max 2KB, margin range) without explaining their meaning or purpose. It fails to clarify what ecLevel represents, what dark/light actually color, or how margin affects the output, leaving significant gaps for a 5-parameter tool.

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 ('Generate') and resource ('QR code') plus the output format ('standalone SVG'), making the tool's primary function unmistakable. It naturally distinguishes itself from sibling tools, none of which generate QR codes, so an agent can easily identify when this tool is relevant.

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 clear context by showing the request shape (POST with the JSON payload) and notes the pricing/demo modes, but it doesn't explicitly state when to use this tool versus alternatives or mention exclusions. Since the purpose is self-evident and no sibling tool overlaps, the usage guidance is implied rather than explicit, which is a modest gap.

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 does a good job: it discloses the POST method, default 1MB cap, and pricing/demo limits. It also notes the tool requires a public URL and returns metadata, but it doesn't detail error handling or rate limits, which keeps it from a 5.

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 tightly packed sentences: the core purpose, the request structure, and pricing. Every sentence provides distinct, valuable information with no redundancy or filler, and it is front-loaded with the main function.

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 tool with only two simple parameters and no output schema, the description conveys the return type (Markdown plus title/metadata), input constraints, and even pricing. Minor gaps like specific metadata fields and error behavior remain, but overall the description is complete enough for an agent to select and call 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 only 50%, so the description adds value by showing the request shape 'POST { url, maxBytes? }' and explicitly mentioning the default 1MB cap. This helps clarify the `maxBytes` parameter beyond the schema's minimum/maximum values, though it doesn't fully explain its meaning as the response size limit.

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 ('Fetch a public URL') and resource, plus the output ('clean Markdown plus title and metadata'). It distinguishes itself from sibling tools by emphasizing 'One call instead of fetch + parse + convert', clearly differentiating from tools like `html_to_markdown` and `convert`.

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 the tool ('One call instead of fetch + parse + convert'), giving context that it replaces a multi-step process. It also mentions the free demo vs. paid credits, which is an important usage consideration, but it doesn't explicitly state when NOT to use it relative to alternatives like `html_to_markdown`.

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 takes on full behavioral disclosure. It mentions DST correctness, return values, request format, and pricing/demo limits. However, it omits error behavior and authentication details beyond 'credits key', and does not explicitly state it's a read-only operation.

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, front-loaded with the core purpose, followed by return/request details and pricing. There is no filler; every sentence contributes useful information, making it 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?

For a moderate tool with no output schema, the description adequately covers purpose, return fields, request format, and constraints. It lacks explicit response structure details, but the described return fields give a sufficient picture for an agent to invoke and interpret results.

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

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

Schema coverage is 100%, with each parameter already well-described (e.g., 'IANA zone or array of zones (max 20)' for 'to'). The description repeats these constraints without adding new semantic detail, so it meets the baseline but does not exceed 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 clearly states the tool's purpose: 'Convert a timestamp between IANA timezones with DST handled correctly.' It specifies the action, resource (timezones), and adds specifics like return fields (local times, UTC offsets, weekday, hour-of-day), distinguishing it from generic conversion tools.

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

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

The description provides clear context for when to use this tool (timezone conversion with DST handling) and outlines the request format. However, it does not explicitly state when not to use it or mention alternatives among siblings like 'convert' or 'cron', though the specificity reduces ambiguity.

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

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

With no annotations, the description carries the full burden. It discloses real checksum math, batch limits, authentication via credits key, pricing, and input caps. This goes beyond typical transparency, though it does not mention output format or error 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?

Three sentences, each earning its place: purpose, usage, and pricing/auth. No redundancy, and the critical information is front-loaded. Excellent structure.

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 captures usage constraints well, but because there is no output schema, it should at least hint at the response shape (e.g., validation result for each ID). Its omission leaves a notable gap for a validation tool, preventing a higher score.

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 has 0% description coverage, so the description compensates by explaining the relationship between type, value, and items (POST {type, value} or {items: [...]}). It clarifies the enum options and batch constraint, adding meaningful semantics not apparent from the bare 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 uses a specific verb ('Validate') and resource ('identifiers') with a detailed list of supported types (credit card, IBAN, EAN-13/UPC-A, ISBN-10/13, UUID) and methods (Luhn, mod-97, etc.), making its purpose unmistakable and clearly distinct from sibling tools like convert or diff.

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 concrete usage context: POST with either a single object or items array (max 50), pricing with credits key, and free demo without. This tells the agent how to invoke it and under what constraints. It lacks explicit 'when not to use' guidance, but the tool's specificity makes alternatives apparent.

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