MarkIt

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

Annotations already mark this as destructive and idempotent. The description adds important behavioral context beyond those annotations: it deletes only the reminder, not the underlying saved item, and it does not work for Google Calendar-synced reminders. This meaningfully informs the agent about side effects and boundary conditions.

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 primary action and parameter source appear first, then the scope boundary, then the Google Calendar exclusion. Every sentence carries distinct information, with no filler 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 single-parameter destructive tool with no output schema, the description provides all necessary context: what it acts on, how to obtain the ID, what side effects occur, and the key limitation. Nothing critical is missing for an agent to invoke it 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% with a single parameter, so the schema already documents reminderId. The description adds useful provenance by stating that IDs come from list_reminders or create_reminder, which helps the agent select valid inputs. This exceeds the baseline without being redundant.

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 action ('Cancel (delete) one reminder'), identifies the specific resource (a reminder identified by reminderId), and differentiates itself from related tools like list_reminders and create_reminder. It also clarifies what the tool does not affect, making its scope 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?

It explicitly tells the agent where to get valid reminder IDs (list_reminders or create_reminder), and it provides a clear exclusion: reminders synced to Google Calendar cannot be cancelled here and must be managed elsewhere. This gives both a clear when-to-use and when-not-to-use signal.

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, it discloses meaningful behavior: repeated calls reschedule rather than duplicate, and whatsapp/telegram are rejected for free users. This adds real context about side effects and restrictions 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?

Three sentences, each earning its place: the operation, the reschedule semantics, and the plan-based restrictions. The example is compact and informative with 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 four-parameter mutation with no output schema, the description covers the essential call semantics, source of ids, rescheduling behavior, and failure condition for Pro-only methods. It could additionally state what a successful response returns or whether deliveryMethod defaults to email, but those are minor gaps.

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 75%, and the description adds value by explaining that itemId comes from search/fetch, giving a full example shape, and clarifying deliveryMethod plan restrictions. It does not repeat the schema's message limits, but it supplements rather than duplicates.

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 plus resource: 'Set (or reschedule) a reminder on a saved item, by its id'. It also tells where ids come from and differentiates itself from siblings like cancel_reminder and list_reminders by framing the operation as set/reschedule.

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 usage context: ids come from search/fetch, one reminder per item, and calling again reschedules. It does not explicitly state when to use a sibling alternative such as cancel_reminder, so it falls just short of fully explicit when/not 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=true and openWorldHint=false, and the description adds valuable behavioral detail: output is truncated past 8000 chars, the response contains stored text, links, and metadata, and returned content must be treated as third-party data rather than instructions. This goes beyond the annotations and meaningfully informs the agent.

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 and id provenance, response contents and truncation limit, and a security warning. The most important operational detail is front-loaded, with no wasted words.

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

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

For a one-parameter, read-only fetch tool with no output schema, the description adequately specifies how to identify the item, what the response contains, and the truncation behavior. The security caveat adds relevant context for safe use. Nothing critical is missing for invoking this 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 description coverage is 100%, so the schema already documents the id parameter. The description adds that ids originate from search results, which is useful context, but it does not add significantly more semantic detail beyond what the schema provides.

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

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

The description clearly identifies the action ('Get the full content'), the resource ('one saved item'), and the required identifier ('by its id'). It also distinguishes this tool from sibling search tools by noting ids come from search results, so an agent can confidently select it.

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 phrase 'ids come from search results' clearly signals that this tool is used after search, providing strong contextual guidance for when to call it. It does not explicitly name sibling tools or state when not to use it, but the context is clear enough.

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, so the read-only nature is known. The description adds useful context beyond annotations by specifying that the result includes item counts and parent relationships, and that the names are intended for search filtering. This is appropriate for a simple, zero-parameter listing tool.

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?

One well-structured sentence front-loads the action and resource, then adds a useful directive for using the output. There is no wasted wording, and every clause 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 zero-parameter, read-only list operation, the description is complete: it states what is returned (categories with item counts and parent relationships) and how the result should be used (exact names for search's category filter). No output schema exists, but the response content is sufficiently described.

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 and an empty input schema, so there are no parameter semantics to clarify. The baseline of 4 applies, and the description correctly avoids introducing any nonexistent parameters.

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 'List' with a clear resource 'the user's categories' and adds detail on the returned data (item counts and parent relationships). It also differentiates from siblings by pointing to search's category filter, so an agent can distinguish it from list_reminders and search without opening their schemas.

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

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

The second sentence explicitly tells the agent to use the exact names to scope search's category filter, providing a clear when-to-use context and a link to a sibling tool. It does not explicitly list when not to use it, but the guidance is direct 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?

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful context by explaining that only pending reminders are returned and that each reminder includes its associated item, which matters because there is no output schema. It does not mention ordering, pagination, or limits, but those are minor for a simple read-only listing.

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 sentence that states the action, scope, and output content with no filler. Every part 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 zero-parameter, read-only listing tool, the description gives enough information for an agent to call it successfully: it names the resource, the pending filter, and the associated item returned. Since there is no output schema, mentioning the attached item is valuable; lack of ordering or pagination detail is a minor gap.

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

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

The tool has zero parameters and the schema already fully communicates that. With no parameters to document, there is nothing missing from the description; the baseline of 4 applies.

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

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

The description uses a specific verb ('list'), a specific resource ('the user's pending reminders'), and an explicit scoping condition ('not-yet-sent'). It also clarifies the returned content ('with the item they're on'), which distinguishes this from sibling tools like cancel_reminder, create_reminder, and list_categories.

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 makes the usage context clear by restricting results to pending, not-yet-sent reminders, which implicitly tells an agent when this tool is appropriate. It does not explicitly name alternatives or when-not-to-use scenarios, but the scoping language is strong enough for an agent to route correctly.

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?

The description goes well beyond the annotations by disclosing background scraping, auto-categorization, indexing for later search, monthly capture limits, and no-op behavior for duplicate URLs. These are important behavioral traits that annotations alone do not convey. There is no contradiction with the provided 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 front-loaded with the core purpose and each sentence adds meaningful detail: input modes, optional fields, background behavior, and quota/duplicate semantics. It is concise yet complete, with no filler or redundant restatement of the tool's name.

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 no output schema and a simple three-parameter input, the description fully covers what an agent needs to know: what to pass, what optional fields mean, what happens in the background, and important side effects like the monthly limit and duplicate no-op behavior. The lack of an explicit return-value description is not a significant gap here.

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, but the description adds useful meaning by explaining that content can be either a URL or text, that a note is the user's own context relating to the conversation, and that a title is only relevant for text notes. This enriches the parameter guidance beyond the raw schema definitions.

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: 'Save a link or a text note to the user's MarkIt library.' It clearly distinguishes two modes of operation (URL vs plain text), which differentiates this tool from siblings like fetch, search, and list_categories. The title is generic, but the description makes the tool's purpose precise and actionable.

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 usage context: pass a URL to save a scraped link, or pass plain text to save a note, with optional note and title fields. It does not explicitly name alternative tools or state when not to use this tool, but the instructions are specific enough that an agent can determine when save_item is appropriate.

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 read-only, and the description goes further: single-page/no-pagination behavior, 'Scores are only comparable within one response', and 'English queries rank best'. These are genuine non-obvious behaviors an agent cannot infer from schema or 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?

Every sentence carries operational value: purpose, result bound, example, date-mode usage, troubleshooting, and ranking caveat. It is front-loaded and free of boilerplate.

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 13-parameter search with no output schema, the description covers the essential behaviors: result cap, pagination absence, relevance mode, sort-by-date mode, and score comparability. Minor gaps remain around default sort and exact result fields, but the example and caveats make calling it correctly feasible.

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 description gives a concrete invocation example ('{query: "pasta recipes", source: "youtube", limit: 5}') and explains query omission semantics, adding meaning beyond the schema. It does not detail every filter, but schema descriptions cover 62% of parameters and the example fills the practical 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?

States a specific verb ('Search') and resource ('the user's MarkIt library') and adds result semantics ('Returns up to 30 ranked results in a single page'). It is clearly distinct from siblings like save_item and fetch.

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?

Gives an explicit alternate invocation mode: 'OMIT query to list the newest saves in date order (use this for "what did I save recently/last")'. Provides retry guidance for empty/failed results and a language caveat, but does not explicitly contrast with alternatives such as fetch for exact retrieval.

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?

Even though annotations carry no safety profile, the description discloses meaningful behavior: a real human reads each note, there is a daily rate limit, and only user-intended verbatim content may be sent. This goes well beyond the structured annotations and prepares the agent for side effects and 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?

All four sentences earn their place: core purpose, invocation guardrails, verbatim-content rule, and rate limit. The critical constraints are front-loaded in the second sentence, and there is no redundant 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 simple two-parameter feedback tool with no output schema, the description covers what the tool does, when to call it, what content to pass, who receives it, and the daily limit. An agent has everything needed to select and invoke it 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 both parameters are already described. The description adds semantic value by requiring the message to be passed 'in the user's own words' and by limiting calls to user-intended content, which instructs the agent on how to fill the message parameter correctly.

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: 'Relay a short feedback note from the user to MarkIt's founder.' This clearly differentiates send_feedback from the reminder/list/search/save siblings, so an agent can identify its unique purpose immediately.

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 explicit trigger conditions ('user explicitly asks to send feedback, report a problem, or message the founder') and explicit prohibitions ('never on your own initiative, and never with content the user did not intend to send'). This is model usage guidance with no ambiguity.

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