GramForge

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered structurally. The description adds only content-scope context (which questions are answered) and says nothing about response format or freshness — acceptable given the annotation coverage, but not additive.

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

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

A single front-loaded sentence naming the resource first, then a compact list of covered topics. Efficient with no filler, though the trailing list is a comma run-on rather than 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 no-argument, no-output-schema content retrieval tool, the description tells the agent exactly what subject matter comes back, which is the main thing needed to decide whether to call it. The only missing piece is disambiguation against sibling tools covering similar topics.

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 takes zero parameters, so the schema carries no semantics to explain and the baseline of 4 applies. The description's topic list is the only parameter-like information and it is not needed for invocation.

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 resource (FAQ answers) and enumerates the topic coverage — technical skills, payments, ownership, languages, integrations. This lets an agent match a user question to the tool, though it never explicitly distinguishes itself from overlapping siblings such as get_pricing (which likely also covers payments) or get_overview.

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

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

Usage is only implied via the topic list: if the question concerns one of the listed subjects, this tool fits. There is no explicit when-to-use rule and no exclusions, so the overlap with get_pricing on 'payments' and with get_overview on general topics is left unresolved for the agent.

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 full safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is low. The description adds the return contents (summary, steps, main links), which is helpful, but says nothing about auth requirements, caching, or link expiry that would go beyond structured data.

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?

Compact and front-loaded: what the tool is, then the value flow, then the return payload. No filler sentences, though the middle clause about the Telegram-assistant workflow is more marketing flavor than invocation-relevant detail.

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

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

With no output schema, the description carries return-value disclosure and does so at a high level (summary, steps, main links) — enough for a zero-required-param read-only overview tool. Only the localized-link behavior of the language parameter is left to 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 100%: the single optional 'language' enum parameter is fully documented in the schema (ISO 639-1, default en). The description never mentions localization, so it adds no meaning beyond the schema — baseline 3 is correct.

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 the resource (GramForge overview) and states concretely what it covers — what the product is, how it works, the describe-to-assistant workflow — plus what is returned. It is clearly not the FAQ, pricing, or use-case tool, though it never names those siblings explicitly.

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

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

Usage is implied: call this to learn what GramForge is before/alongside the other informational tools. There is no explicit when-to-use, no when-not-to use, and no routing to siblings like get_faq or get_pricing when those would be the better fit.

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 openWorldHint=false, so the safety profile is covered. The description adds genuinely non-obvious context beyond that: prices come in two currencies (USD and Telegram Stars) and are lower in many countries, which shapes how results should be interpreted. It stops short of describing return shape, since no output schema 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?

A single dense sentence that front-loads the resource and then the return contents and currency nuance. No filler, though the plural noun phrase makes it slightly list-like rather than immediately readable.

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-optional-parameter read tool with no output schema, the description covers what an agent needs: what plans exist, what data accompanies them, the currencies, and the country-dependent pricing caveat. Remaining gaps (exact field names/format of returned links and features) are minor.

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 both parameters (country with examples, language enum with default en) are already fully documented. The description only reinforces the country scoping ('for the given country') and says nothing about the language parameter, so it adds little beyond the schema.

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

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

The description names the concrete resource (GramForge plans: Free, Starter, Pro, Studio) and exactly what is returned (features and monthly prices in USD and Telegram Stars, scoped by country). It is clearly distinct from siblings like get_faq, get_overview, and match_business, though it does not explicitly name an alternative to route against.

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

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

Usage is implied rather than stated: an agent can infer this is the tool for price/plan questions, but there is no explicit 'use when...' trigger, no exclusions, and no pointer to sibling tools such as get_overview for general product questions.

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

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

Annotations already declare readOnly=true, idempotent=true, destructive=false and openWorld=false, so the safety profile is covered. The description adds real value beyond that by enumerating the returned content (features, example business description, prefilled sign-up link), which matters since there is 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?

A single front-loaded sentence with no filler. The trailing list of returned items is dense but each item is informative rather than redundant.

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

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

For a two-parameter read-only tool with a fully documented schema but no output schema, the description covers what the caller gets back, which fills the main gap. Only minor omissions remain, such as how the language parameter affects returned links.

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 both parameters (language enum, use_case slug) are already fully documented in the schema. The description adds no syntax, format, or default information beyond that, so the baseline 3 applies.

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

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

States a specific resource and scope ('Details of one use case'), which naturally contrasts with the sibling list_use_cases ('one' vs 'list'). It does not explicitly name any sibling, so differentiation is inferred rather than stated.

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

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

No explicit when-to-use or when-not-to-use guidance and no named alternatives. The singular 'one use case' scope implies this is the detail-fetch counterpart to list_use_cases, but the agent must infer that routing itself.

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, non-destructive, and closed-world behavior. The description adds no behavioral context beyond those annotations—it does not describe return format, pagination, or any call constraints. With annotations covering safety, this is a minimal contribution.

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 efficiently lists the guide topics. It avoids unnecessary words, though it could be strengthened by leading with an explicit verb.

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 list tool with no output schema, the description provides enough topical scope for an agent to understand what content is available. However, it omits the essential purpose verb and any routing cues relative to sibling tools, leaving a clear gap in selection guidance.

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

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

Schema description coverage is 100%, and the single language parameter is fully documented with a clear enum and default. The description adds no extra parameter semantics, so the baseline of 3 applies.

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

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

The description names the resource ('Step-by-step guides') and enumerates example topics, but does not state the action explicitly (e.g., 'Lists available guides'). It also does not differentiate this tool from sibling tools like list_use_cases or get_use_case, leaving the agent to infer its exact role.

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?

There is no guidance on when to use this tool versus alternatives such as list_use_cases, get_use_case, or match_business. The description only lists guide topics, so usage must be inferred entirely from the name and schema.

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, idempotent, non-destructive, closed-world behavior. The description adds no permissions, auth, rate limits, or mutation caveats, but it does describe the payload (business types, assistant actions, page link), which is useful output context. With annotations covering safety, this is adequate.

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 dense sentence that front-loads the resource and enumerates the covered business types. It is appropriately sized, though the noun-phrase style is slightly less scannable than a verb-led statement.

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?

No output schema exists, so the description must convey what is returned; it does so by naming the business types, the assistant behavior per type, and the page link. The parameter is documented in schema, and annotations cover the safety profile, so the definition is largely complete for a simple read-only list tool.

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

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

Schema coverage is 100%; the single language enum is fully described with ISO 639-1 and default en. The description adds no parameter semantics, so the high-coverage baseline of 3 applies.

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

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

Names the resource (business types) and the returned content (what the assistant does for each, page link), so the agent knows it is a listing tool. However, it never says how it differs from siblings like get_use_case or match_business, so it falls short of 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?

No explicit when-to-use, when-not, or alternative tool is given; the agent must infer that this is for browsing supported use cases. Sibling get_use_case (singular detail) and match_business are not referenced, leaving routing ambiguous.

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 readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond them: it discloses the output shape (match, assistant action, suggested plan, prefilled sign-up link) and hints at the downstream workflow. It does not discuss input constraints or failure modes, but the annotation profile lowers the bar.

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

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

A single dense sentence that front-loads the input and then enumerates the four returned artifacts 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 two-parameter read-only matcher with no output schema, the description covers input intent and output contents well enough to invoke it correctly. It could mention input length limits or language fallback, but the core contract is 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 coverage is 100%, so the schema already documents both parameters and the language enum. The description adds meaning by explaining that the description is a short business blurb and that its text is used to prefill the sign-up link, which connects the input to an output effect 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?

States a specific verb (match) and resource (business to a use case) and details exactly what it returns: closest use case, what the assistant would do, a suggested plan, and a prefilled sign-up link. It is clearly distinguishable from static siblings like get_faq, get_pricing, and list_use_cases, which return fixed content rather than a computed match.

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

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

The description implies usage (you provide a business description and get a tailored match) but never says when to prefer this over get_use_case or list_use_cases, nor when it is inappropriate. Sibling differentiation is left to inference.

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