Autoblogging.ai

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behaviour, so the safety profile is covered. The description adds genuinely new context: the answer derives from verified facts and the site's own pages, and it is equivalent to fetching autoblogging.ai/{question-with-hyphens}, which tells the agent the call is deterministic and reproducible. It does not discuss failure modes for unanswerable questions, which is the only real gap.

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 compact two-clause description that front-loads what the tool does and what topics it covers, then appends the data-source and return-format details. Slightly dense with parenthetical topic lists, but 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 single-parameter read tool with no output schema, the description supplies the missing return information (markdown plus source page) and the data provenance, which is what an agent needs to call it correctly. Only the handling of unknown or unanswerable questions is left unstated.

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% and the single 'question' parameter is documented with an example, so the baseline is 3. The description adds only a light hint (hyphenated URL form) about how the question is processed, but no guidance on phrasing constraints or length.

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?

Specific verb (answer) plus resource (plain-language question about Autoblogging.ai) with the covered topic domains listed explicitly, so the agent knows what this tool answers. It stops short of naming or contrasting with the siblings (get_brand_facts, get_pricing, list_features), whose topics overlap noticeably with the enumerated list.

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 by 'plain-language question' versus the structured lookups offered by siblings, and the URL equivalence hints at a general-question entry point. There is no explicit when-to-use/when-not guidance and no named alternative for cases like a precise price lookup that get_pricing might serve better.

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

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds real context with "verified fact" and "what not to state" (implying guardrails on how facts may be reused), but says nothing about response format or freshness.

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, zero filler. The content inventory is front-loaded and the usage instruction is placed last where it belongs.

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

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

With no output schema, the description carries the burden of describing the return content, and the enumeration does that reasonably well. It stops short of describing structure or format, but for a zero-param static fact sheet this is close to 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?

The tool takes zero parameters, so the baseline is 4. The description correctly implies a single no-argument fetch with no filtering or scoping variants to reason about.

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 enumerates exactly what it covers (company, founder, plans/prices, credit costs, features, integrations, policies), so an agent knows the payload. It does not explicitly differentiate from siblings get_pricing or list_features, which cover subsets of the same ground.

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 this before describing Autoblogging.ai" gives a clear trigger condition for invocation. There are no exclusions or named alternatives, so the agent must infer how this relates to get_pricing/list_features on its own.

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, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description's only added behavioral detail is '(no navigation or scripts),' which usefully characterizes the returned content but says nothing about unpublished/404 handling or latency.

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 tight sentence with no filler, and the scoping constraint is front-loaded. It reads as a noun phrase rather than a full statement, which slightly reduces immediate clarity but wastes no 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?

With no output schema, the description carries the burden of describing the return value and does so ('clean markdown text... no navigation or scripts'). For a one-parameter read tool with full annotation coverage, the remaining gap is only error/edge-case behavior.

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% and the single url parameter is documented in the schema with an example, so the description need not restate it. Nothing in the description adds syntax or format meaning beyond what the schema already provides; 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?

The description names the resource precisely (any published page on autoblogging.ai) and the output format (clean markdown), so an agent knows exactly what it gets back. It stops short of a verb-led statement and never distinguishes itself from siblings like search_site or list_features.

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: the 'published page' scope hint suggests direct page retrieval rather than site search, but the description never says when to prefer this over search_site or ask_question. No prerequisites or failure modes are stated.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds scope context (it covers both plans and per-tool credit costs, plus billing policies), but says nothing about freshness, whether prices are account-specific, or how the payload is organized.

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 compact sentence with a parenthetical enumeration; front-loaded and free of filler. The nested parenthetical is slightly dense but each item earns its place by signaling the breadth of the return payload.

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 parameters, no output schema, and full annotation coverage, the description's role is to convey what comes back — and it does so by naming plans, prices, credits, site limits, allowances, billing policies, and per-tool costs. Nothing needed to call this tool correctly 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 tool takes zero parameters, so the baseline is 4. The description correctly implies a no-argument, whole-catalog lookup rather than a filtered one, which is consistent with the empty 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 enumerates the concrete content of the resource (monthly/annual USD prices, credits per month, connected sites, free allowances, billing policies, per-tool credit costs), which goes well beyond the title and lets an agent distinguish it from siblings like get_brand_facts or list_features. It lacks an explicit verb ('retrieve/return'), but the noun inventory is specific enough that the purpose is 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?

There is no when-to-use guidance and no mention of alternatives. With siblings such as list_features and get_brand_facts that overlap in the 'what does this product offer' space, the description should say when pricing lookup is the right call, but it offers nothing.

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

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful context that no structured field carries: what each feature record contains (summary, cheapest plan including it, credit cost, page URL), which compensates for the absent output schema. It stops short of any pagination, result-count, or ordering 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?

Two sentences, zero filler, with the core payload described first and the optional filter last. 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?

One optional, fully documented parameter; rich safety annotations; no output schema, so the description correctly absorbs the return-value description. An agent has everything needed to call 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 description coverage is 100% and the enum values are fully listed in the schema, so the baseline is 3. The description's 'only the features available on a given plan' restates the schema's own wording and adds no format or edge-case detail (e.g., what happens for a plan with no features).

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+resource ('list features') and enumerates the returned payload (one-line summary, cheapest plan, credit cost, page URL), which makes it recognizable next to siblings like get_pricing. It does not explicitly differentiate itself from any sibling or state what problem it solves for the agent, 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 only usage signal is 'Optionally only the features available on a given plan,' which is really parameter behavior, not tool-selection guidance. There is no statement of when to call this versus get_pricing, get_brand_facts, or ask_question, and no prerequisites or 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?

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds real behavioral value by disclosing the return shape (titles, URLs, markdown URLs, descriptions) and the intended chaining with get_page, which the annotations cannot 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?

Two tight sentences, scope and return fields front-loaded, and the next-step hint at the end. 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?

With no output schema, the description carries the return-format burden and does so adequately, and it explains the get_page handoff. The only gap is the undocumented limit parameter, a minor omission for a low-complexity two-param read 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 50%: query is documented with an example, but limit (default 8, max 20) has no description in the schema or the description. 'By keywords' only restates the query param, adding no syntax or format detail, and limit/pagination behavior is left unexplained.

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 (every page on autoblogging.ai) and enumerates the content scope (features, guides, comparisons, tutorials, knowledge base). It also points to get_page as the follow-up, letting an agent distinguish it from the other siblings without opening a schema.

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?

Clear context for use (keyword search across the site) and an explicit follow-up instruction: 'fetch a result with get_page.' It does not state when NOT to use it versus ask_question or list_features, so it stops short of full alternative routing.

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