Smartipedia

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

With no annotations, the description carries the full burden and does well: it discloses that the tool performs web search plus LLM generation, takes about 15 seconds, returns existing content when duplicates exist, and has a daily rate limit. These are material behavioral details beyond the name and 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?

Two tight sentences with no filler. The core action is front-loaded, and the caveats about duplicates and rate limits are packed efficiently into the second sentence.

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 creation tool with an output schema, the description covers the essential operational context: what it does, how long it takes, duplicate behavior, and rate limits. The agent has enough information to invoke it correctly and avoid unnecessary calls.

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?

There is only one parameter, title, and schema description coverage is 0%. The description compensates by explaining that the tool generates the article 'from a title,' clarifying the role of the parameter. It does not specify formatting constraints, but for a single simple string parameter that is a minor gap.

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

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

The description uses a specific verb and resource: 'Generate a new sourced encyclopedia article from a title.' It clearly distinguishes this creation operation from sibling tools like read_topic and search_topics, and even states the duplicate-handling behavior.

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 explicit usage guidance: search first, and if the topic is already covered, the existing article is returned instead of creating a new one. This tells the agent when to avoid calling the tool and implies the alternative search_topics.

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 burden of behavioral disclosure. It does capture the core behavior (semantic search with optional filters) and implies a read-only discovery operation, but it does not explain filter semantics, result ordering, or how filters interact. This is adequate but not rich.

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 with the core behavior front-loaded and the usage guidance in the second sentence. Every sentence earns its place, and there is no redundancy or 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?

The description covers the core purpose and when to choose this tool, and the output schema handles return-value shape. However, with no annotations and 0% parameter coverage, the six-parameter input is not fully clarified—especially filter semantics and value domains. It is sufficient for a simple query-only call but incomplete for confident use of all filters.

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

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

Schema description coverage is 0%, so the description must compensate for missing parameter documentation. It only says 'optional filters' without specifying which parameters are filters, what values are accepted, or how limit, quality, category, min_views, and difficulty behave. The agent is left to guess from property names.

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 a specific action ('semantic search') on the resource (topics) and contrasts it with 'keyword search', which maps to the sibling search_topics. This distinguishes the tool from siblings and makes its role unambiguous.

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

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

The description gives explicit conditions for use: 'Use when keyword search misses' and 'to browse a category by meaning rather than exact wording.' It does not name the sibling tool search_topics explicitly or state when not to use it, but the guidance is clear enough to route an agent 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?

With no annotations, the description carries the full burden of behavioral disclosure. It transparently discloses the concurrency risk ('clobbering a concurrent edit') and the mitigation (expected_revision). It also communicates the replacing/overwriting nature of the operation. It does not discuss permissions or irreversibility, but the core behavioral trait is adequately exposed.

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 with no filler. It front-loads the action, then gives the preferred-use condition, then the critical concurrency safeguard. Every sentence contributes essential information.

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

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

The description covers purpose, use case, and the most important parameter, and an output schema exists so return value explanations are unnecessary. However, with no annotations and 0% schema coverage, the lack of detail on how to identify a section, what format content should take, and the role of edit_summary leaves noticeable gaps for an agent selecting and invoking 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 description coverage is 0%, so the description must compensate for schema gaps. It meaningfully explains expected_revision, but the other parameters—slug, section, content, editor, and edit_summary—receive no explicit semantic guidance. An agent must infer the roles of required parameters like section and content from the tool's general purpose.

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: 'Replace one section of an article with corrected Markdown.' It clearly distinguishes itself from the primary sibling alternative, create_topic, by noting it is preferred over duplicating a topic when fixing an error. This leaves no ambiguity about what the tool does.

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

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

The description explicitly says to use this tool when correcting an error in an existing article and to prefer it over creating a duplicate topic. It also gives a concrete safety instruction: pass expected_revision from read_topic to avoid clobbering a concurrent edit. This provides clear when-to-use and how-to-use 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?

With no annotations, the description carries the behavioral burden. It discloses that results are ranked by demand and only contain non-existent topics, which is useful, but it does not clarify whether the returned list is capped by limit, how 'demand' is measured, or whether any other filtering or ordering behavior applies.

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 short sentences, front-loaded with the core definition and followed by the strategic use case. There is no filler or redundant restatement of the tool 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 one-parameter listing tool with an output schema, the description covers the essential information: what is listed, how it is ranked, and what it is for. The main gap is the unmentioned limit parameter, but the schema provides its name and default.

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

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

Schema description coverage is 0%, and the description does not mention the limit parameter at all. The parameter is self-explanatory enough (limit with default 20) to be usable, but the description adds no meaning beyond what the name and default already imply.

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 ('list') and a distinct resource ('missing topics'), defined as topics people searched for that don't exist yet. The demand-ranking and 'decide what to write next' framing clearly separate it from siblings like read_topic, search_topics, and even discover_topics.

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 clear context for when to use the tool: when deciding what to write next, prioritizing unmet demand. It does not explicitly name alternatives or state when not to use it, so it stops short of a full when/when-not guide.

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 behavioral transparency burden. It discloses that the tool returns a short AI explanation, does not generate a full article, and is cheap and fast. It does not explicitly state absence of side effects, but for a preview-style read operation this is reasonably transparent.

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 with no filler. It front-loads the core function, then adds cost and usage guidance. 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 tool with an output schema, the description covers the main workflow purpose and cost profile. It does not mention any edge cases or non-use cases, but the low complexity and existing output schema make it largely 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 description coverage is 0%, so the description must compensate for the text parameter. It adds only 'any phrase,' which provides minimal scope but no constraints, examples, or format guidance. The parameter is simple, but the description does little to enrich 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 a specific verb and resource: 'Get a short AI explanation of any phrase.' It also distinguishes the tool from full article generation by saying 'without generating a full article,' which separates it from create_topic and related siblings.

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 explicit usage context: 'use it to decide whether a topic is worth creating' and notes it is 'cheap and fast.' However, it does not explicitly name alternative tools or state when not to use it, leaving some inference to 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?

No annotations are provided, so the description carries the full burden. 'Fetch' clearly signals a non-mutating read operation, and the description states the return shape (Markdown article with infobox and citations). It does not cover behavior for missing slugs, permissions, or the effect of include_sources, but the core behavior is reasonably transparent.

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 sentence with no filler, front-loading the verb and object. Every word contributes to understanding what the tool does, which is exactly what a concise tool description should do.

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 and has an output schema, so return-value detail is not required. However, the description omits the meaning of include_sources and provides no explicit guidance for choosing between this and sibling tools. It is nearly adequate but has clear 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 description coverage is 0%, so the description must compensate. It explains that the slug identifies the topic, but it never explains include_sources, its boolean meaning, or its default true value. The description leaves half the parameters semantically 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?

The description uses a specific verb ('Fetch') and a specific resource ('full Markdown article for a topic slug'), and mentions the output elements (infobox and citations). This clearly differentiates it from the sibling tools: search_topics discovers topics, while create_topic and edit_section mutate.

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: use this when you already have a topic slug and want the full article content. However, it does not explicitly say when to use it instead of search_topics or discover_topics, nor does it mention that those tools should be used to find a slug first.

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 behavioral disclosure. It does reveal what the tool returns (slugs and summaries) and that it does not return full article content, but it omits other behavioral details such as fuzzy matching behavior, result ordering, pagination, or side-effect guarantees.

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 tight sentences with no filler. The search scope is front-loaded, and the follow-up tool call is the very next piece of information an agent needs.

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 search tool with an output schema and a clearly named sibling (read_topic), the description covers the core workflow and return semantics. The main missing piece is any guidance distinguishing this from discover_topics or clarifying how limit affects the result set.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning for the query parameter by framing the search as keyword-based over titles and summaries, and the optional limit parameter has an understandable name and default. Still, limit behavior is not explicitly described.

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 a specific verb ('Keyword search') and a precise resource scope ('Smartipedia article titles and summaries'). It also differentiates from the read_topic sibling by noting that this tool returns only slugs and summaries, not full articles.

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

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

The description gives a clear workflow: search for topics, then call read_topic with a slug to get the full article. It does not explicitly contrast this with discover_topics or state when not to use it, so it stops short of full exclusion guidance.

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