LoopingAgent

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 clearly states that the tool only retrieves a booking link and that actual slot selection happens externally on Cal.com, preventing the agent from promising booking functionality. It doesn't mention return format or edge cases, but the key behavioral boundary is well communicated.

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 focused sentences with no filler. The core purpose is front-loaded and the important behavioral caveat about external slot selection is included without unnecessary 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?

The tool is simple: one optional parameter, no output schema, no annotations. The description tells the agent what to do with the result (present the link) and what not to do (select the slot). This is sufficient for a correct invocation.

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 topic parameter is already documented as optional. The description adds value by clarifying that topic is only a hint and cannot be used to select the slot, which goes beyond the schema's basic explanation.

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 the specific verb and resource: getting a booking link to schedule a free 30-minute call with Laurent Knauss via Cal.com. The description also clearly differentiates this from the sibling content-retrieval tools (get_article, get_profile, etc.) by making the booking purpose explicit.

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?

Clearly implies use when a user wants to schedule a call with Laurent Knauss, and clarifies the agent's role is to present the link rather than handle booking. It doesn't explicitly list alternatives or exclusions, but sibling names are obviously unrelated, so the intended use case 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?

With no annotations and no output schema, the description carries the full burden and delivers: it discloses what is returned (metadata fields, full text, 3 related articles) and why the full text matters (accurate quoting/citation). It omits failure modes for an invalid slug, but the return contract is well 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?

Two sentences, front-loaded with the tool's verb and payload, then the routing constraint. No filler; every clause either enumerates return content or prevents a misuse.

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 must define the return shape, and it does so field by field plus the related-articles count. Combined with the prerequisite routing note, 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% — both slug (with examples) and include_content (default true, cheaper when false) are documented in the schema itself. The description adds only the motivation for retrieving content, so the baseline 3 is appropriate.

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 a specific verb (get) and resource (a single article), then enumerates the exact payload: metadata (category, description, cover, canonical URL), full plain-text content, and 3 related articles. This clearly separates it from list_articles/search_articles, which appear in the sibling set.

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?

Explicitly routes the agent: 'Use list_articles or search_articles first to find the slug,' which is a real prerequisite since slug is the required parameter. It lacks any explicit when-not-to-use statement (e.g., don't call this to browse), so it falls just short of the top band.

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 disclosure burden. It clearly communicates that this is a read-only profile retrieval and lists the specific information returned, implying no side effects or destructive behavior. It does not detail authentication or data freshness, but the absence of parameters and the informational nature keep the gap small.

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 focused sentences with no filler. The main identity of the tool is front-loaded, the content list is compact, and the usage guidance is delivered in a single clear directive.

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, zero-parameter profile-fetch tool, the description is complete: it names the subject, lists the returned fields, and tells the agent when to use it. No output schema or nested structures exist, and the description sufficiently covers what an agent needs 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?

The input schema is empty (0 parameters), so the baseline is 4. The description adds meaningful contextual information about what the profile contains, even though there are no parameters to document. It does not need to compensate for schema gaps because there are none.

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 'Full professional profile of Laurent Knauss' and enumerates the contained data (title, location, availability, contact, socials, certifications, booking link), making the tool's function unambiguous. It clearly distinguishes from sibling tools like book_call, get_skill, and list_articles, which target different resources.

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 instruction 'Use this first to understand who he is and how to reach him' provides explicit context for when to call this tool and positions it as an initial step. It does not explicitly mention alternatives or exclusions, but for a zero-parameter profile lookup the guidance is sufficient.

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. It does disclose the shape of the returned record (positioning, description, tech stack, live URL, status), which is valuable given there is no output schema, but it says nothing about behavior when the name is unknown, whether the lookup is exact-match/case-sensitive, or any auth/rate considerations.

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 waste. The resource and its returned contents are front-loaded, and the prerequisite guidance follows immediately.

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-record read with one parameter, no annotations and no output schema, the description covers the resource, its return fields and how to obtain a valid input. Only the failure mode for an unknown project name 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 schema already supplies example values for 'name', so the baseline is 3. The description adds real meaning beyond the schema by requiring the 'exact' name and pointing to list_projects as the way to obtain a valid value, implying an exact-match lookup rather than fuzzy search.

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 (get) plus resource (one project) and enumerates exactly what the record contains: positioning, description, tech stack, live URL, status. This clearly distinguishes it from list_projects, which the description names as the sibling that returns the collection rather than a single record.

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?

Explicitly routes the agent: 'Use list_projects first for the exact names.' This names the alternative tool and the condition under which it should be called before this one, leaving no inference needed about input acquisition.

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?

Since there are no annotations, the description carries full responsibility for disclosing behavior. It specifies that the tool returns grouped skills with labels and optional details, making the output format clear. Although it does not explicitly state that the tool is read-only with no side effects, the 'get' naming and informational content strongly imply a safe, non-mutating 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 concise, consisting of two sentences that flow logically from what the tool provides to how it should be used. It avoids unnecessary details and is well-structured, making it easy to read and understand. Every sentence adds value.

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?

Without an output schema, the description effectively documents the return format: skills grouped by domain, each with a label and optional detail. It also places the tool in context by describing the target subject (Laurent Knauss) and the intended purpose (assessing fit for AI/agentic roles). This is sufficient for an agent to decide when and how to use it.

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, so there is no input schema to explain. The description does not need to add parameter semantics because none exist. Any additional information would be redundant. This is a perfect score for a parameterless 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 clearly states that the tool returns Laurent Knauss' technical skills, grouped by specific domains (Agentic AI, RAG & Voice AI, etc.), and each skill has a label and optional detail. It also explicitly mentions the intended use case: to assess fit for AI/agentic development roles. This leaves no ambiguity about the tool's function.

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 a specific use case ('assess fit for AI/agentic development roles') and implicitly distinguishes itself from sibling tools like get_profile by focusing solely on skills. However, it does not explicitly contrast with alternatives or mention situations where this tool would be less appropriate. Slightly more explicit guidance on when to use it versus get_profile would be clearer.

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 burden. It discloses the returned fields (slug, title, category, description), which is valuable since there is no output schema, and the verb 'List' implies a non-mutating read. It does not mention ordering, pagination behavior, or what happens when no articles match, which would be needed for a full 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?

Three tight sentences: purpose and scope first, then return shape, then routing to alternatives. Every sentence earns its place 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 two-parameter list tool with no output schema, the description supplies the domain context, the return fields, and the sibling routing. Nothing an agent needs to call it 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?

Schema description coverage is 100%, so both limit and category are already documented in the schema, including the enum values. The description adds no syntax, default, or format detail beyond what the schema provides, 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?

States a specific verb (List) and resource (articles) with explicit scope (published on loopingagent.com) and domain context. It clearly differentiates itself from siblings search_articles and get_article, which are named directly.

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?

Explicitly routes the agent: use search_articles to find articles on a topic, and get_article to read one in full. This gives when-to-use and when-to-use-something-else guidance with the selecting condition.

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 behavioral burden. It usefully discloses the shape of the response (categories, counts, slugs) and implies a read-only browse, but says nothing about authentication, rate limits, or ordering — reasonable context for a trivial list tool, 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?

Two sentences, zero waste: the first states what is returned, the second states when to use it. The purpose is front-loaded before the guidance.

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 parameterless read-only listing with no output schema, the description compensates well by enumerating the returned fields (counts, slugs) and giving example themes. Minor gaps (ordering of results, whether categories are ever empty) are not material to correct invocation.

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 there is nothing for the description to disambiguate; the baseline for a 0-param tool applies. No parameter-related confusion is introduced.

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 and resource ('List the loopingagent.com article categories') and even names the returned payload (article counts and slugs), which no sibling does. An agent can distinguish this from list_articles/get_article without inspecting any 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?

Gives a concrete use case ('browse the blog by theme ... before fetching an article'), which implies the relationship to get_article/list_articles. It does not explicitly state when NOT to use it or name an alternative by exact tool name, so it falls short of a 5.

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 transparency burden. It explains what the returned list contains and that it is a high-level overview, but it does not state that the call is read-only, that filters are optional, or describe response ordering/pagination. The absence of side effects is implied by 'list' rather than stated.

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 compact sentences front-load the result content, state the purpose, and route to the sibling tool. 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 simple filtered list tool with no output schema, it covers what results contain and directs the agent to get_project for deeper detail. It could be slightly more explicit about the effect of omitting both filters (returns all projects), but the schema already documents the available 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 100% with well-documented enum params, so the baseline is 3. The description's references to 'live status' and 'agentic AI products and full-stack web apps' loosely map to status and category but add no syntax or 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?

States a specific verb ('list') and resource ('projects'), names the owner (Laurent Knauss) and describes the returned fields: domain, live status, positioning, tech stack. It also distinguishes itself from get_project by noting that tool returns the full description.

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 clear when-to-use guidance: 'Use it to assess his delivery track record' and points to get_project for the full description. It does not explicitly list exclusions or say when get_project should be preferred over this tool, but the alternative is named.

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 and does well: it discloses the search scope (title, description, body text), the return format (snippet of the matching passage), and that results are ranked by relevance. It does not mention permissions, rate limits, or explicitly that the operation is read-only, but for a search tool the described behavior is largely sufficient.

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 ordered from scope to return behavior to usage guidance, with zero filler. The four short examples are compact and directly reinforce how to phrase queries.

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 three-parameter search tool with no output schema, the description covers the essentials: what is searched, what comes back (snippets, ranking), and when to use it. Nothing critical an agent needs to call this 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying that the query searches title, description, and body text, which is more specific than the schema's generic 'Search terms' text. It does not elaborate on the limit or category parameters, but those are fully documented in 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 (full-text search) and resource (every article on loopingagent.com), and specifies exactly which fields are searched. It clearly distinguishes from siblings like get_article (by ID) and list_articles (browsing) by framing it as a relevance-ranked discovery tool for quoting or citing.

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 a clear when-to-use scenario ('find an article to quote or cite on a topic') with four concrete query examples spanning crypto and AI topics. It does not explicitly name alternatives (e.g., get_article for known articles) or state when not to use it, so it stops short of full routing guidance.

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