bots

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

No annotations provided, so description carries the burden. Discloses free preview limitations and emotion metadata. Lacks details on rate limits, response format, or potential errors, but for a simple chat tool it 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?

Description is concise (5 sentences) with no fluff. Front-loads the main action, then provides usage limitations, continuation hint, and examples. 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?

Given no annotations and no output schema, the description adequately covers purpose, usage context, parameter hints, and statefulness. Could be slightly more explicit about the reply structure, but overall sufficient for a simple 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 covers all 3 parameters with descriptions. The tool description adds value by providing example slugs and clarifying that session_id is used to continue the conversation, which goes beyond the schema's description.

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?

Description clearly states the verb 'Send a message' and resource 'live URBot expert bot', mentions emotion metadata in reply, and is distinct from sibling tools which are about getting bot info or listing/searching bots.

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 context on when to use: free preview limits, lifted with purchased bots or API key; also explains how to continue a conversation by passing session_id. No explicit when-not-to-use, but siblings are sufficiently different to imply usage.

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 compensates by detailing the return content (name, tagline, price tiers, capability radar, etc.). However, it does not explicitly state that it's a read-only operation, which is a minor 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?

The description is two sentences, front-loaded with the primary action, and includes essential return details. Every sentence adds value 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?

Given the tool has one parameter and no output schema, the description is highly complete. It lists the returned fields (name, tagline, pitch, etc.), mentions the 6-axis radar, knowledge topics, pairing bots, and team synergy bonuses, and references sibling tools for slug discovery.

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% for the single parameter (slug). The description mentions slugs can be found via sibling tools but adds no new semantic detail beyond the schema's description. The schema already provides examples, so the description's contribution is minimal.

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 tool retrieves the full card for a bot by slug, with a specific verb ('Get') and resource ('full card for one URBot bot'). It distinguishes from siblings by mentioning how to find slugs via list_bots or search_bots.

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 tells when to use this tool: when you have a slug to get a bot's full card. It also directs users to sibling tools (list_bots, search_bots) for finding slugs, providing clear context for when not to use this tool.

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 must inform about side effects and behavior. It explains the tool uses a live endpoint when credentials are available, otherwise falls back to a bundled snapshot (marked in frontmatter). This is good transparency, though it could mention that the tool is non-destructive and read-only.

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 long, front-loads the core purpose, and adds context without irrelevant details. Every sentence adds value, achieving maximum conciseness.

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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers the main aspects: what it generates, how it works (two modes), and where to use the output. It could elaborate on the exact format or limitations, but it is sufficient for an agent to decide and invoke 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?

There is only one parameter ('slug') with 100% schema coverage; the schema already provides a clear description and example. The tool description does not add further nuance beyond confirming it identifies a specific bot. Baseline score of 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?

The description clearly states the tool's function: generating a SKILL.md file for a URBot bot. It specifies the output format (YAML frontmatter, system prompt, capability profile) and intended use (dropping into compatible agents). This distinguishes it from siblings like chat_with_bot or search_bots.

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 explains when to use the tool (to export a bot as an agent skill) and mentions two operational modes (live endpoint vs. snapshot). However, it does not explicitly state when not to use it or list alternatives among sibling tools, which would strengthen 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?

No annotations provided, so description carries burden. It discloses data privacy (downloadable, runs locally) and output fields. Missing pagination info but otherwise transparent about what the tool does.

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 concise sentences: first states main action, second covers filtering, third provides usage advice and privacy note. 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 tool with no output schema, description comprehensively covers output fields, number of bots, filter option, and next-step guidance. Contextually complete for agent decision-making.

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 only parameter 'category' has a minimal schema description, but the tool description adds concrete examples of category values and notes case-insensitivity, significantly enhancing its meaning.

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 tool lists the URBot catalog of 150+ bots with specific fields (slug, name, tagline, category, price tiers). It distinguishes itself by implying it's for initial discovery before using get_bot, chat_with_bot, or get_skill.

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 clear context: use to list bots with optional category filter, then use returned slug with other tools. Does not explicitly differentiate from sibling search_bots, but gives enough guidance for typical use.

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 full burden. It discloses that it searches across multiple fields, returns top matches, and lists returned fields. It doesn't cover pagination or ordering, but for a search tool the behavior is reasonably clear.

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 action and key details. No superfluous words. 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?

Given the tool's simplicity (one parameter, no output schema), the description is fully adequate. It covers what is searched, what is returned, and provides usage examples.

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 covers 100% of parameters with a basic description. The description adds value by providing concrete examples ('sourdough', 'tax deductions') and clarifying the parameter's role, exceeding baseline 3.

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 explicitly states it keyword-searches the URBot catalog across multiple fields and returns top matches with specific fields. It clearly distinguishes from siblings like list_bots (listing all) and get_bot (fetching by identifier).

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 advises 'Use this first when you know the task but not which expert bot to use', providing clear context. It does not explicitly state when not to use or list alternatives, but the context and sibling names imply those alternatives.

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