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

Despite no annotations, the description details the process (analyzes, matches, returns), output contents (top 2-3 legends, why matched, key insight, next steps), and includes a disclaimer. It fully discloses 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?

The description is well-structured with sections, bullet points, and examples. Every sentence adds value without 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 complexity (3 params, no output schema, no annotations), the description completely covers input, process, output, and use cases, making it self-contained.

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 baseline is 3. The description adds minimal additional meaning to parameters beyond what's in the schema; examples illustrate use but don't elaborate on each parameter.

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 action ('find') and the resource ('most relevant legends'), with examples that distinguish it from siblings like 'search_legends' and 'list_legends'.

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 'Use this when' section provides explicit scenarios (unsure which legend to ask, want expertise or quick insights). While it doesn't explicitly rule out alternatives, it gives clear usage context.

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?

Discloses output content and includes a disclaimer, but does not mention operational traits like authentication needs, rate limits, or side effects. With no annotations, the description should cover more behavioral aspects.

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?

Well-structured with bullet points and sections, front-loaded with purpose. Slightly verbose but each section adds information; could be more concise.

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?

Covers the output details, formats, and use cases adequately without an output schema. Minor gaps in parameter behavior documentation (e.g., effect of include_related).

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%, but the description adds value by explaining the output for each format and the use cases, expanding beyond schema descriptions.

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 this tool retrieves detailed information about legendary founders/investors, listing specific content categories. However, it does not explicitly distinguish itself from sibling tools like get_legend_insight or list_legends.

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 some context on when to use (e.g., 'When to consult this legend') and available formats, but lacks explicit guidance on when not to use it or how it compares to alternatives.

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 implies a safe read operation but does not disclose any behavioral traits such as side effects, permissions, or rate limits. The note on 'formatted wisdom' gives some output expectation, but is insufficient.

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 action verb and resource, and no unnecessary words. Perfectly concise for the information provided.

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?

Despite no output schema, the description explains the return format ('formatted wisdom') and sets expectations ('quick insight'). No major gaps, though could mention pagination or error cases, but acceptable given simplicity.

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 both parameters are described in the schema. The description adds no extra meaning beyond 'get insight about' the topic, so 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 it retrieves a quick insight or wisdom snippet from a legendary figure, specifying the output is formatted wisdom. It distinguishes from siblings like 'get_legend_context' by emphasizing 'quick' and 'formatted wisdom', but does not explicitly differentiate.

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 guidance on when to use this tool versus alternatives like 'search_legends' or 'get_legend_context'. No mention of 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?

Given no annotations, the description carries full burden. It states the tool lists all legends and mentions the vibe effect. But it does not disclose if it's read-only, any side effects, or authentication needs. Moderate transparency for a listing tool.

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 well-structured with a bulleted list and front-loaded main purpose. It is slightly verbose but each sentence adds context about the tool's offerings.

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 two optional parameters and no output schema, the description covers the main purpose, categories, and vibe option. It lacks details on pagination or errors but is sufficient for basic use.

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 baseline is 3. The description adds value by explaining the vibe parameter effect ('more entertaining presentation') and listing categories, but the schema already defines enum and descriptions.

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 all legendary founders and investors, with specific categories enumerated. It is a specific verb+resource that distinguishes it from siblings like search_legends or get_legend_context.

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 what the tool does and mentions the vibe parameter for presentation style. However, it does not explicitly advise when to use this tool over alternatives like search_legends for filtering or get_legend_context for details.

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 exist, so the description carries the full burden. It explains the process (ask question, select legends, respond in voice, get perspectives) and includes an educational disclaimer. However, it does not disclose whether the tool is stateless, if any data is persisted, or the exact output format (e.g., text vs. structured data). This is adequate but lacks some detail.

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 well-structured with headings, bullet points, and examples. It is slightly lengthy but every section serves a purpose (explanation, usage, examples, disclaimer). A minor trim could improve conciseness, but overall it is efficient and easy to parse.

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 absence of an output schema, the description explains that legends respond 'in their authentic voice' and provide 'diverse perspectives'. It does not specify the return format (e.g., array of strings, text block). For a conversational AI tool, this is largely sufficient, but a brief mention of the output structure would make it 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?

Input schema coverage is 100% with descriptions for all four parameters. The description adds context by explaining that legends can be auto-selected or specified, and that max_legends defaults to 3 (max 5). However, it does not elaborate on the 'category' parameter beyond what the schema's enum provides. The added value is marginal, so a 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 purpose: 'Activate Party Mode - multiple legendary figures discuss your question together!' It uses specific verbs ('discuss') and resources ('legendary figures'), and the concept of a group discussion distinguishes it from sibling tools like get_legend_insight (single figure) or search_legends (finding legends).

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 includes a 'Use this when:' section with four bullet points outlining ideal scenarios (multiple viewpoints, big decisions, varied advice, engaging discussion). It also provides three examples. Though there is no explicit 'when not to use', the positive guidance is clear and sufficient for an agent to make a selection.

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 fully explains the tool's behavior: it searches across multiple fields and provides examples of query matches. It does not disclose potential limits like pagination or empty results, but for a simple search, 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?

The description is concise: a short sentence defining the tool, followed by a list of example queries and a usage suggestion. No unnecessary words, front-loaded with purpose.

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 simple one-parameter tool, the description covers the key aspects: what it does, how to use it, and examples. It does not explain output format or error handling, but these are secondary for a search 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?

The single 'query' parameter is described in the schema as matching name, description, tags, and expertise. The description adds value by providing concrete examples of queries and expected results, enhancing understanding 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 states 'Search for legends by name, description, expertise, or tags,' providing a specific verb and resource. The examples further clarify the purpose, and it implicitly distinguishes from sibling tools like 'list_legends' which is for listing all legends.

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 says 'Use this for query-based discovery when you're not sure which legend to summon,' giving clear context. However, it does not explicitly mention when not to use or provide alternative tools for specific scenarios.

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?

Despite no annotations, the description thoroughly explains the tool's behavior: it analyzes the message, returns a primary action with ready-to-use params, and instructs to execute that action. It details the workflow and expected output. No contradictions with annotations.

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 lengthy but well-structured with headers, emojis, and code blocks. While it could be more concise, the detailed examples and breakdown justify the length for a tool that orchestrates other actions. Every section 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 complexity (suggesting actions for other tools), the description covers input format, output structure with example JSON, workflow, sibling usage guidelines, and a disclaimer. There is no output schema, but the description compensates by detailing the return format comprehensively.

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% with a description for 'message'. The tool description adds significant value beyond the schema by showing example usage (how to pass the message) and explaining that the return contains a primary_action with ready-to-use params. This compensates for the otherwise minimal schema 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?

The description clearly states that the tool returns ready-to-execute actions (party_mode or summon_legend) based on the user's message. It distinguishes itself from siblings by explaining when to use each tool (party_mode, summon_legend, auto_match), providing a specific verb+resource and unique purpose.

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 'USE THIS PROACTIVELY' and lists relevant topics (business, startups, etc.). It also includes a 'WHEN TO USE EACH TOOL' section that guides the agent on selecting the appropriate sibling tool, offering clear context and alternatives.

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. It explains the tool returns persona context for roleplaying and includes a disclaimer. It does not detail auth, rate limits, or output format, but the behavioral impact (safe, non-destructive roleplay) 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?

The description is well-structured with sections for overview, mechanics, and use cases. The list of legends is slightly lengthy but helpful. Overall, concise and front-loaded.

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 explains the output concept. It covers how it works and when to use it, but lacks details on error handling or edge cases. Adequate for a roleplay 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%, and the description adds practical context: 'Call this tool with a legend_id' and explains that the optional 'context' personalizes advice. Examples in the schema further enhance clarity.

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 purpose: 'Summon a legendary founder or investor. Returns their persona context so Claude can roleplay as them.' It distinguishes from siblings like 'list_legends' and 'get_legend_context' by focusing on roleplaying.

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 three specific use cases under 'Use this when', and lists available legends to guide selection. It lacks explicit when-not-to-use guidance, but the use cases are sufficient.

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