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

Discloses that the MCP server does not run the pipeline directly but returns a prefilled URL. Covers the key behavioral trait without contradiction, though minor missing details like auth or rate limits.

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?

Concise and well-structured: opening purpose, builder explanation, args, returns, usage guidance. 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 (2 params, no output schema), the description covers all needed context: when to use, what it returns, and what the builder does. No 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?

With 0% schema coverage, description fully explains parameters: task with example, lang with allowed values and default. Adds substantial value 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?

Clearly states the tool creates a deep link to the Pipeline Builder for custom tasks. Distinguishes from sibling by specifying it's only when no pipeline matches.

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 says when to use (no match from search_pipelines) and instructs to always call search_pipelines 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?

No annotations are present, so the description must convey behavioral traits. It describes return values but does not mention whether the tool is read-only, whether it involves any external calls, or if there are any side effects. Given that it's a stats tool, it's likely safe, but transparency is partial.

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 three sentences, no fluff, front-loaded with purpose and return details. Every sentence is necessary and effective.

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 output schema, the description adequately explains the return structure and provides example use cases. However, it does not mention any potential latency or caching behavior, which would be helpful for an agent planning 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?

Tool has zero parameters, so the description's mention of return fields adds value beyond the empty input schema, providing semantic meaning for the expected output.

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 it provides stats about the MCP catalog, lists return fields, and distinguishes from sibling tools which focus on details or searches rather than catalog-level statistics.

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 provides usage guidance with example queries like 'how many MCP servers exist', which helps an agent decide when to invoke 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 carries the full burden. It describes return fields but does not explicitly state that the operation is read-only or non-destructive. While the name implies a retrieval operation, the description could be more transparent about side effects and safety.

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 and well-structured: a summary sentence, Args, Returns, and usage guidance. Every part adds value without redundancy, and it is front-loaded with the core 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 simplicity of the tool (one parameter, no output schema), the description fully explains the parameter, the return fields, and the usage context. It provides a comprehensive understanding of what the tool offers and when 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 input schema has only one parameter (slug) with no description. The description compensates by explaining that slug is from search_ai_tools results and provides examples ('chatgpt', 'midjourney'). This adds significant meaning beyond the schema, though format constraints are not specified.

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 full details for a specific AI tool, including features, pricing, target audience, and USP. It explicitly differentiates from sibling tools like search_ai_tools by mentioning slug from search results, making the purpose distinct.

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 advises using the tool when the user wants deeper info before signing up, such as pricing breakdown and target audience. It implicitly guides that slug comes from search_ai_tools, but does not explicitly exclude other contexts or list 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?

No annotations are provided, so the description carries full burden. It clearly states the operation returns a full record with specific fields. It does not mention any side effects, but being a read operation, this is sufficient. More explicit read-only indication would improve it.

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, front-loads the purpose in the first sentence, and uses a structured docstring format. Every sentence adds value: purpose, args, returns, and usage 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?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers the return fields and usage context. It could improve by noting that slug comes from search results, but it is 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?

The schema has 0% description coverage for the single parameter 'slug'. The description only restates 'slug: MCP server slug' in the Args section, adding no semantic meaning beyond the parameter name. For a low-coverage schema, the description should explain what a slug is or how to obtain 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 specifically states 'Get complete details for a specific MCP server', clearly identifying the verb and resource. It distinguishes from siblings like search_mcp_servers and get_mcp_install_config by focusing on comprehensive server details.

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 guides when to use: 'Use this when the user wants to learn more about a specific MCP before installing, or when they ask...'. This provides clear context and contrasts with sibling search tools.

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 discloses behavior: returns dict with config (may be null if missing), available_clients, install_command, page_url. It transparently explains the fallback behavior of trying different clients.

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 summary, args, returns, and usage tip. Every sentence adds value, though slightly verbose. Could be trimmed but remains efficient.

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?

Thoroughly covers inputs, outputs, edge cases (null config), and real-world usage guidance (path to config file). No output schema or annotations, but description compensates fully.

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?

Despite 0% schema coverage, description explains both parameters: slug (MCP server slug from search, with example) and client (list of values, default). Adds meaning beyond the minimal schema definition.

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 'Get a ready-to-paste install config for a specific MCP server and client,' using a specific verb and resource. It distinguishes from sibling tools like get_mcp_details or search_mcp_servers by focusing on configuration retrieval.

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 says 'Use this AFTER finding an MCP via search,' providing clear context for when to invoke. It also advises on showing the config snippet and config file path. Lacks explicit 'when-not-to-use' but the context 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?

With no annotations, the description carries full burden. It lists what is returned but does not disclose error handling, performance, or authentication needs. For a read operation, it is adequate but not exemplary.

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 lead sentence, argument breakdown, return list, and usage note. It is reasonably concise, though the return fields could be summarized more succinctly.

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 low complexity (2 params, no enums, no output schema), the description explains the return structure in detail and provides usage context. It lacks mention of potential errors but is sufficient for the tool's scope.

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?

Despite 0% schema coverage, the description adds meaning by explaining the slug originates from search_pipelines and that lang accepts 'en' or 'ru' with a default, compensating for the schema 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 clearly states it retrieves full pipeline details including title, goal, steps, tools, instructions, prompts, and pricing, distinguishing it from sibling tools like search_pipelines which return a 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?

The description specifies the slug should come from search_pipelines and indicates the tool is for providing a complete actionable plan, implying it's used after selecting a pipeline. It does not explicitly exclude other scenarios, but guidance is clear.

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?

Although no annotations are provided, the description explains the return shape (same as search_pipelines) and sorting (by hits descending), giving essential behavioral context. It does not mention side effects or limitations, but for a read-only tool, 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 compact and well-structured: a one-line summary, bullet-style parameter explanations, a note on return format, and a usage hint. 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 simplicity of the tool (2 parameters, no output schema, no annotations), the description covers the core functionality and usage well. It lacks some peripheral details like error handling or authentication, but these are not critical for this read operation.

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?

With 0% schema description coverage, the description fully compensates by explicitly documenting allowed values for lang ('en' or 'ru') and the range for limit (1-25), along with defaults. This adds significant meaning beyond the bare 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?

Description clearly states the tool returns the top-N most-used pipelines by hit count, which is a specific verb+resource combination. It distinguishes itself from sibling tools like search_pipelines by mentioning it provides inspiration and has a different 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?

Description includes explicit usage guidance, telling users to use it for inspiration ('what can I do with AI?', 'show me popular AI workflows'), and implies it's an alternative to search_pipelines. It does not explicitly state when not to use it, but the context is clear.

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 the tool returns full content, lists return fields, and specifies that slug comes from search. It implies read-only behavior and does not mention side effects. Adequate transparency.

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?

Extremely concise: front-loaded summary, then param docs, then return fields, then usage. Every sentence adds value. No redundant or filler text.

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 all necessary aspects: what it does, parameters, return values, and usage context. Given the simple nature and absence of output schema, the description is fully 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?

Schema coverage is 0%, so description compensates. Explains slug as 'Tutorial slug (from search_mcp_tutorials)' and lang as 'en | ru. Default: en.' Adds meaning beyond schema types and defaults. Could mention that lang controls language of content, but still clear.

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?

Clearly states it retrieves full tutorial content (markdown) with steps, code, screenshots. Explicitly distinguishes from search_mcp_tutorials by saying 'Use this AFTER finding a relevant tutorial via search.' Purpose is specific and 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?

Provides explicit usage instruction: 'Use this AFTER finding a relevant tutorial via search.' Also explains how to use the content (render directly or extract commands/config). No explicit when-not-to-use, but the context makes it clear.

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 describes the return structure but does not disclose potential authentication needs, rate limits, or behavior when no categories exist. Adequate but could be more 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 concise with two sentences and a return format note, all front-loaded with the main action. 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?

For a simple listing tool with no parameters and no output schema, the description covers the purpose, return structure, and usage context. It could mention ordering of categories, but overall it is nearly 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?

The tool has no parameters, so schema coverage is 100%. The description adds no parameter-specific meaning, but the baseline for 0 parameters is 4. The return format description provides useful context 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 clearly states it lists AI tool categories with counts, and the name distinguishes it from sibling 'list_mcp_categories' which lists MCP categories. It is a specific verb+resource combination.

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 states it is useful when the user wants to browse tools by category, providing clear context for when to use. It does not mention when not to use or contrast with siblings, but the guidance is adequate.

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 clearly describes the return format (Dict with count and categories list). Since the tool is read-only and has no parameters, the description adequately covers its behavior without omissions.

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: two short paragraphs. The first sentence states the purpose, the second describes the return format, and the third gives usage context. Every sentence adds value, and it is well-structured.

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 tool with no parameters and no output schema, the description is complete. It covers what the tool does, what it returns, and when to use it. No additional context is necessary.

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 are zero parameters, so schema coverage is 100% by default. The description does not need to add parameter information. Baseline for no parameters is 4.

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 'List all MCP categories in the catalog with counts.' It uses a specific verb and resource, and the return structure (count and categories) is explicitly defined. This distinguishes it from sibling tools like search_mcp_servers or get_mcp_details.

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 context on when to use the tool: 'Useful when the user wants to browse by category or asks "what kinds of MCP servers exist".' While it does not explicitly mention when not to use or alternative tools, the context is clear and sufficient for an 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 provided, but description details return format (dict with count and results, each result fields) and search behavior. It lacks mention of auth or rate limits, but for a search tool this is 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?

Description is concise, front-loaded with purpose, then structured Args and Returns sections. No irrelevant sentences.

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, description covers all aspects: parameters, return format, and usage guidance with sibling relation. Complete for its complexity.

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 0%, but description adds meaning for all 4 parameters: query with example, category with examples, pricing with values, limit with range and default. This fully compensates for schema gaps.

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 searches 15000+ AI tools in a specific catalog, with examples and a specific verb (search). It distinguishes from sibling get_ai_tool_details by advising follow-up usage.

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 says when to use the tool (user asks for AI tool for a specific task) and suggests follow-up with get_ai_tool_details. No explicit when-not, but guidance is clear.

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 description carries full burden. It discloses behavior: returns dict with count and results, lists result fields. Provides parameter examples. Does not mention destructive actions or auth, but for a search tool 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?

Well-structured with clear sections: purpose, Args with examples, Returns, usage context. Every sentence adds value. No redundancy. Appropriate length for a search tool with 5 parameters.

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 5 parameters, no enums, and no output schema, the description is highly complete. Covers all parameters, return format, and usage context. Examples for query, category values, and client values are provided. Missing only edge case details but overall excellent.

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%, but description adds extensive meaning: explains each parameter with examples, lists common values for category, client, language, and specifies default for limit. This far exceeds minimal requirement.

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 searches a catalog of 3500+ MCP servers at thedailyworkflow.com. It uses specific verb 'search' and resource 'catalog of MCP servers', distinguishing it from sibling tools like search_ai_tools or search_pipelines.

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 says when to use: 'when the user asks for an MCP server matching some need' and gives examples. Provides post-search action: 'call get_mcp_install_config'. Does not explicitly state when not to use, but context is clear.

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 explains the curated nature, parameter behavior, and return structure. While it doesn't explicitly state read-only, a search tool is inherently non-destructive; the description is transparent enough.

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 an opening sentence, then Args and Returns sections, and usage guidance. It is slightly verbose but all sentences are informative; front-loading the purpose is effective.

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 5 parameters and no output schema, the description provides full coverage: parameter details, return format, examples, and follow-up instruction. It also notes tutorial count and categories, making the tool's scope clear.

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?

With 0% schema description coverage, the description thoroughly explains each parameter's meaning, type, and example values (e.g., query examples, category and difficulty lists, default for lang and limit). This fully compensates for missing schema documentation.

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 searches 147+ MCP tutorials across install, configuration, comparison, and build guides. It provides specific verb and resource, and differentiates itself from siblings like 'get_tutorial_details' and 'search_qa_cases'.

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 states when to use this tool with example queries ('how do I install X MCP', 'best MCPs for Cursor', etc.) and recommends following up with 'get_tutorial_details' for full content.

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?

Describes return format and structure, but lacks mention of error handling or side effects. Acceptable for a search 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?

Front-loaded purpose, then args and returns. Each part 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?

Covers purpose, usage, parameter semantics, and return format completely for an agent to use, despite no output schema.

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?

Provides examples for query, explains lang and limit with defaults. Compensates well for 0% schema description coverage.

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?

Clearly states it searches ready-made AI workflow pipelines with examples. Distinguishes from sibling tools like get_pipeline_details and catalog_stats.

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 says when to use (multi-step AI goals) and when to switch to get_pipeline_details for full 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 are provided, so the description carries the full burden. It explains the tool is a read operation (search) and describes the response structure, including a preview field. It doesn't mention any side effects, but as a search tool, that's acceptable.

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 structured with clear sections (Args, Returns, usage guidance). Every sentence adds value, and there is no redundancy or fluff. It is appropriately sized for a search tool.

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 4 parameters, no output schema, and sibling search tools, the description is complete. It explains parameters, return format, and usage context, covering all necessary information for an agent to select and invoke 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%, but the description provides detailed meanings for all 4 parameters, including examples and value lists for ai_tool, category, and limit. This significantly compensates for the lack of 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 the tool searches '1700+ user-submitted AI prompts' for multiple AI tools, specifying the verb and resource. It distinguishes from sibling tools like search_ai_tools by focusing specifically on prompts.

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 when the user asks "give me a prompt for X" or "find a prompt to do Y"', providing clear context. It doesn't state when not to use it, but the guidance is specific 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?

No annotations are provided, but the description fully discloses behavior: it is a search tool that returns a dict with count and results, each result containing specific fields. No side effects or destructive actions are implied, and the return format is fully specified.

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 fairly long but well-organized into sections (usage, args, returns). It front-loads the purpose and provides concrete examples. Some repetition could be trimmed, but it remains clear and informative.

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 output schema, the description fully describes the return structure including fields like slug, title, problem preview, etc. It covers input parameters, usage context, and output format, leaving no critical gaps for an agent to invoke 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%, but the description provides detailed guidance for all 5 parameters: query examples, server slug example, category with allowed values, lang with allowed values, and limit with range. This adds substantial meaning beyond the bare 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 clearly states it searches Q&A cases, which are curated solutions to real GitHub Issues. It specifies the structured format (problem, cause, solution) and distinguishes from sibling tools that search for tools, servers, tutorials, etc.

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 says 'Use this FIRST when the user reports an error or unexpected behavior with an MCP server' and lists common error examples. Does not explicitly state when not to use, but the context is clear and alternatives are implied by the sibling tool names.

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