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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe, non-destructive operation. The description adds valuable behavioral context beyond annotations: it explains the tool's signature approach ('THINKS like a consultant'), lists available frameworks with brief explanations, and describes the return format ('structured strategic analysis... with real industry benchmarks, quotes from leaders, and specific recommendations'). This compensates for the lack of an output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose. Most sentences earn their place by explaining the tool's unique value, listing frameworks, and detailing returns. However, the bullet-point list of frameworks is slightly verbose, and the final sentence on returns could be more concise, preventing a perfect score of 5.

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 (applies frameworks to business problems) and the absence of an output schema, the description does a good job of explaining what the tool returns. However, it could be more complete by mentioning potential limitations (e.g., data recency, scope of industry benchmarks) or error cases, which would help the agent use it more effectively in varied contexts.

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 the schema already documents all parameters well. The description adds some semantic value by listing and briefly explaining the available frameworks and noting that custom_axes is 'For 2×2 only,' but it doesn't provide significant additional meaning beyond what's in the schema (e.g., no examples for situation or role). This meets the baseline of 3 for high schema 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?

The description clearly states the tool's purpose: 'Apply a McKinsey-style consulting framework to a business problem using real estate industry data.' It specifies the verb ('apply'), resource ('framework'), and domain ('real estate industry data'), and distinguishes it from siblings by emphasizing it 'doesn't just search transcripts — it THINKS like a consultant,' contrasting with tools like mikensey_search or mikensey_get_frameworks.

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 clear context for when to use this tool: for strategic analysis of business problems in real estate using consulting frameworks. It implicitly suggests alternatives by mentioning it doesn't just search transcripts, but it doesn't explicitly name when to use sibling tools like mikensey_get_advice or mikensey_get_benchmarks instead, which would be needed for a score of 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?

Annotations indicate readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds context about the output format ('SCR-structured strategy brief with benchmarks, evidence, and specific recommendations') and data grounding ('real industry data from 53 podcast episodes'), which provides useful behavioral insights beyond the annotations. However, it doesn't mention potential limitations like response time, data freshness, or error conditions.

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 and front-loaded: the first sentence states the core purpose, followed by elaboration on the SCR framework and tool differentiation. Every sentence adds value—explaining the output format, distinguishing from siblings, and summarizing parameters and returns—with no wasted words. It efficiently balances detail with brevity.

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 (generating strategic advice), annotations cover safety (read-only, non-destructive), and the schema fully describes parameters, the description provides good context: it explains the SCR framework, data source, output format, and sibling differentiation. However, without an output schema, it could benefit from more detail on the return structure (e.g., format specifics like sections or length), though it does mention 'benchmarks, evidence, and specific recommendations.'

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 clear descriptions for each parameter (e.g., 'Your role in the industry' for 'role'). The description adds minimal value beyond the schema: it restates 'role' and 'situation' similarly, and mentions 'topic' as 'optional focus area' without additional semantics. Since the schema already documents parameters well, the 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: 'Get a McKinsey-style strategy brief structured as Situation-Complication-Resolution (SCR), grounded in real industry data from 53 podcast episodes.' It specifies the verb ('Get'), resource ('strategy brief'), format ('SCR'), and data source ('53 podcast episodes'), and distinguishes it from the sibling 'mikensey_analyze' by noting this is for 'executive-ready output' versus 'deeper analysis with specific frameworks'.

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

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

The description provides explicit guidance on when to use this tool versus alternatives: 'This is the executive-ready output tool... For deeper analysis with specific frameworks (2×2, Issue Trees, Porter's, etc.), use mikensey_analyze instead.' It clearly defines the context (generating a complete strategy memo) and names a specific alternative tool, helping the agent choose appropriately.

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?

Annotations already indicate this is a read-only, non-destructive, idempotent operation with a closed-world dataset. The description adds valuable context beyond annotations by specifying the data source ('podcast conversations'), content types (e.g., 'specific numbers cited by industry leaders'), and return format ('Benchmark data with metric name, value, context, and source'), enhancing the agent's understanding without contradictions.

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 and front-loaded, starting with the core purpose, followed by key details, arguments, returns, and examples. Each sentence adds value without redundancy, such as specifying data content and usage scenarios, making it efficient and easy to parse for an agent.

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 (simple query-based retrieval), rich annotations (covering safety and behavior), and lack of output schema, the description is mostly complete. It explains the purpose, data source, return format, and usage examples. However, it could improve by detailing potential limitations (e.g., data recency) or error cases, slightly reducing completeness.

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 100% description coverage, with the query parameter documented as 'Filter benchmarks by keyword'. The description adds minimal value beyond this, repeating the filtering purpose and providing examples (e.g., 'mortgage', 'agent'), but does not clarify syntax or constraints. With high schema coverage, 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: 'Look up real estate, mortgage, and proptech industry benchmarks and metrics from podcast conversations.' It specifies the verb ('look up'), resource ('benchmarks and metrics'), and source ('podcast conversations'), distinguishing it from siblings like mikensey_get_episode or mikensey_get_frameworks by focusing on quantitative data rather than episodes or advice.

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 clear context on when to use this tool: for accessing benchmark data from podcasts, with examples like 'attach rate' or 'cost'. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., mikensey_search might overlap), leaving some ambiguity in tool 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?

The annotations already indicate this is a read-only, non-destructive, idempotent operation with a closed world. The description adds context about the tool's purpose ('deep-dive into a specific conversation') and the need to find episode IDs first, which provides useful behavioral insight beyond the annotations. It doesn't contradict annotations, but could mention more about rate limits or error handling.

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 and front-loaded with the core purpose, followed by usage guidelines and parameter details. Every sentence serves a clear purpose without redundancy, making it efficient and easy to parse for an AI agent.

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 annotations cover safety and idempotency, and the schema fully describes parameters, the description provides good context about usage and purpose. However, without an output schema, it could benefit from more detail on return values (e.g., format of 'metadata' mentioned). It's mostly complete but has a minor gap in output clarification.

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 the schema already fully documents all parameters. The description adds minimal value by listing parameters with brief examples (e.g., episode slug format) and default values, but doesn't provide significant additional semantics beyond what's in the schema. This meets the baseline for high schema 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?

The description clearly states the specific action ('Read the full or partial transcript') and resource ('of a specific podcast episode'), distinguishing it from siblings like mikensey_list_episodes (which lists episodes) or mikensey_search (which searches content). It explicitly mentions what the tool does beyond just the title.

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

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

The description provides explicit guidance on when to use this tool ('Use this to deep-dive into a specific conversation') and when not to use it ('Use mikensey_list_episodes or mikensey_search first to find the episode id'), naming specific alternatives. This helps the agent understand the proper workflow and tool 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, indicating it's a safe, read-only, idempotent operation with a closed world. The description adds valuable context beyond this: it explains what types of frameworks are available (Industry vs. Consulting with examples), clarifies the return format (name, description, when to use, examples), and mentions filtering capabilities. No contradiction with annotations exists.

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 bullet points for framework categories, a clear Args section, and a Returns statement. Every sentence adds value: it explains the two framework types with examples, documents parameters, specifies return format, and provides usage guidance. 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?

Given the tool's complexity (browsing frameworks with filtering), rich annotations (covering safety and behavior), and 100% schema coverage, the description is complete. It explains what the tool does, when to use it, what it returns, and how it differs from siblings. No output schema exists, but the description adequately describes return values.

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 both parameters having clear enum values and descriptions. The description adds some semantic context by explaining that 'audience' filters industry frameworks by target audience and 'type' filters by framework category, but this largely repeats what's in the schema. Baseline 3 is appropriate as the schema does most of the work.

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: to browse two specific categories of frameworks (Industry Frameworks and Consulting Frameworks) available in Mikensey. It distinguishes this from sibling tools by specifying it's for browsing frameworks, not applying them (mikensey_analyze) or getting other content like advice, benchmarks, or episodes.

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 when to use this tool vs. alternatives: 'To actually APPLY a consulting framework to your specific situation, use mikensey_analyze.' This provides clear guidance on when to use this tool (for browsing frameworks) versus when to use a sibling tool (for applying frameworks).

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?

Annotations already cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true), but the description adds valuable context: it specifies the return format (episode list with id, guest, title, date, word count) and mentions pagination behavior via offset, which annotations don't provide.

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 front-loaded with the core purpose, followed by usage guidance and parameter details. Every sentence adds value: the first defines the tool, the second gives usage context, and the parameter/return sections provide essential operational info 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 read-only list tool with good annotations and no output schema, the description is complete: it explains what the tool does, when to use it, parameters, and return values. It compensates for the lack of output schema by specifying the return format, making it sufficient for agent 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 description coverage is 100%, so the schema already documents all parameters well. The description adds minimal value beyond the schema by listing parameters with basic explanations, but doesn't provide additional syntax or format details. Baseline 3 is appropriate given high schema 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?

The description clearly states the verb ('List') and resource ('all available podcast episodes') with specific attributes (guest names, dates, topics). It distinguishes from siblings like 'mikensey_get_episode' (singular) and 'mikensey_search' (likely more advanced filtering).

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

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

It provides clear context for when to use ('browse what's available or find a specific episode'), but doesn't explicitly state when NOT to use it or name specific alternatives among siblings like 'mikensey_search' for more complex queries.

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?

The annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, so the agent knows this is a safe, repeatable search operation. The description adds valuable context about what content is searched (podcast transcripts), what domains it covers (real estate, mortgage, proptech), and what the returns include (excerpts with episode info, guest name, quotes), which goes beyond the annotations. No contradiction with annotations exists.

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 efficiently structured with a clear purpose statement, usage guidance, parameter overview with examples, and return value description - all in 6 sentences with zero wasted words. It's front-loaded with the core functionality and provides just enough detail 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 search tool with good annotations and full schema coverage, the description provides excellent context about content scope, use cases, and return format. The only minor gap is the lack of an output schema, but the description adequately describes what's returned. It could slightly improve by mentioning search limitations or result ordering, but it's largely complete.

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

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

Schema description coverage is 100%, with both parameters well-documented in the schema (query as search terms with length constraints, limit as maximum results with default and bounds). The description adds minimal value beyond the schema by providing example queries and mentioning the default limit, but doesn't explain parameter interactions or advanced usage. Baseline 3 is appropriate when schema does the heavy lifting.

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 across Mike DelPrete podcast transcripts for specific content types (topics, quotes, metrics, advice) and distinguishes it from siblings by specifying it's for searching transcripts rather than analyzing, getting advice/benchmarks/episodes/frameworks, or listing episodes. The verb 'search' is specific and the resource 'Mike DelPrete podcast transcripts' is well-defined.

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 when to use this tool ('Use this tool to find what industry leaders have said about specific topics in real estate, mortgage, and proptech') and provides three concrete examples of search queries with their expected outcomes. This gives clear guidance on appropriate use cases versus the sibling tools which serve different purposes like analysis or retrieval of specific content types.

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