Built with Jon — Hidden Profit Tools

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

No annotations provided, so description carries burden. It discloses use of specific math and fallback to defaults. Lacks explicit statement on safety (read-only), side effects, or return format.

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 purpose, no unnecessary words. Clear and 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?

Covers purpose, usage, and fallback. Missing output format (e.g., currency, monthly vs annual control). For a calculation tool with nested inputs, more detail on return value would be helpful.

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?

Adds value beyond schema: explains that missing inputs use defaults and are flagged. Schema only describes inputs as numbers keyed by field names; description clarifies fallback behavior.

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 computes a dollar figure for an operational leak using specific math. Distinguishes from list_leak_calculators but not from run_scorecard.

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 call: when user wants cost of specific leak types. Mentions fallback behavior for missing inputs. No explicit 'when not to use' but still helpful.

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

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

With no annotations provided, the description carries full responsibility. It discloses that markdown text is included only 'when available', acknowledging data uncertainty. However, it does not mention if the tool is read-only, requires authentication, or has 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?

A single, front-loaded sentence that efficiently conveys purpose, input, and output details. Every part is essential, and the information is directly actionable.

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 retrieval tool with one parameter and no output schema, the description covers input source, output content, and data availability. It is nearly complete but could briefly mention output format or any prerequisites (e.g., authentication).

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 covers 100% of parameters with a clear description and example. The description adds value by linking the slug to 'search_articles', providing context beyond the schema's static example.

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 'Get one article by slug', specifies the source ('from search_articles'), and lists what is returned (canonical URL and full markdown text). It effectively distinguishes the tool from siblings by referencing the slug source.

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 implies that slugs should come from 'search_articles', offering a contextual hint. However, it does not provide explicit guidance on when to use or avoid this tool compared to other siblings like 'get_use_case' or 'get_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?

Description indicates a read operation ('Get') and lists content, but no annotations are present. Does not disclose subtle behaviors like filtering or side effects, which are minimal for this 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?

Two sentences, no waste, front-loaded with purpose. Could add parameter info 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?

No output schema or annotations, but tool is simple. Missing parameter explanation is a gap; otherwise adequate for static framework retrieval.

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%, yet the description does not mention the 'section' parameter or its enum values at all, failing to add meaning 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 retrieves specific named frameworks (Five Dispositions and 5-phase process) and the context of decision automation. No sibling tool has a similar 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?

Provides explicit usage context ('Call when discussing how to decide what to automate') but lacks exclusionary guidance or 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 provided, so the description carries full burden. It indicates the tool fetches details (non-destructive) and lists return contents (deliverables, worked sample, etc.), but does not disclose any authentication requirements, rate limits, or side effects. Adequate but not fully 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?

Two sentences, no filler, front-loaded with the key action and context. Every sentence contributes 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?

Despite having no output schema, the description lists what it returns (deliverables, worked sample, etc.), which suffices for a zero-parameter tool. It is complete for its intended 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?

The tool has zero parameters, so schema coverage is 100%. The description adds no param info, but none is needed. Baseline score of 4 applies for no parameters.

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 retrieves details of the Hidden Profit Review, specifying it's Jonathan Malkin's paid review of a recurring workflow. This distinct purpose sets it apart from sibling tools like 'calculate_leak' or 'get_article'.

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 'Call when a user wants professional help finding or fixing a profit leak,' providing a clear usage context. However, it does not mention when not to use it or suggest alternatives, leaving slight room for misinterpretation.

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 the description thoroughly discloses the return content (pain, workflow, verdicts, AI fit, after state). It doesn't mention permissions or side effects, but as a read operation, 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?

Concise single sentence packed with all key information, no redundancy. Front-loaded with the action and core content.

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?

No output schema, but description fully describes the return content. For a single-item retrieval tool with one parameter, it is adequately 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 coverage is 100%, so baseline is 3. The description adds value by specifying ID format ('A1' or 'S3'), which is more concrete than the schema's 'e.g. A1'.

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 single use case in full, listing its components (pain, workflow, per-step verdicts, etc.), and distinguishes from sibling tools like search_use_cases which likely return summaries.

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 explicitly says when to use this tool (to get a full use case). While it doesn't explicitly state when not to use, the sibling context implies using search_use_cases for lists, providing sufficient 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 are provided, so the description carries the full burden. It describes the output (list of calculators with fields) but doesn't explicitly state it's a read-only, non-destructive operation. However, the nature of listing is implicitly safe, and the description adds value by naming the calculators.

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 a single sentence that immediately states the purpose, lists the calculators, and provides a usage hint. It is front-loaded and contains no superfluous information.

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 parameters, no output schema, and a simple purpose, the description is fully complete. It tells the agent what the tool does, what it returns, and how it fits into the workflow.

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 zero parameters, and schema description coverage is 100%. The scoring guideline sets a baseline of 4 for no parameters. The description adds no parameter-specific detail, but none is needed.

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 exactly 10 specific leak calculators with their input fields and defaults. It uses 'list' as a specific verb and identifies the resource (leak calculators). This distinguishes it from siblings like 'calculate_leak' which performs calculations.

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 before calculate_leak,' providing clear context on when to invoke this tool. While it doesn't mention when not to use, the guidance is sufficient for a simple listing 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 bears full burden. It discloses that the tool is a list operation with grouped results and stats, which is transparent. No destructive or complex behaviors need explanation.

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, no wasted words. The key action ('List the 15 categories...') is front-loaded, and the usage hint is appended efficiently.

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 zero parameters, no output schema, and a simple list operation, the description is fully complete. It tells the user what they get (grouped categories with counts) 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 zero parameters, so the baseline is 4. The description adds extra context about grouping and stats, which is helpful but not required given the empty 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 uses a specific verb ('List') and resource ('categories of the Built with Jon Use Case Library'), and includes details about grouping (business function, industry, personal) and output (counts, key stats). It clearly distinguishes from sibling tools like 'search_use_cases' that are for searching.

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 'Call this first to orient before searching', providing clear when-to-use guidance. While it does not explicitly state when not to use it, the context implies that after orientation, one should switch to 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?

No annotations are provided, so the description carries the full burden. It states the tool runs a deterministic scorecard and returns a verdict, but it does not disclose any behavioral traits such as side effects, data storage, or required permissions. There is no contradiction, but the information is minimal.

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 core purpose, and every phrase adds value without redundancy. It is appropriately concise for the tool's simplicity.

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 high schema coverage, required parameters, and no output schema, the description is sufficient. It explains the tool's purpose, process (conversational + call), and outcome. The agent has enough to decide when to use it. A minor gap is not describing the exact output structure, but the scoring verdict is implied.

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%, but the description adds value by clarifying the conversational ordering ('ask the user these questions') and explaining that optional parameters (q4, q5, q9) 'flavor the reading, does not affect the score'. This goes beyond the schema's basic 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 runs a 3-minute AI Workflow Scorecard based on user answers, produces a scored verdict on business leaks and first fix, and references the deterministic scoring from a known website. It distinguishes itself from siblings like calculate_leak and get_articles by being the specific scorecard function.

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

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

The description advises to 'ask the user these questions conversationally, then call this with their answers', providing clear usage context. It does not explicitly list when not to use or alternatives, but the context of sibling tools implies this is for the scorecard execution only.

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. Description does not disclose behavioral traits beyond basic functionality (returns titles, descriptions, slugs). For a read-only search tool, this is acceptable but not exceptional.

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 contain all essential information: purpose, parameters with filters, and return fields. No extraneous 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 scale (50 articles), topics, parameter behavior, and return fields. Lacks mention of pagination or sorting, but overall provides sufficient context for a search tool with 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?

Schema coverage is 100%, and description adds meaningful details: 'Exact tag match, case-insensitive' for tag, 'Max results, 1-20 (default 10)' for limit, and clarifies query matches title, description, tags, and body.

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 searches articles about specific topics, with a specific resource (Jonathan Malkin's ~50 articles). It mentions relation to get_article but does not explicitly contrast with search_use_cases or other siblings, though the context is clear.

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 implies usage context (searching articles by keyword/tag) but does not specify when to prefer this tool over alternatives like get_article, or provide when-not-to-use 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 are provided, and the description does not disclose behavioral traits such as rate limits, sorting, pagination, or whether it is read-only. However, it is implied to be a search operation with multiple filters.

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: first states purpose and scope, second provides usage context and filter options. No redundant or irrelevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for a search tool but lacks details on return format or behavior when no results are found. Without an output schema, some behavioral context is missing.

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

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

Schema coverage is 80%, and the description adds a list of filter dimensions but does not provide additional semantic context beyond what is in the schema. 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 searches a '96-entry Use Case Library of small-business AI and automation workflows' and provides specific examples of when to use it, distinguishing it from siblings like 'get_use_case' and 'list_use_case_categories'.

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 gives clear guidance on when to call the tool ('when a user describes a recurring business pain') with concrete examples, but does not explicitly mention when not to use it or 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?

The description describes what the tool does (explains, returns information) without needing annotations. It is transparent about being an informational guide, with no destructive behavior. The output is explained adequately.

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?

A single sentence that is front-loaded with 'START HERE' and concisely covers the tool's purpose, audience, and content. 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 simple onboarding tool with no parameters or output schema, the description fully explains its role and content. 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?

The input schema has 0 parameters, so no parameter information is needed. The baseline for 0 params is 4, and the description adds no param details (correctly).

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 uses 'START HERE' to indicate it's an onboarding tool, and specifies it 'explains every tool, why to use it, what it returns, how the tools work together, and the best first prompt'. This distinguishes it clearly from the sibling tools, which are functional (e.g., calculate_leak, get_article).

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 explicitly says 'if you are new to Built with Jon', which sets the context for use. It does not explicitly state when not to use it, but the alternative is implied to be the other tools once familiar. Clear guidance overall.

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