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

Without annotations, the description provides some behavioral context: it's an AI analysis, free with a daily limit, and not legal advice. However, it does not disclose data handling, storage, or privacy practices, which would enhance transparency for a tool processing user-supplied text.

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 extremely concise: two sentences covering purpose, output types, limitations, and disclaimer. Every word adds value with no redundancy 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?

Given the average complexity (two parameters, no output schema), the description adequately covers input requirements, output types, limitations, and differentiation from sibling tools. It provides enough information for an agent to correctly invoke the tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining the analysis output (red flags, clauses, protections, questions) and the optional focus keyword, providing context beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: analyze contract text for risks, red flags, one-sided clauses, missing protections, and questions. The verb 'analyze' and resource 'contract' are specific, and it distinguishes from sibling tools like check_non_compete_enforceability, which focus on specific legal questions.

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 usage for general risk analysis and mentions free tier limitations and a disclaimer, but does not explicitly specify when not to use this tool or suggest alternatives among siblings. Some context is provided, but not comprehensive guidance.

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

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

No annotations provided, so description carries full burden. It discloses the output categories (enforceable, limited, void) and included data (salary thresholds, statute). Does not mention potential limitations or side effects, but for a read-only lookup this is 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?

Two well-structured sentences, front-loaded with key information. 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 single-parameter tool with no output schema, the description sufficiently covers purpose, input, and output. Mentions specific data points (salary thresholds, statute) but could be more precise about what 'key statute' entails. Overall adequate for the tool's simplicity.

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

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

Schema coverage is 100% with one parameter. Description adds value by providing an example input ('California' or 'california') and clarifying slug format, which goes beyond the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description explicitly states the tool checks enforceability of non-compete agreements by US state, including whether enforceable, limited, or void, along with salary thresholds and key statute. Covers all 50 states + DC, clearly distinguishing it from broader contract analysis tools.

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

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

No explicit guidance on when to use this tool vs. alternatives. While the description implies it's for state-specific non-compete law, it does not provide when-not scenarios or mention related tools like analyze_contract or get_jurisdiction_requirements.

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 discloses that the tool returns a URL for human action, requires a preview_id, and is non-destructive. Missing details on authentication or rate limits, but the operation is simple and 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?

Two sentences with no wasted words. The description is front-loaded with the action and immediately provides the purpose and usage instruction.

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 one parameter, no output schema, and simple operation, the description covers the essential: what the tool does, what input it needs, and what to do with the output. It could mention URL validity but is complete enough for the context.

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

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

Schema coverage is 100% with one parameter already described in the schema. The description does not add extra meaning beyond referencing the preview_id, so baseline 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 states the verb 'Returns' and the resource 'URL', specifies the purpose (view preview and pay to unlock contract), and distinguishes from siblings by explicitly referencing start_contract_draft as a prerequisite.

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 usage after creating a draft with start_contract_draft and instructs to share the link with the user. It does not explicitly state when not to use or list alternatives, but the context is clear among siblings.

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 provides essential behavioral details: it returns 'pending' while generating, and upon completion returns free preview, locked section titles, and a URL for unlocking. This covers the key states and outputs, though it omits error handling, timeouts, or authentication requirements. It is transparent enough for a simple status poll.

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 efficiently packs prerequisite, behavioral states, and return values without redundancy. It is front-loaded with the action and resource, making it easy to scan.

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

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

For a tool with no output schema, the description covers what the agent needs: the prerequisite (start_contract_draft), the status response, and the components of the full preview when complete. It could be improved by noting potential error conditions or status values beyond 'pending' and 'complete', but it is satisfactory for common use cases.

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% (the single parameter 'preview_id' is described). The description does not add new semantics beyond the schema's description; it merely restates that the ID comes from start_contract_draft. Therefore, the description does not significantly enhance parameter understanding beyond what the schema already provides.

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: checking the status of a contract draft started with a specific sibling tool (start_contract_draft). It distinguishes itself from siblings like analyze_contract and get_checkout_link by focusing on the draft generation process and what it returns (pending status, free preview, locked sections titles, unlock URL).

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 mentions when to use this tool ('Check a contract draft started with start_contract_draft'), providing a clear prerequisite. It covers two states (during generation and when complete), which guides usage. However, it does not explicitly state when not to use it or mention alternatives, which would improve the score.

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 burden. It discloses that the tool returns fields, types, options, and validation rules, which gives insight into the output structure. However, it does not explicitly state that the tool is read-only or describe any side effects, though for a question retrieval tool these are likely 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 with no extraneous information. It is front-loaded with the core purpose and adds essential context about usage and return content 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 the tool's simplicity (two parameters, no nested objects, no output schema), the description is complete. It explains the tool's purpose, its place in a workflow, and the nature of the returned data, which is sufficient for an agent to use it 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 coverage is 100% with each parameter described in the input schema. The tool description does not add additional parameter-level meaning beyond the high-level context, so 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 retrieves intake questions for a specific contract type, explicitly links it to the drafting workflow by mentioning use before start_contract_draft, and distinguishes it from siblings like list_contract_types and start_contract_draft.

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 the tool ('before calling start_contract_draft') and implies its role in collecting prerequisite information. It does not provide exclusions or alternative tools, but the context makes usage 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 full burden. It discloses the type of information returned (requirements, pitfalls, etc.), which implies a read-only lookup. However, it does not explicitly confirm no side effects, auth needs, or rate limits, leaving some ambiguity.

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 concise sentences: the first defines the output, the second specifies how to call it with slug examples. No unnecessary words, front-loaded with key 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 simple lookup tool with two string parameters and no output schema, the description covers the return content and usage adequately. It does not mention limitations or pagination, but the tool's complexity is low, so 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?

Schema coverage is 100%, so the schema already describes both parameters. The description adds context by showing example values for contract_slug and jurisdiction_slug, but does not add new constraints or format details. Baseline 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 returns statute-cited legal requirements, key differences, common pitfalls, terminology, and FAQs for a contract type in a specific jurisdiction. It provides concrete examples of slugs, distinguishing it from sibling tools like analyze_contract or check_non_compete_enforceability.

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 instructs to use contract slug and jurisdiction slug with examples. It implies when to use (for legal requirements), but does not explicitly state when not to use or list alternatives. Clear context but no exclusions.

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

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns descriptions, use cases, and prices. For a read-only list with no parameters, this is sufficient transparency, though it omits potential pagination 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?

Two sentences, both front-loaded with action and value. Every word earns its place; no redundancy or filler.

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 parameters and no output schema, the description adequately explains what the tool returns and its purpose. It could mention ordering or pagination, but for a likely small set, it's sufficient.

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 no parameters (0 params), so schema_description_coverage is effectively 100%. Baseline for 0 params is 4; the description doesn't need to add parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all contract types with descriptions, use cases, and prices, and positions it as the starting point for finding slugs/IDs. This distinguishes it from siblings like analyze_contract or start_contract_draft.

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 'Start here to find the right contract type slug/id for other tools,' indicating it's a prerequisite step. While no when-not guidance is given, the context implies it's the primary discovery 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 provided, so description carries full burden. Discloses multi-agent AI engine, 3-5 minute generation time, immediate preview_id return, free preview only, full contract unlocked via human checkout. Fully informs agent of behavioral traits.

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, starts with action, covers key details. Could be slightly more structured, but no wasted words. 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?

Given complexity (multi-agent AI, preview vs full, polling needed), description covers all essentials: prerequisites, return value, workflow, limitations. No output schema, but explains preview_id and subsequent polling. Complete for agent to use 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 covers all 3 parameters (100% coverage). Description adds value: specifies that deal_summary fields come from get_intake_questions, provides example format, and notes that contract_type ids are from list_contract_types. Goes beyond schema alone.

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 'Start generating a contract draft' with specific verb and resource. Distinguishes from siblings like get_intake_questions (data collection) and get_draft_status (polling).

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 to provide deal_summary fields collected via get_intake_questions and mentions contract_type from list_contract_types. Implicitly indicates when to use (after intake, before polling). No explicit when-not, 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.