ServicePal MCP Server

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It describes what the tool does but lacks critical behavioral details: it doesn't specify whether this is a read-only calculation or if it has side effects, doesn't mention performance characteristics like rate limits or latency, and doesn't describe the output format or any assumptions in the calculation model. For a calculation tool with no annotation coverage, this leaves significant gaps.

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, efficient sentence that clearly states the tool's purpose and key inputs. It's front-loaded with the core functionality and wastes no words. Every element earns its place by contributing essential information about what the tool calculates and what factors it considers.

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 annotations and no output schema, the description is incomplete for a calculation tool with 4 parameters. It adequately describes what the tool calculates but fails to address critical contextual elements: it doesn't explain the output format (e.g., numeric result, structured data), doesn't mention calculation methodology or assumptions, and provides no error handling information. For a tool that presumably returns a calculated value, the lack of output information is a significant gap.

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 4 parameters thoroughly. The description adds marginal value by mentioning the same three inputs (industry, operating hours, season) that map to parameters, but doesn't provide additional context beyond what's in the schema descriptions. The baseline of 3 is appropriate when the 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's purpose: 'Calculate how many calls a business misses per week' with specific inputs (industry, operating hours, season). It uses a specific verb ('calculate') and resource ('missed calls'), but doesn't explicitly differentiate from sibling tools like 'estimate_revenue_loss' or 'compare_solutions' that might serve related purposes.

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 no guidance on when to use this tool versus alternatives. It mentions the calculation inputs but doesn't specify scenarios where this tool is preferred over siblings like 'estimate_revenue_loss' (which might calculate financial impact) or 'compare_solutions' (which might evaluate different strategies). No exclusions or prerequisites are mentioned.

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 the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it performs the comparison, what factors are considered, whether it's a read-only analysis or has side effects, or what the output format might be. For a comparison tool with zero annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that clearly states the tool's function. It's appropriately sized and front-loaded with the core purpose, though it could potentially benefit from slightly more detail given the lack of annotations and output schema.

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

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

Given the complexity of comparing two service models, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what aspects are compared (cost, efficiency, customer satisfaction?), what the comparison output looks like, or how the analysis is performed. For a tool with this function and minimal structured data, more context is needed.

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 both parameters adequately. The description mentions the parameters ('business type and call volume') but doesn't add meaningful semantic context beyond what the schema provides, such as typical business types or call volume ranges. 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's purpose: comparing AI answering service vs human receptionist for specific business parameters. It specifies the verb 'compare' and the resources being compared, though it doesn't explicitly distinguish from sibling tools like 'calculate_missed_calls' or 'estimate_revenue_loss' which might have related but different functions.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'calculate_missed_calls' or 'estimate_revenue_loss', nor does it specify prerequisites, exclusions, or appropriate contexts for this comparison tool. Usage is implied but not explicitly defined.

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 for behavioral disclosure. It states the tool 'estimates' revenue loss, implying a read-only calculation, but doesn't clarify if it's deterministic or heuristic, what assumptions it makes, or if it requires specific permissions. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that front-loads the core purpose without unnecessary words. Every element (verb, resource, parameters) earns its place, making it easy to parse quickly.

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 (estimation with three parameters), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and parameters but lacks details on behavior, assumptions, or return values. For a tool with no structured safety or output information, more context would be beneficial.

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 three parameters. The description mentions the parameters (industry, call volume, job value) but adds no additional meaning beyond what's in the schema, such as parameter relationships or calculation methodology. Baseline 3 is appropriate when the 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's purpose: estimating revenue loss from missed calls using specific parameters (industry, call volume, job value). It specifies the verb 'estimate' and resource 'revenue lost from missed calls', making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'calculate_missed_calls' or 'compare_solutions', which might have overlapping domains.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions. With sibling tools like 'calculate_missed_calls' and 'compare_solutions' available, the lack of differentiation leaves the agent uncertain about tool selection in related scenarios.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool creates a demo URL but lacks details on permissions required, whether the demo is temporary or permanent, rate limits, or what happens if inputs are invalid. For a tool that likely involves resource creation, this is a significant gap in 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?

The description is two concise sentences with zero waste. The first sentence front-loads the core purpose, and the second adds key outcome. Every word earns its place, making it easy to scan and understand quickly.

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 (8 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks details on behavioral traits, usage context, and output format. With no annotations to fill gaps, the description should do more to compensate, but it meets a basic threshold for understanding the tool's function.

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 fully documents all 8 parameters. The description adds no additional meaning beyond implying that parameters like 'business_name' and 'phone_number' are used to personalize the demo, but this is already clear from the schema. Baseline score of 3 is appropriate as the 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 specific action ('Generate a white-labeled AI phone answering demo page') and the resource ('for a business'), with the outcome ('Creates a personalized demo URL'). It distinguishes itself from sibling tools like 'calculate_missed_calls' or 'estimate_revenue_loss' by focusing on demo generation rather than analytics or 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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing business details, or compare it to sibling tools like 'get_industry_use_case' for context. Usage is implied only through the action described, with no explicit when/when-not instructions.

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 retrieves information, implying a read-only operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, error handling, or what 'detailed' entails in terms of output format or depth.

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, efficient sentence that front-loads the core purpose. It avoids redundancy and is appropriately sized for a simple tool, though it could be slightly more structured by separating purpose from details.

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 annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., read-only nature, potential errors) and doesn't explain the return values or format, which is critical for a tool that retrieves 'detailed' information.

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 the schema documenting the single required parameter 'industry' as 'Industry key'. The description adds no meaning beyond this, as it doesn't explain what constitutes an 'industry key' or provide examples. The baseline is 3 since the schema handles parameter documentation adequately.

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 with specific verbs ('Get detailed use case information') and resources ('for a specific industry'), including what information is retrieved ('common scenarios and pain points'). It doesn't distinguish from sibling tools, but the purpose is 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?

No guidance is provided on when to use this tool versus the sibling tools (calculate_missed_calls, compare_solutions, estimate_revenue_loss, generate_custom_demo). The description implies usage for industry-specific insights but offers no context on alternatives or exclusions.

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