MCP Calc Tools

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have clearly distinct mathematical purposes, such as 'derivative' for derivatives, 'integral' for integrals, and 'black_scholes' for option pricing. However, there is some overlap between 'area' and 'riemann_sum' (both approximate areas under curves) and 'darboux_sum' (another area approximation method), which could cause confusion in tool selection.
Naming Consistency3/5
The naming is mixed with some consistency in using descriptive terms like 'compound_interest' and 'fourier_transform', but it lacks a uniform pattern. For example, 'black_scholes' uses underscores while 'npv' is an acronym, and there's no consistent verb_noun structure, making it readable but not predictable.
Tool Count3/5
With 18 tools, the count is borderline high for a calculator server, as it covers a broad range from basic math to advanced transforms and financial calculations. This might feel heavy and could overwhelm agents, though each tool seems purposeful within the mathematical domain.
Completeness5/5
The tool set provides comprehensive coverage of mathematical operations, including calculus (derivatives, integrals, limits), transforms (Fourier, Laplace, Z), financial calculations (NPV, present value, Black-Scholes), and basic functions (exponential, logarithm). There are no obvious gaps for a mathematical calculation server, supporting a wide range of agent workflows.
Average 2.8/5 across 18 of 18 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Inno Setup License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
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. 'Calculate compound interest' reveals nothing about the tool's behavior - it doesn't specify whether this is a read-only calculation, what format the output takes, whether it handles edge cases, or any computational characteristics. This is inadequate for a tool with 4 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just two words, with no wasted language. It's front-loaded with the core purpose, though this brevity comes at the cost of completeness. Every word earns its place in communicating the basic function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a financial calculation tool with 4 parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It doesn't explain what the tool returns, how to interpret results, parameter requirements, or any context needed for proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 4 parameters (principal, rate, time, compounds), the description provides no information about what these parameters mean, their units, valid ranges, or relationships. 'Calculate compound interest' doesn't even mention that parameters exist, let alone explain their semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Calculate compound interest' clearly states the verb ('calculate') and resource ('compound interest'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'present_value' or 'npv' that also perform financial calculations, leaving room for confusion about when to use this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With sibling tools like 'present_value' and 'npv' that handle related financial calculations, there's no indication of when compound interest calculation is preferred or what specific scenarios it addresses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. The description only states what the tool calculates without revealing any behavioral traits: it doesn't mention error handling, computational limits, assumptions (e.g., cash flow timing), or output format. For a calculation tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a simple calculation tool and front-loads the core purpose. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial calculation with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, parameter details, or output expectations. For a tool that performs NPV calculation—which involves assumptions about timing and rate—more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions 'cash flows' and implies a rate, but doesn't explain parameter meanings: what cashFlows array represents (e.g., period amounts), what rate is (e.g., discount rate as decimal), or their units. It adds minimal value beyond the schema's structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Calculate Net Present Value of cash flows', which is a specific verb ('Calculate') plus resource ('Net Present Value of cash flows'). It distinguishes from siblings like 'present_value' by specifying NPV calculation, though it doesn't explicitly contrast them. The purpose is unambiguous but could be more differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'present_value', 'compound_interest', and 'black_scholes' available, there's no indication of when NPV calculation is appropriate versus other financial calculations. The description lacks any context about use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions calculation but does not specify whether this is a read-only operation, what permissions might be required, error handling, or output format. For a tool with 6 parameters and no annotations, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mathematical calculation tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations, making it inadequate for fully understanding the tool's operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing clear descriptions for all parameters (e.g., 'Function to integrate', 'Variable of integration'). The description does not add any additional meaning beyond what the schema already documents, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Calculate the Darboux sum of a function', which is clear but vague. It specifies the verb 'calculate' and the mathematical concept 'Darboux sum', but does not distinguish it from sibling tools like 'riemann_sum' or 'integral', leaving ambiguity about when to use this specific tool versus alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as 'riemann_sum' or 'integral', nor any mention of prerequisites or exclusions. The description provides only a basic statement of purpose without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 calculates e^x but doesn't mention computational limits (e.g., handling large powers), precision, error handling, or output format. For a mathematical tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, clear sentence: 'Calculate exponential function (e^x)'. It's front-loaded and wastes no words, making it easy for an agent to parse quickly. Every part of the sentence earns its place by directly stating the tool's core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of mathematical operations and the lack of annotations or output schema, the description is insufficiently complete. It doesn't address potential issues like numerical stability, domain restrictions (e.g., complex numbers), or what the return value represents. For a tool in a server with many mathematical siblings, more context is needed to ensure correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, and the description doesn't add any parameter details beyond what's implied by 'e^x'. It doesn't explain that 'power' corresponds to the exponent x, its expected range, or units. With one undocumented parameter, the description fails to compensate for the low schema coverage, providing minimal semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Calculate exponential function (e^x)' which specifies the verb ('calculate') and resource ('exponential function'). It distinguishes this from siblings like 'logarithm' or 'derivative' by focusing on the exponential operation. However, it doesn't explicitly differentiate from all mathematical siblings, keeping it at a 4 rather than a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'compound_interest' or 'present_value' which might involve exponential calculations. There's no mention of specific contexts, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 what the tool does but doesn't describe error handling (e.g., for invalid inputs like negative values or base=1), performance characteristics, or output format. For a computational tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a simple mathematical function and front-loaded with the core purpose, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's computational nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover error conditions, mathematical constraints (e.g., domain restrictions), or what the return value represents, which are critical for reliable tool invocation in a mathematical context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'any base' which hints at the 'base' parameter, but doesn't explain the 'value' parameter or provide any details on valid ranges, units, or default behaviors. The description adds minimal value beyond the schema's structural information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as 'Calculate logarithm with any base', which is a specific verb ('calculate') and resource ('logarithm'). It distinguishes from most siblings (e.g., 'area', 'derivative', 'integral') by specifying the mathematical operation, though it doesn't explicitly differentiate from all possible mathematical tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 any prerequisites, constraints, or comparison with sibling tools like 'exponential' or 'solve', leaving the agent to infer usage context solely from the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 but only states the calculation purpose. It doesn't mention whether this is a read-only operation, if it requires specific inputs (beyond parameters), error handling, or output format. For a calculation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, direct sentence that front-loads the core purpose without any wasted words. It efficiently communicates the tool's function in minimal space, earning full marks for brevity and clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial calculation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the calculation formula, assumptions (e.g., compounding frequency), or what the result represents, making it inadequate for an agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'future cash flows' but doesn't explain the three parameters (futureValue, rate, time) beyond what the schema provides (0% coverage). It fails to clarify what units or formats these parameters expect (e.g., rate as percentage or decimal, time in years), leaving semantics ambiguous despite the schema defining types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 a specific verb ('calculate') and resource ('present value of future cash flows'), making it immediately understandable. However, it doesn't differentiate from sibling financial tools like 'npv' or 'compound_interest', which might have overlapping functionality in financial calculations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'npv' or other financial calculation siblings. It lacks context about specific scenarios, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 calculates volume but doesn't describe what the calculation entails (e.g., numerical approximation, symbolic integration), error handling, performance characteristics, or output format. For a mathematical tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with a single sentence: 'Calculate the volume of revolution around x-axis'. There is no wasted text, and it directly states the core purpose without unnecessary elaboration, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mathematical calculation tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the mathematical method, input formats, or what the output represents (e.g., a numeric value, units). This leaves critical gaps for an agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning none of the parameters (expression, start, end) are documented in the schema. The description doesn't add any meaning beyond the parameter names—it doesn't explain what 'expression' represents (e.g., a mathematical function in terms of x), or what 'start' and 'end' refer to (e.g., bounds for the x-axis). This fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 the volume of revolution around x-axis'. It specifies the verb ('calculate') and resource ('volume of revolution'), though it doesn't explicitly differentiate from sibling tools like 'area' or 'integral', which might handle related mathematical operations. The purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'integral' or 'area', which could be relevant for related calculations, nor does it specify prerequisites or contexts for use. This leaves the agent without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions calculation but doesn't disclose computational method (e.g., numerical integration), accuracy considerations, performance characteristics, error handling, or what happens with invalid inputs. For a mathematical tool with 4 parameters, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a straightforward mathematical operation and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mathematical calculation tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the return value format, error conditions, numerical method used, or how it relates to similar sibling tools. The context signals indicate significant gaps in documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 25% (only 'n' has a description), so the description must compensate but doesn't. It implies 'expression', 'start', and 'end' parameters through 'curve between two points' but provides no details about format, units, or constraints. The description adds marginal context but doesn't fully address the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 a specific verb ('calculate') and resource ('area under a curve between two points'). It distinguishes from some siblings like 'derivative' or 'limit' by focusing on area calculation, though it doesn't explicitly differentiate from 'integral' which might serve 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.
Usage Guidelines2/5
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 like 'integral', 'riemann_sum', or 'darboux_sum' which appear to be related mathematical operations. There's no mention of prerequisites, limitations, or comparative context with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 but offers minimal information. It states what the tool does but doesn't describe computational characteristics, error conditions, precision, or what happens with invalid inputs. For a mathematical calculation tool with 6 parameters, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at just 5 words, front-loading the essential information with zero wasted words. Every word earns its place by specifying the calculation type and target, making it efficient for agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mathematical calculation tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the output represents (e.g., price in what units/currency), doesn't mention the Black-Scholes model assumptions or limitations, and provides no context about when this calculation is appropriate versus alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, with each parameter clearly documented in the input schema. The description doesn't add any parameter information beyond what's already in the schema, so it meets the baseline of 3 for high schema coverage without providing additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 a specific verb ('Calculate') and resource ('Black-Scholes option price'), making it immediately understandable. It doesn't explicitly differentiate from sibling tools like 'option_greeks', but the focus on price calculation rather than Greek calculations provides implicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'option_greeks' or other financial calculation tools in the sibling list. It doesn't mention prerequisites, typical use cases, or limitations that would help an agent decide when this is the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but only states what the tool does, not how it behaves. It doesn't mention error handling (e.g., invalid expressions), computational limitations, output format, or whether it's a pure function. For a mathematical tool with zero annotation coverage, this represents a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at just one sentence that directly states the tool's purpose with zero wasted words. It's front-loaded with the essential information and doesn't include any unnecessary elaboration or redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mathematical calculation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the output looks like (e.g., simplified expression, step-by-step solution), error conditions, or mathematical limitations. Given the complexity of derivative calculation and the rich sibling tool context, more contextual information would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description doesn't add any parameter information beyond what's already in the schema, which has 100% coverage with clear descriptions for both parameters. The baseline is 3 when the schema does the heavy lifting, and the description neither compensates for gaps nor provides additional semantic context about parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('calculate') and resource ('derivative of a mathematical expression'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'integral' or 'limit' that also perform mathematical operations, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With multiple mathematical operation siblings (integral, limit, solve, etc.), there's no indication of when differentiation is appropriate versus integration or other operations, leaving the agent without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only states what the tool does ('Calculate') without mentioning computational characteristics, error handling, output format, or performance considerations. For a mathematical computation tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with no wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a Fourier transform operation, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how results are formatted, or any mathematical assumptions, leaving significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear parameter descriptions in the schema (e.g., 'Function of time' for expression). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Calculate the Fourier transform of a function', which specifies the verb ('Calculate') and resource ('Fourier transform'). However, it doesn't distinguish this tool from its sibling 'laplace_transform' or 'z_transform', which are related mathematical transforms, so it doesn't fully differentiate from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'laplace_transform' or 'z_transform'. It lacks context about appropriate mathematical scenarios, prerequisites, or exclusions, leaving the agent to infer usage based on general knowledge of Fourier transforms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only states the calculation purpose without mentioning error handling, computational limits, output format, or whether it's a pure function. This leaves significant gaps for a mathematical tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with zero wasted words. It efficiently communicates the core purpose without unnecessary elaboration, making it perfectly front-loaded and appropriately sized for this mathematical tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of Laplace transforms and the lack of both annotations and an output schema, the description is insufficient. It doesn't explain what the output looks like, potential limitations, or error conditions, leaving the agent with incomplete information for proper tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all three parameters clearly documented in the schema itself. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline for high schema coverage without compensating with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Calculate') and the specific mathematical operation ('Laplace transform of a function'), which is a well-defined concept. It distinguishes itself from siblings like 'fourier_transform' and 'z_transform' by specifying the transform type, though it doesn't explicitly contrast with them in the text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'fourier_transform' or 'z_transform', nor does it mention prerequisites or typical use cases. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits like error handling (e.g., for undefined limits), computational complexity, or output format. For a mathematical tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of mathematical limit calculation and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., a numeric value, symbolic expression, or error), how it handles edge cases, or any mathematical assumptions. This leaves the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as format examples or constraints. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Calculate the limit of a function as it approaches a value', which is a specific mathematical operation. It distinguishes itself from siblings like 'derivative' or 'integral' by focusing on limits, though it doesn't explicitly contrast with them in the description text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'solve' or 'derivative' that might handle related mathematical problems, there's no indication of when limit calculation is specifically appropriate or what prerequisites might be needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 calculates Riemann sums but does not describe what the tool returns (e.g., numerical result, error handling), performance characteristics (e.g., computational limits), or side effects. For a tool with 6 parameters and no annotations, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Calculate the Riemann sum of a function using different methods.' It is front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, mathematical calculations) and lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., a numerical approximation), error conditions, or limitations (e.g., handling of invalid inputs). For a tool performing numerical integration, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter semantics beyond what the input schema provides. The schema has 100% description coverage, with clear documentation for each parameter (e.g., 'Function to integrate' for 'expression', 'Method: left, right, midpoint, or trapezoid' for 'method'). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate or add value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 the Riemann sum of a function using different methods.' It specifies the verb ('calculate') and resource ('Riemann sum of a function'), making the intent unambiguous. However, it does not explicitly distinguish this tool from sibling tools like 'integral' or 'darboux_sum', which might serve similar mathematical purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'different methods' but does not specify contexts, prerequisites, or exclusions. Given sibling tools like 'integral' and 'darboux_sum', there is no indication of when this tool is preferred, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While 'solve' implies a computational operation, the description doesn't specify what type of equations are supported (linear, polynomial, etc.), whether symbolic or numeric solutions are returned, error handling for unsolvable equations, or computational limitations. For a mathematical 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, with zero wasted language. It's front-loaded with the core purpose and contains no unnecessary elaboration. Every word earns its place by contributing essential information about the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the mathematical complexity implied by the sibling tools and the absence of both annotations and output schema, the description is insufficiently complete. It doesn't explain what format the solution is returned in, whether multiple solutions are possible, error conditions, or limitations of the solving capability. For a mathematical solving tool in this context, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('expression' and 'variable') well-documented in the schema. The description adds no additional parameter information beyond what the schema already provides. The baseline score of 3 reflects adequate parameter documentation entirely through the schema, with no value added by the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 a specific verb ('solve') and resource ('an equation'), and specifies the target ('for a variable'). It distinguishes from siblings like 'derivative' or 'integral' by focusing on solving equations rather than calculus operations. However, it doesn't explicitly differentiate from all mathematical siblings, keeping it at 4 rather than 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With 16 sibling tools including 'derivative', 'integral', 'limit', and various financial calculators, there's no indication of when solving equations is appropriate versus using other mathematical operations. No exclusions, prerequisites, or context for selection are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only states what the tool does ('calculate'), without mentioning computational aspects like performance, error handling, or output format. For a mathematical tool with no annotations, this is a significant gap, as it doesn't address potential limitations or how results are returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with a single sentence: 'Calculate the Z-transform of a function'. It wastes no words and directly communicates the core purpose without unnecessary elaboration, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mathematical transform tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a symbolic expression, numerical result, or error message), nor does it provide examples or context for usage. This leaves significant gaps for an AI agent to understand how to interpret results or handle edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for all parameters (e.g., 'Function of discrete time' for 'expression'). The description adds no additional parameter semantics beyond what the schema provides. According to the rules, when schema coverage is high (>80%), the baseline score is 3, as the schema adequately documents the parameters without needing extra explanation in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 the Z-transform of a function'. It specifies the verb 'calculate' and the resource 'Z-transform', which is a well-defined mathematical operation. However, it doesn't explicitly differentiate from sibling tools like 'fourier_transform' or 'laplace_transform', which are related transforms, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'fourier_transform' or 'laplace_transform', nor does it explain the specific contexts or mathematical problems where the Z-transform is applicable (e.g., discrete-time signal processing vs. continuous-time for Laplace). Without such context, users must infer usage based on domain knowledge.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 calculation action but lacks details on behavioral traits: it doesn't specify if this is a read-only operation, whether it requires authentication, what the output format looks like (e.g., numeric values, JSON structure), or any rate limits. For a calculation tool with no annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Calculate the Greeks for a Black-Scholes option.' It is front-loaded with the core action and resource, with zero wasted words. Every part of the sentence earns its place by specifying what is calculated and the model involved, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (financial calculation with 6 parameters), no annotations, and no output schema, the description is minimally adequate. It identifies the tool's purpose but lacks completeness: it doesn't explain the output (what Greeks are returned, e.g., delta, gamma), behavioral context, or usage scenarios. For a tool with rich parameters but no structured output or annotations, more detail would be helpful for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all parameters (e.g., S as 'Current price of the asset'). The description adds no additional parameter semantics beyond what's in the schema, such as explaining the Black-Scholes model context or typical value ranges. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't compensate with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 the Greeks for a Black-Scholes option.' It specifies the verb ('calculate') and the resource ('Greeks'), and mentions the underlying model ('Black-Scholes option'), which distinguishes it from generic calculation tools. However, it doesn't explicitly differentiate from sibling tools like 'black_scholes' (which might calculate option prices rather than Greeks), leaving room for improvement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 (e.g., 'black_scholes' for option pricing or 'npv' for financial calculations) or specify contexts like financial modeling, risk assessment, or educational purposes. Without such cues, the agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mention computational limits, error handling, symbolic vs. numeric integration, or output format. For a mathematical 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the mathematical complexity, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., symbolic expression, constant of integration), error conditions, or limitations, which are crucial for an integration tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., examples, constraints, or usage tips), meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('calculate') and resource ('indefinite integral of a mathematical expression'), distinguishing it from sibling tools like 'derivative' or 'solve' by focusing on integration rather than differentiation or equation solving.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'riemann_sum' or 'darboux_sum' for numerical integration, or 'solve' for equation solving. It lacks context about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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