scicalc-mcp

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation in its domain (e.g., determinant vs. matrix multiply, t_test vs. linear_regression, integrate_function vs. find_root). Even similar-sounding tools like describe and dataframe_describe have clearly different inputs and purposes.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern (e.g., matrix_multiply, solve_linear_system, dataframe_groupby_aggregate). The naming is descriptive and predictable, with no mixing of conventions.
Tool Count5/5
With 19 tools, the server covers linear algebra, statistics, calculus, data frame operations, and plotting—an appropriate breadth for a scientific calculator. The count is well-scoped without being overwhelming.
Completeness4/5
The tool set covers core linear algebra, basic statistics, calculus, data frame summarization, and plotting. However, it lacks matrix operations like transpose or decomposition, and statistical tests like ANOVA, leaving minor gaps.
Average 3.4/5 across 19 of 19 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT 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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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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

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 basic operation and output format, omitting details like automatic scaling, outlier handling, or side effects. This is insufficient for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is remarkably concise: a single sentence that immediately states the verb and resource. No words are wasted, though critical information is missing.
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 lack of annotations and output schema, the description is incomplete. It does not explain required vs optional parameters, data constraints, or how the output is generated. Essential context for correct invocation is absent.
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?
Schema description coverage is 0% (no parameter descriptions in schema), yet the tool description adds no explanation for any of the three parameters (bins, data, title). The user must infer meaning from parameter names alone, which is inadequate.
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 tool draws a histogram of numerical samples and returns a PNG image. The verb '绘制' (draw) and resource '直方图' (histogram) are specific and differentiate it from sibling tools like plot_line and plot_function.
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, nor any prerequisites or limitations. It does not specify data type requirements or address when not to use it.
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 provided, so the description must disclose all behavioral traits. It only states the output type but omits assumptions (e.g., handling of non-numeric columns, missing data, required data format).
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, well-formed sentence that directly conveys the tool's function without any extraneous words. It is appropriately sized for the simple operation.
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?
The description lacks information about input format expectations, output structure, and error handling. Given no output schema and low complexity, it still leaves gaps for correct usage.
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?
Schema has 0% description coverage, and the tool description does not explain the single parameter 'csv_text' (format, encoding, required structure). The description adds no value beyond the schema definition.
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 tool returns a Pearson correlation coefficient matrix for numerical columns, using a specific verb and resource. It is distinct from sibling tools like 'correlation' which may handle single pairs or different methods.
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?
No guidance on when to use this tool versus alternatives like 'correlation' or 'dataframe_describe'. No context about prerequisites or typical use cases.
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, the description must carry the full burden, but it only states the formula. It does not disclose the return type (e.g., slope and intercept, R-squared), error handling, or constraints like equal-length arrays. This minimal disclosure leaves significant uncertainty about tool behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that delivers the core purpose with no superfluous words. It is front-loaded with the key information. However, adding structured details about parameters or output would improve completeness without breaking conciseness.
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 lack of annotations, output schema, and zero schema description coverage, the description is insufficient. It only provides the basic purpose, leaving out critical information about expected inputs (e.g., data length, format) and outputs (e.g., what values are returned). A more complete description is needed for this simple tool.
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 coverage is 0%, and the description adds only the contextual hint that y = slope * x + intercept, which implies x and y are numeric arrays. However, it does not specify that they must be of equal length, the order of elements, or any formatting requirements. The description fails to compensate for the missing schema descriptions.
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 tool fits a simple linear regression with the explicit formula y = slope * x + intercept. It specifies the verb '拟合' (fit) and the resource '线性回归' (linear regression), and it distinguishes itself from sibling tools like correlation or t_test.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention that it is limited to simple linear regression, nor does it exclude multiple regression or suggest other tools for more complex modeling. There is no context about prerequisites or data assumptions.
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 provided, so description carries full burden. It only states the output format (PNG image) but omits behavioral traits such as handling of missing data, axis scaling, or default styling. This insufficiently informs the agent of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, making it concise. However, it lacks structure and barely covers essential details. It is adequate in length but at the expense of completeness.
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 5 parameters and no output schema or annotations, the description is incomplete. It does not specify input constraints (e.g., matching array lengths), output characteristics, or any additional context needed for 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?
Schema has 5 parameters with 0% coverage (no descriptions). The description does not explain any parameter, not even the required x and y arrays. Although parameter names are somewhat suggestive, the description should add meaning beyond the schema, which it fails to do.
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?
Description clearly states the tool draws a line chart from x/y data and returns a PNG image, using specific verb (绘制) and resource (折线图). It distinguishes from sibling plotting tools like plot_function and plot_histogram by its focus on line charts.
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?
No usage guidance provided. Description does not indicate when to use this tool vs alternatives, nor when not to use it. Sibling tools exist for other plot types, but the description lacks explicit differentiation or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lists the statistics returned, including sample standard deviation and variance, which are behavioral choices. However, it does not mention edge cases (e.g., empty array, NaN handling) or performance characteristics. With no annotations, the description carries the full burden and is only partially adequate.
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: one line stating the purpose and one line listing the statistics. Every word is useful, front-loaded, and no unnecessary information. Perfectly concise for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description covers the essential behavior: what it expects and what it returns. It could include examples or error behavior, but for a simple descriptive statistics tool, this level of completeness is adequate. The sibling tools suggest a statistical context, and the description fits well.
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 has 0% description coverage, and the description does not add any semantic detail beyond the schema type for the 'data' parameter. It does not explain input constraints, format, or acceptable ranges, which is insufficient for a parameter with no schema 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 returns summary statistics for a numeric sample, listing specific measures. The verb 'describe' matches the output. However, it does not explicitly differentiate from sibling tools like 'dataframe_describe', though the input type (array) implies a different use case.
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?
No guidance on when to use this tool versus alternatives such as 'dataframe_describe' for tabular data or other statistical tools. The description assumes the user knows the context, providing no when-to-use or when-not-to-use hints.
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, and description does not disclose convergence criteria, error handling, or behavior for multiple roots or no root.
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?
Two sentences plus parameter list, no wasted words. Front-loads purpose.
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?
No output schema, no annotations. Lacks information on return value, errors, or algorithm assumptions. For a mathematical tool, more context would be beneficial.
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?
Description explains expression as mathematical expression in x and guess as starting point, adding meaning beyond the schema which has no descriptions. However, no further details on format or constraints.
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?
Clearly states it solves for root of f(x)=0 near initial guess. Verb and resource are specific. Distinct from siblings like integrate_function or solve_linear_system.
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?
No guidance on when to use this tool over alternatives. Does not mention limitations 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 present, so the description must cover behavioral traits. It fails to mention what happens if the matrix is not square (likely an error), numerical stability, or any side effects. The description is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It is extremely concise and front-loaded.
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 tool's simplicity and the presence of an output schema (not shown), the description is just barely adequate. It identifies the tool's purpose but lacks details on input validation, output format, or error conditions.
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 coverage is 0%, so the description should compensate. It adds the critical constraint that the matrix must be square, which is not in the schema. However, it does not explain the format or type constraints beyond that.
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 'Compute the determinant of a square matrix,' which is a specific verb (compute) and resource (determinant of a square matrix). It distinguishes this tool from sibling tools like matrix_multiply or matrix_inverse.
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?
No guidance is provided on when to use this tool versus alternatives, such as when to choose determinant over other matrix operations. The description offers no context for usage or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details output format (dict with eigenvalues and eigenvectors, complex numbers as pairs), which adds behavioral context beyond the bare function. However, it does not disclose limitations such as requiring a square matrix or potential numerical stability issues.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (2 sentences) and front-loaded with the purpose. It is efficient but could be slightly more structured (e.g., bullet points for output format).
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?
For a simple mathematical tool with one parameter, the description covers basic output but lacks critical context like input constraints (matrix must be square) and return value interpretation (e.g., ordering of eigenvectors).
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?
The input schema has 0% parameter description coverage, and the tool description adds no meaning beyond the schema's type definition. The parameter 'matrix' is not explained in terms of its expected shape or constraints.
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 explicitly states 'compute eigenvalues and eigenvectors of a square matrix', providing a specific verb and resource. This clearly distinguishes it from sibling tools like 'determinant' or 'matrix_inverse'.
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?
No guidance on when to use this tool versus alternatives like 'determinant' or 'solve_linear_system'. Also no mention of prerequisites (e.g., matrix must be square).
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 must cover behavioral traits. It only mentions the sampling point range (2-5000) but omits side effects, return type, or safety considerations. This is insufficient for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a header and parameter list. It is reasonably concise, though it could be more terse. The key information is front-loaded.
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 lack of annotations and output schema, the description should be more thorough. It does not describe the return value (e.g., a plot image) or error handling. However, it covers the main purpose and parameters adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description adds significant value by explaining each parameter, e.g., '以 'x' 为变量的数学表达式' for expression. This helps an AI agent understand parameter usage beyond schema types.
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 '绘制单变量函数曲线' (plot univariate function curve) and specifies the interval [start, stop]. This distinguishes it from sibling tools like plot_line and plot_histogram, which handle data points.
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 lacks any guidance on when to use this tool versus alternatives. No explicit context, exclusions, or hints about when not to use it are provided.
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, the description must fully disclose behavior. It fails to explain output format (single number), edge cases (e.g., different lengths, missing values), or assumptions (linearity). The method options are listed but not elaborated.
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: two sentences for the purpose plus a brief parameter list. No redundancy, and the key action (compute correlation) is front-loaded.
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?
For a simple 3-parameter tool with no output schema, the description covers inputs and purpose but omits output details and constraints (e.g., data must be numeric, equal length). It is adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has no descriptions (0% coverage). The description adds essential meaning: x and y are samples, must have same length, and method takes 'pearson' or 'spearman'. This compensates for the schema gap, though it could detail method behavior.
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 'Compute the correlation coefficient between two samples', specifying the verb (compute) and resource (correlation coefficient) with explicit method options. It distinguishes itself from siblings like 'dataframe_correlation_matrix' which operates on dataframes.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention conditions, prerequisites, or exclusions. The description only explains parameters, leaving the agent to infer usage context.
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, the description carries the full burden of behavioral disclosure. It does not mention the algorithm used, convergence criteria, limitations, or error handling. Agents are left unaware of potential issues like non-convergence or local vs. global minima.
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 concise with two short sentences and a parameter list. Every sentence adds value, and the structure is front-loaded with the core purpose.
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 tool's complexity (mathematical optimization) and the lack of output schema or annotations, the description is somewhat minimal. It covers purpose and parameters adequately but omits return values, potential errors, and algorithmic details, which could be important for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 0%, the description adds meaning beyond types: 'expression' is a mathematical expression in variable 'x', and 'guess' is the starting point with a default of 0. This clarifies key details not evident from the schema alone.
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 tool finds the local minimum of a univariate function near an initial guess. It uses a specific verb ('求' meaning 'find') and resource ('局部极小值'), which distinguishes it from siblings like find_root or integrate_function.
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?
No guidance is provided on when to use this tool versus alternatives. For instance, it does not explain that this tool is for minimization, while find_root is for root-finding. There are no explicit when-to-use or when-not-to-use 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?
No annotations are provided, so the description carries full burden. It does not disclose the solution method, behavior for singular matrices, error handling, or any side effects. This is insufficient for a tool that can encounter mathematical edge cases.
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: two sentences, front-loaded with purpose, and every sentence adds value. No wasted words.
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 existence of an output schema (not shown), the description need not detail return values. However, it lacks completeness on edge cases (e.g., singular matrices, overdetermined systems) and does not mention that the matrix must be square. For a simple tool, it is minimally adequate but could be more robust.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds meaning: clarifies that 'a' is an n x n coefficient matrix and 'b' is a right-hand side vector of length n. This provides dimensional constraints and mathematical role beyond the raw schema.
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 tool solves a linear system A x = b to find x, using specific verb 'solve' and specific resource 'linear system'. It distinguishes from siblings like determinant, matrix_inverse, and eigen.
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 (e.g., matrix_inverse) or under what conditions (e.g., matrix must be square and non-singular). 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It mentions the output structure but does not disclose potential errors, assumptions about CSV format (e.g., missing values), or computational limits. Very minimal 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 concise, front-loaded with the main purpose, and clearly separates parameter and return value descriptions. Every sentence is informative and non-redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter and no output schema, the description adequately explains the output structure (shape, columns, dtypes, describe). Missing details on error handling or performance, but sufficient for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'csv_text' has no schema description (0% coverage), but the tool description adds that it should contain a header row. This adds meaningful context beyond the schema.
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 it parses CSV text and returns summary statistics for each column. It uses a specific verb ('解析') and resource ('CSV 文本'), and is distinct from siblings like 'describe' and 'dataframe_groupby_aggregate'.
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 does not provide any guidance on when to use this tool versus alternatives like 'describe' or 'dataframe_correlation_matrix'. There is no when-to-use or when-not-to-use information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses error behavior for singular/non-square matrices, which is valuable. However, it fails to mention other traits like immutability, precision, or performance implications.
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 very concise—two sentences with no wasted words. It front-loads the core operation and adds critical error information efficiently.
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?
Despite having an output schema, the description does not mention that the tool returns the inverse matrix. For a simple tool, this is a minor gap; overall it is adequate but not fully complete.
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 coverage is 0%, yet the description does not add meaning to the 'matrix' parameter beyond its type (already visible in schema). No constraints like real-valued or size requirements are provided.
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 tool computes the inverse of a square matrix, distinguishing it from sibling tools like determinant and solve_linear_system. It also mentions error handling for singular or non-square matrices, adding precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for computing inverses but offers no explicit guidance on when to use this vs alternatives like solve_linear_system. It lacks when-not or exclusion criteria.
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 bears full responsibility for behavioral transparency. It discloses the one-sample vs two-sample conditional logic but omits details about assumptions (e.g., normality, equal variance), output (e.g., t-statistic, p-value), and edge cases.
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—only two sentences—with no redundant information. The key conditional logic is front-loaded, making it easy 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?
Without an output schema, the description should at least hint at the return value (e.g., test statistic, p-value). It does not. The conditional logic is clear, but critical information about what the tool actually returns is missing, making it incomplete for a statistical function.
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 0%, so the description must compensate. It explains the role of sample_b in switching tests and implicitly defines popmean as the population mean for one-sample tests. However, sample_a and sample_b are not explicitly described beyond their types, and popmean lacks explicit definition.
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 it performs a t-test, and distinguishes between one-sample and independent two-sample based on the presence of sample_b. This is specific and helps differentiate from sibling statistical tools like correlation or linear_regression.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit conditional usage: use sample_b for two-sample test, otherwise one-sample. It does not mention when to avoid this tool or compare to other statistical tests, but the conditional logic is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that x must be strictly increasing, which is a key behavioral constraint. However, lacks details on error handling, output format, or behavior for invalid inputs. With no annotations, the description carries full burden.
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?
Succinct two-sentence description followed by a clear parameter list. Every sentence adds value, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mathematical nature and presence of an output schema, the description is mostly complete. Explains all parameters and a key constraint. Output format is not described but likely inferable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining each parameter: x (known, strictly increasing), y (known), query (positions), and kind (linear/quadratic/cubic). Adds significant meaning beyond the schema.
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: interpolate values at specified query points. While it doesn't explicitly differentiate from siblings, the purpose is distinct among the provided 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?
No guidance on when to use this tool versus alternatives like linear_regression or integrate_function. No when-not-to-use or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides basic behavioral context (grouping and aggregation), lists allowed aggregate functions, but does not disclose edge cases, handling of missing values, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with one line for purpose and a list of parameters. It is front-loaded with the main action. Minor improvement could be better separation of purpose and details.
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?
The description lacks information about the output format or return value. Since there is no output schema, the description should at least hint at what the result looks like, but it does not.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description thoroughly explains each parameter: csv_text as raw CSV with header, group_by as column name, value_column as numeric column, and agg as one of the listed functions. This adds significant value.
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 verb (group and aggregate) and the resource (dataframe columns). It distinguishes from sibling tools like dataframe_describe by specifying the grouping and aggregation action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. The allowed aggregation functions are listed, but context on optimal usage or exclusions is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return value (value and error estimate) and input format, but it does not specify the numerical method used, potential failure cases, or performance characteristics. With no annotations, the description carries the full burden but only provides basic behavioral information.
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 concise, well-structured with a clear header and bullet-like parameter explanations. Every sentence adds value, and there is no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema), the description covers input, output, and constraints adequately. It mentions supported functions and constants, and the return format. Minor gaps include lack of algorithm details or error handling, but overall it provides a complete enough picture for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0%, so the description fully explains each parameter: 'expression' is a math expression in x with examples and supported functions/constants, 'lower' and 'upper' are integration limits. This adds significant meaning beyond the schema's bare property names.
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 that the tool performs numerical integration of a univariate function over an interval, using specific verbs and resource. It distinguishes the tool from sibling tools like find_root, minimize_function, and determinant, which perform different mathematical operations.
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 does not provide any guidance on when to use this tool versus alternatives. It lacks context about prerequisites, such as the function being integrable or continuous, and does not mention alternative tools for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must cover behavior. It describes inputs and output but does not mention dimension mismatch errors or other edge cases. The behavioral disclosure is adequate but not comprehensive.
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 three sentences, each carrying essential information. It is front-loaded with a clear title-like statement and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a standard matrix multiplication tool, the description covers purpose, parameters, and return format adequately. The presence of an output schema reduces the need to document return structure. Minor omissions like error cases lower completeness slightly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, so the description provides essential meaning: a is left matrix and b is right matrix, both specified as lists of rows. This adds clarity beyond the schema's generic array type, though it omits dimensional alignment details.
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 tool performs matrix multiplication, specifying the dimensions (m x n and n x p) and the output is the product matrix. This distinctively separates it from sibling tools like determinant or matrix_inverse.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for multiplying two matrices but provides no explicit guidance on when to use this tool versus alternatives like solve_linear_system or eigen. It lacks context on prerequisites or when not to use it.
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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