math-mcp-learning-server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.12.3
Disambiguation5/5
Each tool targets a distinct operation: expression evaluation, interest calculation, statistics, unit conversion, matrix operations (5 separate), plot types (6 separate), and workspace management. No two tools have overlapping purposes, and cross-references in descriptions clarify boundaries (e.g., calc_statistics vs calc_expression).
Naming Consistency5/5
All tool names follow a consistent pattern: a domain prefix (calc_, matrix_, plot_, workspace_) followed by a descriptive noun (e.g., expression, interest, determinant, histogram, save). Snake_case is used uniformly, with no mixing of styles.
Tool Count5/5
17 tools is well-scoped for a math learning server, covering arithmetic, statistics, linear algebra, unit conversion, plotting, and data persistence. Each tool has a clear role, neither too few nor too many for the domain's breadth.
Completeness4/5
The tool set covers core mathematical areas (expressions, statistics, matrices, unit conversion, plotting) and workspace persistence, but lacks dedicated tools for algebra, calculus, or probability distributions. Minor gaps exist, but the core learning workflows are supported.
Average 4.1/5 across 17 of 17 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 79 commits in the last 12 weeks
- Last stable release on August 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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.
This repository includes a glama.json configuration file.
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.
Add related servers to improve discoverability.

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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds a dependency requirement (matplotlib) but no other behavioral traits. With annotations, the bar is lower; this is adequate but 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 extremely concise: two short sentences and two examples. It is front-loaded and every sentence serves a purpose. 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?
With 7 parameters and no output schema, the description lacks explanation of what happens after plotting (e.g., display or return). While annotations cover safety, the brief description does not fully address the tool's runtime behavior.
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 baseline is 3. The description adds no extra parameter details beyond examples that mirror the schema. No additional meaning provided.
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 specifies 'Create a scatter plot from data points', which is a clear verb-resource pair. However, it does not distinguish from sibling plot tools like plot_line_chart or plot_histogram, which have similar formats.
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 a scatter plot versus alternatives. It only notes a dependency (matplotlib) but no context for selection among siblings.
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?
Annotations already declare readOnlyHint and idempotentHint, indicating no side effects. The description adds the dependency on matplotlib but does not elaborate on other behavioral traits like display behavior or resource usage. No contradiction with annotations.
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 sentence with a prerequisite note and two example calls. Every sentence is valuable, and it 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 no output schema, the description does not explain what the tool returns (e.g., a figure object or whether it displays the plot). The three parameters are well-described, but the tool's output behavior is missing, which may confuse an AI 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?
Input schema covers 100% of parameters with clear descriptions. The description provides examples demonstrating typical parameter values, which adds minimal extra 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 creates statistical histograms, with examples showing typical usage. However, it does not differentiate from sibling plot tools like plot_box_plot or plot_line_chart, which serve similar visualization purposes.
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 mentions the requirement of matplotlib but provides no explicit guidance on when to use this tool over alternatives (e.g., for histograms vs. other chart types). Usage is implied by the tool name and examples.
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?
Annotations already indicate readOnlyHint=true and idempotentHint=true, establishing safety. The description adds a specific error condition (ValueError if NumPy unavailable) which is useful. However, it does not disclose behavior for singular (non-invertible) matrices or other edge cases, which is a notable gap beyond what annotations cover.
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 succinct with three labeled sections: purpose, note, and examples. Every sentence contributes value. There is no redundancy or filler. The structure aids quick parsing, fitting within a few lines.
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?
With a single parameter and existing output schema, the description is largely complete. It covers the core operation, a dependency note, and examples. Minor omission: no mention of what happens for singular matrices or the output format (though output schema likely handles that). Overall, it meets the needs for this straightforward 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?
The input schema has 100% description coverage, so the baseline is 3. The description's examples mirror the schema's example, adding no new semantic information. No additional detail about matrix constraints (e.g., size, value ranges) is provided. The description adds marginal value over 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 explicitly states 'Calculate the inverse of a square matrix,' which is a specific verb+resource combination. It clearly distinguishes from sibling tools like matrix_determinant and matrix_multiply by specifying 'inverse' and 'square matrix'. Examples further clarify the intended input format.
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 matrix_determinant or matrix_multiply. The description lacks information about prerequisites (e.g., matrix must be invertible) or when to choose another matrix operation. Examples show typical usage but do not help the agent decide contextually.
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?
Annotations indicate mutation (readOnlyHint=false) and no idempotency. Description adds context of persistence (surviving restarts) but does not disclose overwrite behavior if a variable with the same name already exists, which is a notable gap.
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?
Description is brief and front-loaded with the core purpose. Two examples efficiently show usage. Could be slightly more concise, but overall well-structured with no wasted sentences.
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?
Description and annotations cover basic behavior, but the tool has an output schema not shown here. No mention of return value, error handling, or overwrite behavior. For a mutation tool with 3 required params, more completeness is expected.
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 coverage is 100% with clear descriptions for all three parameters. The description adds no additional semantic meaning beyond the schema and examples, meeting the baseline for full 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?
Title and description clearly state the tool saves a calculation to persistent workspace. Examples illustrate the exact syntax with name, expression, and result. Distinct from sibling tools like workspace_load or calc_* tools.
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?
Description explains what the tool does but lacks explicit guidance on when to use it versus alternatives (e.g., when to save vs. just calculate). Examples are helpful but no when-not-to-use or prerequisite conditions.
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?
Annotations already indicate idempotent and readonly. Description adds that data persists across sessions (via examples), but no error behavior or case of missing variable.
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?
Extremely concise with no wasted words. Front-loaded with purpose and followed by illustrative examples.
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 low complexity, single required param, and presence of output schema, the description is largely complete. Could mention that it returns the saved value, but not necessary.
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 already describes the single parameter with 100% coverage. Description provides examples but adds minimal new semantic value 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?
Clearly states it loads saved calculation results from workspace, with examples that distinguish it from sibling tool workspace_save and other computation tools.
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?
Implicitly suggests use after workspace_save, but no explicit guidance on when not to use or alternatives among the many calculation tools.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating no side effects. The description adds 'requires matplotlib' as a dependency, but does not elaborate on behavior like display or return value.
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 (three lines plus examples) and front-loaded with purpose. Examples add clarity but could be trimmed slightly; overall efficient.
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 rich annotations and full schema, the description is sufficient. It explains the tool's purpose, provides examples, and notes a dependency, though it omits details about output (plot display or saving).
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 coverage is 100% with clear descriptions for each parameter. The description's examples illustrate usage but do not add semantic meaning beyond what the schema provides.
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 creates a box plot for comparing distributions, specifying the resource (box plot) and verb (create). It distinguishes from sibling plotting tools by mentioning box plot specifically.
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 implies use for comparing distributions via box plots, and examples clarify typical input format. However, it does not explicitly state when to use this tool over alternatives like plot_histogram or plot_line_chart.
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?
Annotations already declare readOnlyHint and idempotentHint, so the description adds value by noting the NumPy dependency and the ValueError if unavailable. This goes beyond the annotations without contradicting them.
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 sentence for purpose, a note, and two examples. It is front-loaded and every sentence earns its place without unnecessary verbosity.
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 that an output schema exists, the description does not need to explain return values. It covers purpose, dependency, error handling, and examples. Missing explicit mention of error for non-square matrices, but overall complete.
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 already has a comprehensive description for the 'matrix' parameter (100% coverage). The description does not add additional meaning beyond what the schema provides, so a baseline of 3 is appropriate.
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 'Calculate the determinant of a square matrix,' using a specific verb and resource. It distinguishes from sibling tools like matrix_inverse and matrix_eigenvalues by focusing on determinant calculation.
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 examples and notes a dependency (NumPy) but does not explicitly contrast with siblings or specify when to use this tool over alternatives. Usage context is implied but not explicit.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and idempotency. The description adds the 'requires matplotlib' dependency, which is valuable beyond annotations. No contradiction or additional hidden behavior is disclosed.
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 sentence plus two illustrative examples. Every element earns its place with no redundancy, making it easy to parse quickly.
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 rich input schema (all parameters described) and annotations (safety profile), the description is largely complete. It adds the matplotlib dependency and usage examples. However, it does not specify the return type (e.g., plot object or display), which is a minor gap in the absence of an output schema.
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 coverage is 100% with clear descriptions for all three parameters. The description reiterates parameter usage through examples (e.g., plot_function('x**2', (-5, 5))), adding minimal new semantic value 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 the verb 'generate' and the resource 'mathematical function plots', with examples showing usage for expressions like 'x**2' and 'sin(x)'. It distinguishes from sibling plot tools (e.g., plot_histogram, plot_line_chart) by focusing on mathematical expressions rather than data-based plots.
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 basic context ('requires matplotlib') and usage examples, but does not explicitly state when to use this tool versus alternatives like plot_scatter or calc_expression. No exclusions or when-not-to-use guidance is given.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to reiterate safety. It adds value by detailing the formula and parameter constraints (e.g., rate as decimal), providing more context than the annotations alone.
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 well-structured with purpose, formula, and examples. It is slightly verbose but efficient, with no redundant information. The front-loading of purpose helps agents quickly understand the tool.
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 existence of an output schema, return values do not need explanation. The description adequately covers input parameters and usage. It does not discuss error handling or edge cases, but for a simple calculator, this is sufficient.
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 coverage is 100%, so baseline is 3. The description adds examples that demonstrate parameter usage, such as default compounds_per_year=12 and rate conversion. This enhances understanding beyond the 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?
Description explicitly states 'Calculate compound interest for investments' with a clear formula and examples. It distinguishes itself from sibling tools like calc_expression or calc_statistics by being specific to compound interest calculations.
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 does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, there's no mention of when not to use it or comparisons to sibling tools. The usage context is implied but not stated.
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?
Annotations already provide readOnlyHint and idempotentHint. The description adds supported types and examples, which are consistent but do not reveal additional behavioral traits beyond the tool's obvious nature.
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?
Description is concise, well-structured with bullet points and examples, front-loaded with purpose. Every sentence adds value, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For the tool's simplicity, the description covers purpose, supported types, parameters, and examples. Output schema is present, so return values do not need explanation.
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 coverage is 100%, baseline 3. Description adds value by grouping units by type and showing example conversions, clarifying parameter usage beyond 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 'Convert between different units of measurement' and lists supported unit types. It distinguishes itself from sibling calculation tools by being the only unit conversion tool.
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 unit conversion with listed types, but does not explicitly state when to use this tool versus alternatives (e.g., for non-unit calculations). No exclusions or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, indicating a safe read-only operation. The description adds value by disclosing the dependency on NumPy and the potential ValueError. This provides useful behavioral context beyond the annotations.
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?
Description is extremely concise: one line for purpose, one line for dependency, and two examples. Every element adds value without verbosity. Front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, high schema coverage, rich annotations, and presence of an output schema, the description adequately covers all necessary context. It mentions the critical dependency and provides examples, making it complete for typical usage.
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%, and the schema already defines the 'matrix' parameter clearly. The description adds examples but no additional semantic nuance beyond what the schema provides. Baseline 3 is appropriate.
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 verb 'Transpose' and the resource 'matrix' with an explanatory phrase 'swap rows and columns'. The tool's function is unambiguous and distinct from sibling matrix operations like determinant, inverse, and multiply.
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 through the name and mathematical context (e.g., transposing a matrix). It does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions. The examples help but do not constitute clear usage guidance beyond the basic operation.
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 adds context beyond annotations by noting the dependency on NumPy and the ValueError raised if unavailable. It also provides examples. Annotations already indicate readOnlyHint and idempotentHint, which the description does not contradict.
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 sentence for purpose, a note about dependency/error, and two examples. Every element serves a purpose with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to explain return values. It covers purpose, dependency, error conditions, and usage examples, which is complete for a straightforward mathematical 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 schema already has 100% coverage with descriptive text and examples for each parameter. The description provides additional examples showing concrete usage, which helps clarify matrix shape and dimension expectations 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 explicitly states 'Multiply two matrices (A × B).' This is a specific verb and resource, clearly distinguishing it from sibling tools like matrix_determinant and 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 matrix multiplication but provides no explicit guidance on when to use versus alternatives, nor any exclusions. Siblings include other matrix operations, but no differentiation is offered.
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?
Annotations already declare readOnlyHint and idempotentHint. Description adds the list of operations and example return values, providing useful context beyond 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?
Description is concise with a clear structure: purpose, operations list, usage note, examples. No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-param tool with 100% schema coverage and an output schema, the description fully covers purpose, usage, and examples. No gaps.
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 covers both parameters 100% with descriptions and examples. Description repeats the operations list and provides example call, adding modest 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 'Perform statistical calculations on a list of numbers', specifying the verb and resource. It distinguishes from siblings by referencing the 'calculate' tool for single expressions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use: 'compute descriptive statistics over a list of numbers', and when not: 'to evaluate a single mathematical expression, use the calculate tool instead.'
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?
Annotations already declare readOnlyHint and idempotentHint=true. Description adds context that it requires matplotlib, creates realistic patterns for educational purposes, and does not use real market data. No contradictions. Slight gap: does not explicitly state that it generates data each call (consistent with idempotent hint if parameters same).
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?
Highly concise: three short paragraphs plus two examples. Front-loaded with purpose, then usage, then notes. Every sentence adds value with no redundancy.
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 4 parameters, no output schema, but annotations present, the description covers purpose, usage, parameter examples, and behavioral notes (matplotlib requirement). Could mention that the output is a displayed plot (not returned data) but not essential for agent usage. Missing explicit return description is minor.
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 coverage is 100% with descriptions for all 4 parameters. Description adds examples and clarifies trend values (bullish, bearish, volatile) and moving average overlay, but these are supplementary rather than essential beyond schema. At baseline 3 since schema already does heavy lifting.
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 generates and plots synthetic financial price data, with specific mentions of time-series price data and moving average overlay. It distinguishes itself from the sibling tool 'plot_line_chart' by specifying use for financial data vs general XY data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Use for time-series price data with optional moving average overlay.' Also provides an alternative: 'For general XY data, use plot_line_chart instead.' This gives clear guidance on tool selection.
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?
Annotations already indicate readOnlyHint and idempotentHint, so the safety profile is clear. The description adds the dependency note (requires matplotlib), but does not detail what the tool returns (e.g., whether it displays a chart or returns a figure). Minor 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?
Concise with a clear purpose sentence, a usage note, and two illustrative examples. No wasted words; information is front-loaded.
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 full schema coverage and annotations, the description adequately covers purpose, usage, and examples. However, it does not specify the output format (e.g., returns a matplotlib figure or displays the chart), which would improve completeness for a tool with no output schema.
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 descriptions cover 100% of parameters with clear examples and defaults. The description does not add new parameter details beyond the schema; examples illustrate usage but do not enhance parameter understanding significantly.
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 creates a line chart from data points and specifies it is for general XY data. It distinguishes from the sibling tool plot_financial_line.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use (general XY data) and when not to use (time-series price data with moving average, suggesting plot_financial_line instead). Also mentions the dependency on matplotlib.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds value beyond annotations by noting NumPy requirement and providing examples. No contradiction with readOnlyHint and idempotentHint.
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?
Description is brief, front-loaded with action, includes necessary note and examples. Every sentence is useful, no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given full schema coverage and presence of output schema, description is complete. It explains tool purpose, dependency, and provides examples.
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 coverage is 100%, so baseline is 3. Description includes examples but does not add new parameter semantics beyond 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?
Description clearly states 'Calculate the eigenvalues of a square matrix', which is a specific verb+resource. It distinguishes from sibling matrix tools like determinant and inverse.
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?
Provides context on required dependency (NumPy) and failure mode (ValueError). Lacks explicit 'when not to use' but purpose is clear enough to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds 'safely' context beyond annotations, lists all operations and functions, and provides examples demonstrating 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?
Very concise: single line purpose, bulleted ops, clear usage note, and three diverse examples. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple calculator: purpose, operations, parameter, usage guidance, examples. Annotations and output schema handle remaining context.
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 already provides good parameter description (100% coverage). Description adds constraint 'single mathematical expression' and usage context, adding value beyond 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?
Clearly states 'evaluate mathematical expressions' with supported operations and functions. Distinguishes from siblings like calc_statistics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (single expression) and when not (statistics), naming the alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

$math-mcp-learning-server MCP server$

Copy to your README.md:

[![math-mcp-learning-server MCP server](https://glama.ai/mcp/servers/clouatre-labs/math-mcp-learning-server/badges/card.svg)](https://glama.ai/mcp/servers/clouatre-labs/math-mcp-learning-server)

Score Badge

$math-mcp-learning-server MCP server$

Copy to your README.md:

[![math-mcp-learning-server MCP server](https://glama.ai/mcp/servers/clouatre-labs/math-mcp-learning-server/badges/score.svg)](https://glama.ai/mcp/servers/clouatre-labs/math-mcp-learning-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/clouatre-labs/math-mcp-learning-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server