Math MCP Server

Server Quality Checklist

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Latest release: v4.1.8
Disambiguation3/5
The tools are mostly distinct in their specialized purposes (derivative, solve, matrix_operations, statistics, unit_conversion), but the 'evaluate' tool is a catch-all that can also handle calculus and matrices, creating ambiguity with the specialized tools. An agent might struggle to decide whether to use 'evaluate' or a dedicated tool for a given task.
Naming Consistency3/5
Naming is consistently lowercase and uses underscores for multi-word names, but the pattern mixes verbs (evaluate, simplify, solve) and nouns (derivative, matrix_operations, statistics, unit_conversion). This is readable but not a uniform verb_noun convention.
Tool Count5/5
With 7 tools, the server is well-scoped for a math toolkit, covering algebra, calculus, linear algebra, statistics, and unit conversion. Each tool has a clear domain, and the count is within the ideal range.
Completeness4/5
The server covers core math operations well (evaluate, simplify, derivative, solve, matrices, statistics, conversions), but integration (antiderivatives) is a notable missing calculus operation. This is a minor gap that agents might work around with 'evaluate' if supported, but it's not explicitly included.
Average 3.4/5 across 7 of 7 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 68 commits in the last 12 weeks
- Last stable release on August 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under ISC License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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?
No annotations exist, and the description does not disclose any behavioral traits such as side effects, performance considerations, or error handling. The tool is presented as a black box.
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, consisting of one declarative sentence followed by examples. It front-loads the core purpose and is free of unnecessary words, though the examples could be more systematically structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, yet the description does not mention return types or format. The nested 'scope' object lacks explanation of structure. Given the complexity and multiple siblings, the description is insufficient for an agent to fully understand the tool's capabilities and boundaries.
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% coverage, describing both parameters. The description adds a concrete example for 'scope', clarifying its usage. However, no additional semantics are provided for 'expression' beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Evaluate a mathematical expression' and lists examples covering arithmetic, calculus, matrices, etc. However, sibling tools like 'derivative' and 'matrix_operations' overlap with the claimed capabilities, creating ambiguity about when to use this tool versus more specific 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 is provided on when to use this tool versus siblings. There are no conditions, limitations, or examples of appropriate use cases, leaving the agent to infer from 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?
No annotations are provided, yet the description discloses no behavioral traits beyond the basic operation. It does not mention whether it handles various expression types, side effects, or output format.
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 sentence plus an example, which is concise and front-loaded. Every part is relevant, though it could be slightly more informative.
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 output schema and annotations, the description is incomplete. It does not explain the return value or provide sufficient context for the two parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema's parameter descriptions.
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 simplifies a mathematical expression and provides an example, making its purpose unambiguous. It implicitly distinguishes from sibling tools like derivative or evaluate.
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 evaluate or solve. The description lacks context for appropriate usage.
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, yet it only mentions input formatting, not output shapes, constraints (e.g., square matrices for inverse/determinant/eigenvalues), or error behavior. It does not contradict annotations, but it is not transparent about the operation'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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and immediately followed by an input format example. No wasted words.
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?
Despite having no output schema, the description does not describe return values or per-operation output differences (e.g., determinant returns a scalar, eigenvalues return a list). For a multi-operation tool with several operations and a required schema, the description leaves significant gaps for an agent trying to interpret results.
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 all three parameters at 100%, and the description's matrix-format example is already present in the schema ('e.g., [[1,2],[3,4]]'). It adds no new parameter-level meaning beyond what the schema provides, so it meets the baseline.
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 specifies 'Perform matrix operations' and lists concrete operations (multiply, inverse, determinant, transpose, eigenvalues), clearly identifying the action and resource. The sibling tools are non-matrix math utilities, so the matrix focus distinguishes it.
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 an input-format guideline ('Matrices should be in array format like [[1,2],[3,4]]'), which is helpful, but it never explicitly states when to prefer this tool over alternatives or when not to use it. The difference from siblings is implied by the matrix-specific language rather than spelled out.
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 bears the full burden of behavioral disclosure. It only notes that mode returns an array, which is already stated in the schema's operation description. It does not clarify return types for other operations, error handling, or edge cases like empty data, so transparency is limited.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly lists the operation names. It is front-loaded with the verb 'Calculate' and leaves no unnecessary words. Every part of the sentence contributes to understanding the tool's purpose.
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 simplicity (2 parameters, enum, no output schema), the description is mostly complete. It lists all supported operations and highlights mode's array return. However, it could mention that other operations return numeric values or that data must be a JSON array string, but the schema covers the latter, so the overall context is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema; it lists operation names that are already in the enum and does not elaborate on the data format beyond what the schema states. Thus it provides no extra 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 tool calculates statistical values and enumerates the specific operations (mean, median, mode, etc.), which is a specific verb+resource. It distinguishes itself from sibling tools like evaluate, simplify, and derivative, which handle other types of math operations.
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 statistical calculations by listing supported operations, but it does not explicitly state when to use this tool over alternatives or provide exclusions. No sibling alternatives are mentioned, so guidance is implied rather than explicit.
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 are provided, and the description does not disclose any behavioral traits beyond the conversion operation. It does not mention side effects (likely none), permissions, or error handling, but for a simple conversion tool, this is acceptable. No contradictions with annotations as none exist.
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 very concise at one sentence plus an example, lacking any structural elements like headings or bullet points. However, it efficiently conveys the core purpose, earning a high score for brevity.
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 complete schema, the description is mostly adequate. However, it does not explain the return format (e.g., numeric value with unit string) or acknowledge potential limitations (e.g., unit compatibility), leaving some gap for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both parameters (value and target_unit). The description adds an example but no additional semantic detail beyond what the schema provides, so 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?
The description clearly states 'Convert between units' and provides concrete examples ('5 inches to cm' and '100 fahrenheit to celsius'), making the tool's purpose unambiguous. It distinguishes from sibling tools like derivative or solve which handle 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives examples of how to use the tool but does not explicitly state when to prefer it over alternatives or when not to use it. There is no guidance on unsupported conversions or edge cases, leaving the agent to infer usage from 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?
No annotations are provided, and the description only covers basic functionality. It lacks disclosure of limitations, error handling, or behavior for complex expressions (e.g., multivariate or implicit differentiation).
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 and an example. Every part is necessary 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?
For a simple tool with no output schema, the description provides a basic understanding and example. However, it does not explain return format or behavior for edge cases, leaving some gaps.
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?
Both parameters have schema descriptions. The description adds value with an example showing correct parameter order and usage, clarifying syntax 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 tool computes derivatives of expressions with respect to a variable, and the example reinforces this. It distinguishes from sibling tools like solve or simplify by focusing solely on differentiation.
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 example but does not explicitly state when to use this tool versus alternatives like evaluate or simplify. No exclusions or context are 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?
Without annotations, the description carries full burden but only provides a single example. It does not specify the nature of solutions, error handling, or limitations, leaving behavioral ambiguity.
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 one sentence and an example, no extra words, 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?
Adequately states purpose and provides an example, but lacks details on return value structure, supported equation types, or error scenarios, making it minimally complete.
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 already describes parameters, but the description adds a concrete example demonstrating expected input format and syntax, adding value beyond the schema's generic 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 'Solve an equation' and provides a concrete example showing usage and output, effectively distinguishing it from sibling tools like derivative and simplify.
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?
No explicit guidance on when to use this tool versus alternatives. The example illustrates usage but does not set boundaries or mention when not to use solve.
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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