math-mcp

Server Quality Checklist

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Latest release: v0.0.0-development
Disambiguation5/5
Each tool has a distinct purpose: evaluate computes, validate_syntax checks syntax, and variable tools manage context state. No two tools overlap in a confusing way.
Naming Consistency3/5
Variable tools follow a clear verb_noun pattern (set_variable, get_variable, list_variables, clear_variables), but evaluate and validate_syntax are single verbs without a noun, mixing conventions. Still readable but not fully consistent.
Tool Count5/5
Six tools is well-scoped for a math expression server, covering evaluation, validation, and variable management without excess or deficiency.
Completeness4/5
Core features are covered: evaluate, validate, and variable CRUD. A minor gap is lack of single-variable deletion (only clear all), but set_variable can overwrite, so agents can work around it.
Average 3.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on August 17, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the core operation and does not mention what happens if the variable doesn't exist, whether the return value is typed, or any potential side effects. This is a minimal read operation, but the description fails to add any context beyond the tool name.
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, front-loaded sentence that communicates the essential operation with zero waste. It is concise and well-structured for a tool of this simplicity.
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 low complexity (one parameter, no output schema, no annotations), the description is adequate but incomplete. It clearly states the purpose, but does not cover what the return value looks like or error behavior, which would be helpful for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage with a clear description for the single 'name' parameter. The tool description adds no additional meaning beyond the schema, so the baseline score 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 uses a specific verb ('Get') and a clear resource ('variable'), directly stating the operation. It distinguishes itself from siblings like set_variable, list_variables, and clear_variables, which involve different actions.
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 gives no guidance on when to use this tool versus alternatives. It does not mention that list_variables should be used for retrieving all variables, nor does it describe any context or exclusions.
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. It does not disclose whether the variable is overwritten, whether the expression is evaluated immediately, what happens on invalid expressions, or any persistence/scope details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no filler. It is appropriately front-loaded and every word earns its place.
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 two-parameter tool with fully described parameters, the description gives the core purpose. However, given it is a mutation tool with no annotations and no output schema, it lacks behavioral context (overwrite, error handling, scope) that would make it fully 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?
Schema description coverage is 100%, and the parameter descriptions ('Variable name', 'Mathematical expression to evaluate and store') are clear. The tool description adds no additional semantic detail 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 action ('Set a variable value') and the context ('math context'). It distinguishes the tool from siblings like get_variable, list_variables, and clear_variables by specifying the write operation.
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 set a variable versus evaluate an expression directly. There is no mention of 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 carries the full burden of behavioral disclosure. It only mentions 'strict grammar validation' as a trait, but does not disclose error behavior, state mutation (read vs. write), support for variables (despite sibling tools set_variable/get_variable), or return value format. This is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that communicates the core purpose and a key distinguishing feature. Every word contributes value; there is no repetition of the tool name or schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and two parameters, the description is too brief. It fails to mention how to reference variables (given sibling tools), what happens on invalid expressions (error vs. result), and what the return value of evaluation or validate_only mode looks like. This leaves significant gaps for a user or agent to use the tool correctly.
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%, as both parameters have descriptive text in the schema. The tool description itself adds no parameter-level semantics beyond what the schema already provides, so the baseline of 3 applies.
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 states a specific verb ('Evaluate'), a resource ('mathematical expressions'), and adds a distinguishing scope ('strict grammar validation'). It clearly differentiates from the sibling tool 'validate_syntax', which likely only checks syntax without computing.
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 intended use (evaluating expressions) is implied by the tool name and description, and the validate_only parameter hints at a distinction from pure syntax validation. However, there is no explicit guidance on when to use this tool versus 'validate_syntax' or how it relates to the variable-related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states that validation is performed, but does not disclose critical behavioral details such as the return value (boolean, error, etc.) or whether the tool has side effects. This is a minimal disclosure for a validation tool.
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 entire description is a single, six-word sentence. It is maximally efficient and front-loaded, with no unnecessary 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?
Given the absence of annotations and output schema, the description should explain the tool's behavior and expected outputs. It does not mention what result the validation returns or how the agent should interpret failures, making it incomplete for an agent to reliably use the 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 already provides a 100% description of the 'expression' parameter. The description adds the context of 'against grammar', clarifying the validation criterion, but does not provide additional syntax or format details 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 a specific action ('Validate') on a specific resource ('mathematical expression syntax') against a defined standard ('grammar'). This distinguishes it from sibling tools like 'evaluate' or 'set_variable' which handle evaluation and variable management.
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 the use case of checking syntax before evaluation, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. The phrasing suggests a pre-evaluation step, but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the action but does not disclose side effects such as whether the clearing is permanent, scoped to the current session, or affects system-defined variables. The word 'clear' is ambiguous between deletion and nullification.
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, concise sentence with no superfluous words. It efficiently conveys the core function without wasting the agent's attention.
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, the description is adequate but leaves gaps. It does not clarify what 'defined variables' means, whether the action is scoped globally or per-session, or what the state after clearing looks like. Without annotations or output schema, a bit more context would help an agent use it safely.
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 tool has zero parameters, and the schema is empty. The baseline for zero parameters is 4, and there are no parameter semantics to explain. The description adds no parameter information, which 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 uses a specific verb 'Clear' with a clear resource 'all defined variables'. It is distinct from sibling tools like set_variable, get_variable, list_variables, and evaluate, which focus on individual operations or evaluation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios like resetting state or provide exclusions, leaving the agent without context for choosing this over set_variable or list_variables.
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 states the action ('list') but does not mention whether the operation is read-only, non-destructive, or what output to expect. No side effects, permissions, or return format are 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 a single sentence of four words, extremely concise and front-loaded. Every word earns its place with no redundancy or unnecessary detail.
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 zero-parameter list tool, the description is nearly complete. The only gap is the lack of output format information, but given the simplicity of the tool and absence of an output schema, the description suffices. It could be enhanced by mentioning whether it returns names, values, or both, but this is not critical.
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 tool has zero parameters, so the description cannot add parameter semantics beyond the empty schema. The baseline for 0 parameters is 4, and the description does not need to compensate for any missing parameter information.
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 'List all defined variables' uses a specific verb (list) and resource (variables), clearly indicating the tool's function. It also differentiates from siblings like get_variable (which likely retrieves a single variable) by specifying 'all defined variables'.
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 get_variable or clear_variables. The description is minimal and does not state whether this should be used to enumerate variables or for other purposes, relying solely on the name.
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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