Learning Assistant MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have clearly distinct purposes, but explainConcept and explainThisCode could be confused by an agent selecting between a general concept explanation and a specific code explanation. Similarly, reviewCode and debugHelper overlap when reviewing buggy code, though their primary focuses differ.
Naming Consistency4/5
Tool names mostly follow a camelCase verb_noun pattern (explainConcept, reviewCode, suggestBestPractice, compareApproaches, generateLearningPath, explainThisCode). However, debugHelper deviates as a noun phrase rather than a clear verb_noun, and quizMe uses an object pronoun instead of a direct noun.
Tool Count5/5
With 8 tools, the server is well-scoped for a learning assistant. Each tool addresses a distinct pedagogical or coding assistance need, and the count is neither too sparse nor overwhelming.
Completeness5/5
The tool set covers the core learning workflow: explaining concepts, understanding code, reviewing code, debugging, suggesting best practices, comparing approaches, generating learning paths, and practice quizzes. There are no obvious gaps for the stated purpose of a learning assistant.
Average 3.7/5 across 8 of 8 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
- No stable releases found
- 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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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 are provided, so the description carries the full burden. It states the basic behavior (comparing and listing pros/cons) but does not disclose any additional behavioral traits such as output format, determinism, error handling, or requirements beyond having at least two approaches. This is minimal for a tool with no annotation support.
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 that includes both the action and the key output. Every word earns its place, with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with a clear purpose, and the schema fully documents its parameters. However, without an output schema or annotations, the description leaves gaps about the exact output structure and when to use the tool. It is adequate but not rich enough to make it fully self-contained.
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 parameters are already well-documented. The description adds no additional semantic meaning beyond what the schema provides—it mentions 'multiple approaches' and 'problem' but does not clarify the 'criteria' parameter or any relationships between parameters. 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 a specific verb ('compares') and resource ('multiple approaches to solve a programming problem'), and adds the output behavior ('listing pros and cons of each'). This distinguishes it from sibling tools like explainConcept or reviewCode, which have different 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 usage context is implied: use this tool when you have multiple approaches and want a comparative analysis. However, there is no explicit mention of when not to use it or any alternatives. The description lacks clear guidance on choosing this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the core function without mentioning side effects, limitations, authentication needs, or output format. It also does not describe how parameters like difficulty or questionType affect behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence with no redundant wording. It immediately states the action and object, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has four parameters with full schema descriptions, and the description provides the high-level purpose. However, without an output schema, the description could have elaborated on the structure of generated questions or constraints, but it remains sufficient for basic selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage of parameter descriptions, including enums for difficulty and questionType. The description adds no additional meaning beyond the schema, so it meets the baseline but does not enhance understanding.
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 ('Generates') and identifies the resource ('practice questions') and subject ('programming concept'). It clearly distinguishes from sibling tools like explainConcept or reviewCode, which focus on explanation and review rather than testing.
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: to test understanding of a concept. However, it does not explicitly state when to use it versus alternatives like explainConcept or generateLearningPath, nor does it provide any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states the core action but does not reveal whether the tool is read-only, what the feedback format looks like, whether it handles invalid code, or any other behavioral characteristics. This is a notable gap for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action ('Reviews a code snippet') and concisely lists the review dimensions. Every word adds value with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The input schema is well documented, but the tool lacks an output schema and annotations. The description gives a general sense of the feedback (correctness, style, improvements) but does not clarify the structure, depth, or form of the returned review, leaving the agent with uncertainty about the output. This is adequate but not 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%, so the baseline is 3. The description does not add any parameter-specific meaning beyond what the schema already provides for code, language, context, and focusAreas. It neither compensates nor detracts from the schema's clarity.
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 'reviews' combined with a clear resource 'code snippet' and explicitly states the output ('feedback on correctness, style, and potential improvements'). This clearly distinguishes it from sibling tools like explainThisCode (explains) and debugHelper (debugs), making the purpose unambiguous.
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 when you have a code snippet needing review, but it does not provide explicit when-not-to-use guidance or name alternatives. No exclusions or comparisons to sibling tools are mentioned, leaving the context somewhat assumed rather than clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only says 'suggests best practices' without detailing what the agent should expect in return (e.g., list format, interactivity) or whether any side effects exist. This leaves significant transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core purpose without any filler. Every word serves a 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?
The tool has only three parameters and no output schema, so the description is not required to explain return values. However, it lacks usage disambiguation from sibling tools and behavioral transparency, making it adequate but not 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%, so the schema already documents all parameters. The description adds minimal value by mentioning 'pattern' and 'technology' as potential task domains, but it does not elaborate on how currentApproach influences suggestions. 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 the tool's function with a specific verb ('suggests') and resource ('best practices for a specific programming task, pattern, or technology'). This distinguishes it from sibling tools like explainConcept and reviewCode, which focus on explanation and review rather than prescriptive best practices.
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 by stating the tool is for best practices on a task, pattern, or technology. However, it does not explicitly mention when to use this tool over alternatives like compareApproaches or generateLearningPath, nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It states the core action (analyzing) and result (guidance), which conveys a read-only, non-destructive behavior. However, it does not detail how optional parameters affect the output, nor the format of the guidance, leaving some uncertainty about the tool's internal behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that is front-loaded with the key action and resource. It contains no filler or redundant information, making it concise and easy to parse.
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 has no output schema and no annotations, the description should ideally clarify what the debugging guidance looks like and how the optional parameters contribute. It does convey the basic purpose, but lacks detail on return values and parameter usage patterns, leaving the agent to infer some context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents each parameter. The description adds minimal semantic value beyond the schema by referring to 'error message or bug description' which aligns with the 'error' parameter, but it does not clarify the purpose of 'code', 'context', or 'language' beyond their existing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('analyzes') and resource ('error message or bug description'), and the outcome ('provides debugging guidance'). It distinguishes itself from siblings like 'explainThisCode' (which explains code) and 'reviewCode' (which reviews quality), making its purpose unmistakable.
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 tool should be used when an error message or bug description is available, but it does not explicitly state when to prefer this over alternatives like 'reviewCode' or 'explainThisCode'. There are no exclusions or alternative comparisons, so guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that examples are included and the tone is beginner-friendly, but does not mention that the 'level' or 'language' parameters affect the output, nor describe the return format. This is some behavioral context but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence of 9 words that immediately communicates purpose and a key feature (examples). It is front-loaded, with no filler or redundancy.
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 text-generation tool, the description covers the core function and hints at output with 'with examples'. However, it omits that 'level' and 'language' parameters influence the response, and the 'beginner-friendly' wording undersells the full range of levels supported by the schema, creating a minor completeness gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter (concept, level, language) having a clear description. The tool description does not add additional semantic meaning beyond the schema, so the baseline score 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 uses a specific verb ('Explains') and resource ('programming concept') with qualifiers ('simple, beginner-friendly terms with examples') that clearly define the tool's scope. This distinguishes it from sibling 'explainThisCode', which focuses on code rather than concepts.
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 phrase 'beginner-friendly terms' implies use for simplified explanations, but there is no explicit guidance on when to use this tool over alternatives like 'explainThisCode' or 'compareApproaches'. No exclusions or alternative suggestions 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?
With no annotations provided, the description carries the burden of behavioral disclosure. It indicates the output will contain steps, resources, and milestones, but does not explain any limitations, prerequisites (beyond schema), return format, or whether results are custom vs. templated. It adds some context but leaves significant room for 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 a single, well-formed sentence that is front-loaded with the primary action and includes necessary details about the output without any filler. Every word contributes value.
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?
The tool is relatively simple with a clear schema and three enums, and the description covers the core outcome (steps, resources, milestones). Since there is no output schema, the description provides a reasonable expectation of the result. It could be slightly richer (e.g., mention that it adapts to current/goal levels), but the overall context is sufficiently 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 provides 100% coverage with descriptive text for each parameter (topic, currentLevel, goalLevel, timeCommitment). The description adds no additional parameter-level semantics, so it falls at the baseline of 3 as the schema does the 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 uses a specific verb ('Creates') and identifies a clear resource ('structured learning path') with concrete deliverables ('steps, resources, and milestones'), distinguishing it from sibling tools like explainConcept or debugHelper. The purpose is immediately obvious and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for generating a learning path 'for learning a topic', which gives a clear usage context. However, it does not explicitly mention when to prefer this tool over alternatives, nor does it provide exclusions or complementary use cases with sibling tools. The guidance is implied rather than 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?
With no annotations provided, the description carries the full burden. It discloses the output granularity ('line-by-line or block-by-block') which is a useful behavioral trait. However, it does not mention that the tool does not execute code, typical output format (e.g., text/markdown), or any limitations. This is adequate but not rich.
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 that front-loads the primary action and output. It includes the key distinction of line-by-line or block-by-block without unnecessary detail. Every word contributes meaning.
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?
The tool has relatively low complexity (3 parameters, no output schema). The description covers the core function and output granularity, while the schema handles parameter details. It is complete enough for an agent to select and invoke the tool correctly, though it could mention return format or supported languages for extra clarity.
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% parameter description coverage: code, language, and detailLevel each have clear descriptions. The tool description adds no extra parameter semantics beyond what the schema provides, so a 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 clearly states the tool's function: providing detailed, line-by-line or block-by-block explanations of code. It uses a specific verb ('provides') and resource ('code'), and the granularity mentioned distinguishes it from sibling tools like explainConcept (which likely explains concepts without code).
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 clear usage context: use this tool when you need to understand what a piece of code does. It does not explicitly mention when not to use it or list alternatives, but the purpose is self-evident and distinct from siblings like reviewCode or debugHelper. This meets the 'clear context, no exclusions' threshold.
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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