MCP Server Boilerplate
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: one is for creating MCP servers (get-mcp-docs) and the other is for greeting users (hello-world). There is no overlap or ambiguity between these functions, making tool selection straightforward.
Naming Consistency2/5The naming is inconsistent: get-mcp-docs uses kebab-case with a verb-noun structure, while hello-world uses kebab-case but is a phrase rather than a clear action. There is no predictable pattern across the tool set, making it harder to infer functionality from names alone.
Tool Count2/5With only 2 tools, this server feels thin and under-scoped for a 'boilerplate' purpose, which typically implies a foundational set of utilities. The count is too low to provide meaningful coverage or demonstrate a coherent tool surface for server development.
Completeness1/5The server is severely incomplete for a boilerplate MCP server domain. It lacks essential operations like configuration, testing, deployment, or common utilities, leaving obvious gaps that would hinder agent workflows and fail to support typical development tasks.
Average 2.4/5 across 2 of 2 tools scored. Lowest: 1.7/5.
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 status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto 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
- Behavior1/5
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 but offers none. 'Make an MCP server' doesn't indicate whether this is a read operation, a write operation, or something else. It doesn't disclose what happens when invoked (e.g., creates files, returns documentation, requires specific permissions). The description provides no behavioral context beyond the minimal action implied by the verb 'Make'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just three words, this is under-specification rather than effective conciseness. The description fails to communicate essential information and wastes the opportunity to provide meaningful guidance. A truly concise description would still convey purpose and usage while being brief.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a tool with 1 parameter, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what the tool does, when to use it, what behavior to expect, or what the output might be. For a tool that presumably performs some meaningful action (based on the name 'get-mcp-docs'), this description leaves the agent with almost no useful information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the single parameter 'name' as 'The name of the MCP server'. The description adds no additional meaning about this parameter - it doesn't explain what format the name should take, what it's used for, or provide examples. With complete schema coverage, the baseline of 3 is appropriate since the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Make an MCP server' is a tautology that essentially restates the tool name 'get-mcp-docs' in different words. It doesn't specify what action is performed (e.g., retrieve documentation, generate code, create configuration) or what resource is involved. The description fails to distinguish this tool from its sibling 'hello-world' or provide any meaningful clarification about its function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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, what context it's appropriate for, or any prerequisites. There's no mention of when-not-to-use scenarios or comparison with the sibling tool 'hello-world'. The agent receives zero usage direction beyond the vague description.
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. 'Say hello to the user' implies a read-only, non-destructive operation, but it doesn't specify output format, side effects, or any constraints (e.g., rate limits, authentication needs). For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description 'Say hello to the user' is a single, efficient sentence that directly conveys the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with zero waste, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no annotations, no output schema), the description is minimally adequate. It states what the tool does but lacks details on output, usage context, or behavioral traits. For a basic greeting tool, this might suffice, but it doesn't provide complete guidance for optimal agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'name' documented as 'The name of the user'. The description doesn't add any parameter details beyond what the schema provides. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Say hello to the user' clearly states the tool's purpose with a specific verb ('Say hello') and target ('to the user'). It distinguishes itself from the sibling tool 'get-mcp-docs' by focusing on greeting rather than documentation retrieval. However, it doesn't explicitly mention the resource being acted upon (e.g., generating a greeting message).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There's no mention of context, prerequisites, or comparison with the sibling tool 'get-mcp-docs'. The agent must infer usage based solely on the tool name and description without explicit direction.
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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- Evaluate tool definition quality.
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