Skip to main content
Glama
ngeojiajun

Code Snippet Server

by ngeojiajun
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: create_snippet for creation, delete_snippet for deletion, and list_snippets for listing with optional filtering. There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (create_snippet, delete_snippet, list_snippets) using snake_case throughout. This makes the set predictable and easy to understand.

    Tool Count3/5

    With only 3 tools, the set feels thin for a snippet management server. While it covers basic operations, it lacks tools for updating snippets or retrieving a single snippet by ID, which are common needs in such domains.

    Completeness3/5

    The server provides create, delete, and list operations, but there are notable gaps: no update_snippet tool to modify existing snippets and no get_snippet tool to retrieve a specific snippet by ID. This limits functionality and may cause agent workarounds.

  • Average 2.9/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No issues in the last 6 months
    • No commit activity data available
    • 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.

    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.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

  • 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 states the tool creates a snippet but doesn't explain what happens after creation (e.g., where the snippet is stored, if it's private or public, or any side effects like notifications). This leaves significant gaps in understanding the tool's behavior beyond the basic action.

    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, efficient sentence that front-loads the main action ('Create a snippet') and specifies key parameters. It avoids unnecessary words, but could be slightly more structured by explicitly noting optional parameters or usage context.

    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 complexity of a creation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, error handling, or what the tool returns upon success. For a mutation operation, more context is needed to guide effective use by 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?

    The schema description coverage is 100%, so the input schema already documents all parameters (title, language, code, tags) with descriptions. The description adds minimal value by listing required parameters but doesn't provide additional context like format examples or constraints beyond what the schema offers, aligning with the baseline score.

    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 action ('Create a snippet') and specifies the required components ('specify title, language, and code'), which distinguishes it from sibling tools like 'delete_snippet' and 'list_snippets'. However, it doesn't explicitly mention the optional 'tags' parameter, making it slightly less specific than a perfect score.

    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 like 'list_snippets' or 'delete_snippet'. It lacks context about prerequisites, such as authentication or workspace setup, and doesn't indicate when this tool is appropriate compared to other operations.

    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 but only states the action without behavioral details. It doesn't disclose that this is a destructive operation (implied by 'Delete'), whether it's reversible, what permissions are needed, or what happens on success/failure.

    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 with a single, clear sentence that front-loads the essential information. There is zero wasted text, making it efficient for quick understanding.

    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 this is a destructive tool with no annotations and no output schema, the description is incomplete. It lacks crucial context like behavioral effects, error handling, or return values, leaving significant gaps for safe and effective use.

    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 the 'id' parameter fully. The description adds no additional meaning beyond what's in the schema, such as format examples or constraints, meeting the baseline for high coverage.

    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 action ('Delete') and target resource ('a snippet'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools like 'create_snippet' or 'list_snippets' beyond the obvious verb difference, missing explicit comparison.

    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 doesn't mention prerequisites (e.g., needing an existing snippet ID), exclusions, or comparisons to siblings like 'list_snippets' for finding IDs first.

    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 the full burden of behavioral disclosure. It states 'List snippets' which implies a read-only operation, but doesn't address permissions, pagination, rate limits, or what happens if no filters are applied. This leaves significant gaps for a tool with no annotation coverage.

    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 and front-loaded, consisting of a single sentence that directly states the tool's purpose and filtering options. There is no wasted language or unnecessary elaboration, making it efficient for an agent to parse.

    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 annotations and output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits like pagination or permissions. For a list tool with filtering, more context is needed to guide the agent effectively.

    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 description coverage is 100%, so the input schema already documents both parameters ('language' and 'tag') with clear descriptions. The description adds minimal value by mentioning filtering by language or tags, but doesn't provide additional syntax, format details, or usage examples beyond what the schema provides.

    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's purpose with a specific verb ('List') and resource ('snippets'), and mentions filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'create_snippet' or 'delete_snippet' beyond implying it's a read operation, which prevents a perfect score.

    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 like 'create_snippet' or 'delete_snippet'. It mentions filtering options but doesn't specify contexts, prerequisites, or exclusions for usage, leaving the agent without clear direction.

    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

mcp-code-snippets MCP server

Copy to your README.md:

Score Badge

mcp-code-snippets MCP server

Copy to your README.md:

Latest Blog Posts

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/ngeojiajun/mcp-code-snippets'

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