Skip to main content
Glama
MyBlockcities

MCP Server Template for Cursor IDE

by MyBlockcities
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one fetches website content, while the other returns a static mood response. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool for each task.

    Naming Consistency3/5

    The tools use different naming conventions: 'mcp_fetch' follows a prefix_verb pattern, while 'mood' is a simple noun. This mixed style lacks a predictable pattern, though both names are readable and descriptive of their functions.

    Tool Count2/5

    With only two tools, the server feels thin and under-scoped for a template intended for Cursor IDE, which typically involves more complex operations. This minimal set may not adequately cover common IDE-related tasks, suggesting a mismatch in scope.

    Completeness2/5

    Inferred as a template for IDE integration, the tool surface is severely incomplete. It lacks core functionalities like file manipulation, code analysis, or project management, leaving significant gaps that would hinder agent workflows in an IDE context.

  • Average 3.3/5 across 2 of 2 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 status not available

  • 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 full burden but only states basic functionality. It doesn't disclose important behavioral traits like error handling, rate limits, authentication needs, content type handling, or what 'returns its content' specifically means (HTML, text, metadata).

    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 - just 7 words that directly convey the core functionality. Every word earns its place with zero wasted text, making it perfectly front-loaded and efficient.

    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 that fetches websites with no annotations and no output schema, the description is insufficient. It doesn't explain what 'content' means, how errors are handled, what formats are supported, or any limitations. The agent would have to guess about important behavioral aspects.

    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 description doesn't add any parameter information beyond what's already in the schema (which has 100% coverage). The schema fully documents the single 'url' parameter, so the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't need to.

    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 ('fetches') and resource ('a website'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'mood' (which appears unrelated), so it doesn't fully achieve sibling distinction.

    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 or any contextual prerequisites. It simply states what the tool does without indicating appropriate use cases or limitations.

    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 of behavioral disclosure. It reveals key traits: the server will 'always respond with a cheerful message and a heart ❤️' and 'it's always happy!' which discloses predictable behavior. However, it doesn't mention rate limits, authentication needs, or other operational constraints that would be helpful for a complete behavioral picture.

    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 front-loaded with the core purpose. Every word earns its place - 'Ask the server about its mood' establishes function, and 'it's always happy!' adds useful behavioral context without redundancy.

    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 (single parameter, no output schema, no annotations), the description provides adequate context. It explains what the tool does and the predictable response behavior. For a straightforward mood inquiry tool, this is reasonably complete, though it could benefit from mentioning the response format more explicitly.

    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 the single 'question' parameter with examples. The description doesn't add any parameter-specific information beyond what's in the schema. This meets the baseline of 3 for high schema coverage where the description doesn't need to compensate.

    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: 'Ask the server about its mood' - a specific verb ('Ask') and resource ('server's mood'). It distinguishes from the sibling tool 'mcp_fetch' by focusing on mood inquiry rather than data fetching. However, it doesn't explicitly contrast with the sibling, keeping it at 4 instead of 5.

    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 context through 'it's always happy!' which suggests this is for cheerful interactions, but doesn't provide explicit guidance on when to use this versus alternatives. No when-not-to-use scenarios or comparison to sibling tools is mentioned, leaving usage somewhat implied rather than clearly defined.

    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-server-heroku MCP server

Copy to your README.md:

Score Badge

mcp-server-heroku 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/MyBlockcities/mcp-server-heroku'

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