Skip to main content
Glama
KasarLabs

Cairo Coder

by KasarLabs
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity or overlap between tools. The tool 'assist_with_cairo' has a clear and distinct purpose focused on Cairo and Starknet development assistance.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'assist_with_cairo' follows a clear verb_noun pattern and is descriptive of its function.

    Tool Count2/5

    A single tool is too few for a server named 'Cairo Coder' that aims to assist with development tasks. This suggests a thin surface that may not adequately cover the domain of Cairo/Starknet development, such as lacking separate tools for code analysis, debugging, or testing.

    Completeness2/5

    The tool set is severely incomplete for the apparent domain. While 'assist_with_cairo' covers general assistance, there are obvious gaps in CRUD/lifecycle coverage, such as no tools for compiling, deploying, testing, or managing contracts, which are essential for development workflows.

  • Average 3.8/5 across 1 of 1 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

  • Behavior3/5

    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 describes the tool's function ('AI-powered analysis') and output ('returning helpful information to generate accurate code or explanations'), which covers basic behavior. However, it lacks details on limitations, error handling, or performance characteristics (e.g., response time, accuracy constraints), which would be valuable for an AI agent to know when invoking this tool.

    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 appropriately sized and front-loaded, with the core purpose stated first. Each sentence adds value: the first defines the tool, the second provides usage guidelines, the third explains the analysis process, and the fourth adds an extra use case. While efficient, the final sentence could be integrated more seamlessly, and there's minor repetition (e.g., 'accurate' appears twice), preventing a perfect score.

    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 complexity of the tool (AI-powered analysis with 3 parameters) and the absence of both annotations and an output schema, the description is moderately complete. It covers purpose and usage well but lacks details on behavioral traits (e.g., how the AI analysis works, potential limitations) and output format. Without an output schema, the description should ideally hint at what the tool returns, but it only vaguely mentions 'helpful information,' leaving gaps for an AI agent to understand the full 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?

    The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description does not add any additional meaning or context about the parameters beyond what the schema provides (e.g., it doesn't explain how 'query' interacts with 'codeSnippets' or 'history' in practice). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without parameter info in the description.

    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: 'Provides assistance with Cairo and Starknet development tasks through AI-powered analysis.' It specifies the verb ('assists with') and resource ('Cairo and Starknet development tasks'), making it easy to understand what the tool does. However, since there are no sibling tools mentioned, it cannot differentiate from alternatives, preventing a perfect score 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 Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on when to use the tool: 'Call this tool when the user's request involves **writing, refactoring, implementing from scratch, or completing specific parts (like TODOs)** of Cairo code or smart contracts.' It also includes additional use cases like understanding Starknet's ecosystem. This comprehensive guidance leaves no ambiguity about appropriate usage scenarios.

    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

cairo-coder-mcp MCP server

Copy to your README.md:

Score Badge

cairo-coder-mcp 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/KasarLabs/cairo-coder-mcp'

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