Skip to main content
Glama
newideas99

RAT MCP Server

by newideas99
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

    The two tools have clearly distinct purposes: one checks the status of a response generation task, while the other initiates the generation of a response. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (check_response_status and generate_response), using snake_case throughout. The verbs 'check' and 'generate' appropriately describe their actions, and there are no deviations in style or convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent purpose of response generation through OpenRouter. A more complete surface might include tools for managing tasks, handling errors, or configuring parameters, but the current set is minimal and may limit agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for response generation tasks. While it covers initiating and checking status, it lacks tools for canceling tasks, retrieving results beyond status, handling errors, or managing task history. This will likely cause agent failures in more complex scenarios.

  • Average 3.1/5 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 for behavioral disclosure. It mentions the AI models involved (DeepSeek and Claude) and the platform (OpenRouter), but doesn't describe key behavioral traits like rate limits, authentication needs, response format, error handling, or whether it's a read/write operation. The description adds some context about the implementation but lacks crucial operational details.

    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, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for the tool's complexity and gets straight to the point. Every word earns its place by specifying both the action and the implementation method.

    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 tool has 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how to interpret results, error conditions, or operational constraints. For a tool that presumably generates AI responses through external services, more context about response format, limitations, and integration details would be needed.

    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 all 4 parameters. The description adds no parameter-specific information beyond what's in the schema. It doesn't explain how parameters interact (e.g., how 'clearContext' and 'includeHistory' relate) or provide usage examples. This meets the baseline of 3 when schema coverage is complete.

    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 ('Generate a response') and specifies the implementation method ('using DeepSeek's reasoning and Claude's response generation through OpenRouter'). It distinguishes from the sibling tool 'check_response_status' by focusing on generation rather than status checking. However, it doesn't specify what type of response is generated (e.g., text completion, analysis, etc.), keeping it at a 4 rather than a perfect 5.

    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 when to use it over other response generation methods or when the sibling tool 'check_response_status' would be appropriate. There's no context about use cases, prerequisites, or limitations, leaving the agent with minimal usage direction.

    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 the tool checks status but doesn't explain what the status values mean, whether it's read-only or has side effects, or any rate limits or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

    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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to grasp immediately.

    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 tool's complexity (checking task status) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what status information is returned, potential outcomes, or error conditions, leaving the agent without enough context to fully understand the tool's behavior and results.

    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% description coverage, with the 'taskId' parameter clearly documented as 'The task ID returned by generate_response.' The description adds no additional parameter semantics beyond this, so it meets the baseline score of 3 where the schema does the heavy lifting.

    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 ('check') and resource ('status of a response generation task'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'generate_response' beyond the implied relationship, 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 Guidelines3/5

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

    The description implies usage context by referencing 'taskId returned by generate_response,' suggesting this tool should be used after initiating a task with its sibling. However, it lacks explicit guidance on when to use it versus alternatives or any prerequisites beyond the task ID, leaving some ambiguity.

    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

Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP MCP server

Copy to your README.md:

Score Badge

Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-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/newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP'

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