Skip to main content
Glama
niko91i

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

by niko91i
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
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 task, while the other initiates the task itself. There is no overlap or ambiguity between monitoring and execution functions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (check_response_status, generate_response) with clear action-oriented names. The naming is uniform and predictable across the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent scope of AI response generation with reasoning and status tracking. This minimal set may force agents to work around missing operations like error handling or configuration adjustments.

    Completeness2/5

    The toolset is severely incomplete for a response generation service. It lacks essential operations such as canceling tasks, retrieving task history, configuring generation parameters, or handling errors, which are typical in such AI workflow domains.

  • Average 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions using DeepSeek's reasoning and Claude's response generation, hinting at AI model integration, but fails to disclose critical traits like rate limits, authentication needs, response format, error handling, or cost implications. The description adds minimal behavioral context beyond the basic action, leaving significant gaps for a tool that likely involves external API calls.

    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 directly states the tool's action and the technologies involved. It is front-loaded with the core purpose and avoids unnecessary details. However, it could be slightly more structured by explicitly mentioning the input or output, but overall it earns its place without waste.

    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 AI model integration and the lack of annotations and output schema, the description is incomplete. It does not explain the return values, error cases, or how the response is formatted (e.g., text, JSON). For a tool with 4 parameters and no structured output information, the description should provide more context to guide 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%, with all parameters well-documented in the input schema. The description does not add any meaning beyond what the schema provides, such as explaining how 'showReasoning' interacts with DeepSeek's reasoning or clarifying the context management. With high schema coverage, the baseline score of 3 is appropriate, as the description offers no extra parameter insights.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'Generate[s] a response using DeepSeek's reasoning and Claude's response generation through OpenRouter,' which provides a clear verb ('Generate') and resource ('response') but lacks specificity about what kind of response or for what purpose. It distinguishes from the sibling tool 'check_response_status' by focusing on generation rather than status checking, but the purpose remains somewhat vague without context on the response type or domain.

    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 offers no guidance on when to use this tool versus alternatives, such as other AI models or direct API calls. It mentions using DeepSeek and Claude via OpenRouter, but does not specify scenarios, prerequisites, or exclusions. Without explicit usage context, the agent must infer based on the tool name and parameters alone.

    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. The description states it 'checks the status,' which implies a read-only operation, but it doesn't specify whether this is a polling mechanism, if there are rate limits, authentication requirements, or what the status values might be. 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, clear sentence: 'Check the status of a response generation task.' It is front-loaded and wastes no words, making it highly efficient and easy to parse.

    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 tool's moderate complexity (status checking with one parameter), no annotations, and no output schema, the description is minimally adequate. It identifies the purpose and relates to the sibling tool via the schema, but it lacks details on behavioral aspects like response format, error handling, or operational constraints, which are important for a status-checking tool.

    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 single parameter 'taskId' documented as 'The task ID returned by generate_response.' The description doesn't add any additional meaning beyond what the schema provides, such as format details or validation rules. With high schema coverage, the baseline score of 3 is appropriate.

    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: 'Check the status of a response generation task.' It specifies the verb ('Check') and resource ('status of a response generation task'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'generate_response' beyond the implied relationship.

    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 'task ID returned by generate_response' in the schema, suggesting this tool should be used after initiating a task with the sibling tool. However, it doesn't provide explicit guidance on when to use this tool versus alternatives or any prerequisites beyond the taskId parameter.

    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-deepseek-V3-et-claude-desktop MCP server

Copy to your README.md:

Score Badge

MCP-deepseek-V3-et-claude-desktop 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/niko91i/MCP-deepseek-V3-et-claude-desktop'

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