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arikusi

Deepseek MCP Server

by arikusi
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.1.0

  • Disambiguation5/5

    Each tool has a distinctly separate purpose: chat, fill-in-the-middle completion, and session management. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tools follow a consistent 'deepseek_verb' pattern in snake_case, making it easy to predict functionality from the name.

    Tool Count5/5

    Three tools cover the core interactions (chat, completion, session management) without being too few or too many for the server's stated scope.

    Completeness4/5

    The set provides the essential operations for the domain, but lacks auxiliary features like model listing or cost queries, which would enhance completeness.

  • Average 4/5 across 3 of 3 tools scored.

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

    • 2 of 2 community issues answered or closed in the last 6 months
    • 61 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • This repository includes a glama.json configuration file.

  • 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 exist, so the description alone must convey behavioral traits. It lists actions but does not mention impacts like data loss on deletion, authentication needs, or rate limits. Basic transparency but insufficient for a management tool with destructive potential.

    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 concise with two sentences, front-loading the purpose. Could be slightly more structured but is efficient and avoids unnecessary words.

    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?

    No output schema is provided, yet the description does not specify what the 'list' action returns (e.g., session IDs). The tool is fairly complete given the schema and context signals, but missing output details and error conditions.

    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 coverage is 100% with both parameters described. The description adds useful context about sessions used with deepseek_chat, but adds no new details beyond what the schema already provides for parameters.

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

    Purpose5/5

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

    The description clearly states the tool manages multi-turn conversation sessions with specific actions (list, delete, clear), and distinguishes from the sibling tool deepseek_chat by noting sessions are used with that tool's session_id parameter.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

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

    The description explains that sessions store history used by deepseek_chat, providing context for when to use this management tool. However, it lacks explicit when-not-to-use guidance or alternative conditions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden. It discloses non-thinking mode, Beta endpoint, 4K token cap, aliases resolution, automatic cost tracking, and model fallback with circuit breaker resilience.

    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 concise at three sentences, covering key points without fluff. However, it could be slightly better structured by front-loading the core purpose.

    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 presence of an output schema (so return values need not be explained) and the description covering cost tracking and resilience, it is sufficiently complete for the complexity of the 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?

    Schema description coverage is 100%, so baseline is 3. The description adds context like 'Ideal for code completion' but does not add meaning beyond what the schema provides for parameters.

    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 it is for Fill-in-the-Middle completion with DeepSeek V4, suitable for code completion and content infilling. It distinguishes itself from siblings by noting it has no thinking mode, but does not explicitly differentiate from deepseek_chat or deepseek_sessions.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

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

    The description provides context for when to use (code completion, content infilling) and mentions it runs in non-thinking mode, implying that for thinking tasks, deepseek_chat should be used. However, it does not explicitly state when not to use or compare to deepseek_sessions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Despite no annotations, the description discloses key behavioral traits: model capabilities (1M context, up to 384K output), thinking mode, streaming, cost tracking, and circuit breaker resilience. However, it does not mention error handling, rate limits, or idempotency, which would improve transparency.

    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 dense paragraph but remains informative and front-loaded with purpose. It could be more scannable with bullet points, but every sentence adds value. No superfluous content.

    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 complexity (11 parameters, output schema present), the description covers features, model options, and capabilities. It lacks details on error handling but is otherwise complete enough for an AI agent to select and invoke the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, so baseline is 3. The description adds significant value beyond schema by explaining model aliases, default thinking behavior, JSON output hints, and session_id auto-creation. This enhances parameter understanding and usage.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: 'Chat with DeepSeek V4 models'. It specifies verb (chat), resource (DeepSeek V4 models), and key capabilities. It also distinguishes from the sibling tool 'deepseek_sessions' by focusing on chat completion rather than session management.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

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

    The description provides clear guidance on when to use the tool (chat completion tasks) and explains model alias mapping (deepseek-chat -> v4-flash, deepseek-reasoner -> v4-flash thinking). It implies when not to use (e.g., if session management is needed, use deepseek_sessions), though it does not explicitly state exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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.

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