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arizen-dev

deepseek-mcp

by arizen-dev
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.5.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'deepseek' for fast, cheap tasks and 'advise' for deep reasoning. Descriptions explicitly state when to use each, leaving no ambiguity.

    Naming Consistency2/5

    The tool names follow no consistent pattern: 'deepseek' is a product name, while 'advise' is an imperative verb. They do not share a common structure, which could confuse agents expecting a uniform naming convention.

    Tool Count5/5

    With only 2 tools, the server is well-scoped for its purpose of providing two complementary reasoning modes. The count is appropriate and not excessive or insufficient.

    Completeness5/5

    The server covers the full spectrum of reasoning needs: fast execution via 'deepseek' and deep reasoning via 'advise'. There are no obvious gaps in the tool surface for the stated domain.

  • Average 4.8/5 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 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.

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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

  • Behavior5/5

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

    Description adds context beyond annotations (readOnlyHint, idempotentHint) by specifying 'non-thinking mode' and typical latency 2-5s. No contradictions; aligns well with annotations.

    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?

    Extremely concise with no wasted words. Front-loaded with key facts: speed, mode, latency. Lists are clear and organized.

    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?

    Missing explicit mention of return format or output structure, which would be helpful since no output schema is provided. Otherwise, context is sufficient for a simple, read-only 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 coverage is 100%, so baseline 3 is appropriate. The description does not add parameter-specific meaning beyond what the schema provides, but the schema itself is adequate.

    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?

    Clearly states the tool's purpose: 'Fast, cheap task execution via DeepSeek V4 Flash (non-thinking mode)'. Lists specific use cases and distinguishes from the sibling 'advise' by noting it is for deeper reasoning.

    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?

    Explicitly provides 'Best for:' and 'Not for:' lists, gives typical latency, and directs to 'advise' for scenarios needing deeper reasoning, offering clear guidance on when to use this tool versus alternatives.

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

  • Behavior5/5

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

    The description discloses that the tool returns a structured response with CONCLUSION/REASONING/WATCH OUT, that it is more expensive (~6x flash) and slower (60-120s), and that show_reasoning prepends a reasoning block. These details go beyond the readOnlyHint=true annotation, providing rich behavioral context.

    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 concise yet packed with essential information. It front-loads the purpose, then provides usage guidelines, defaults, cost, and speed. Every sentence adds value, and the structure is logical. No unnecessary filler.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has 4 parameters, 1 required, and no output schema, the description fully compensates by describing the output format (CONCLUSION/REASONING/WATCH OUT) and providing cost and speed. The agent has all necessary context 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.

    Parameters5/5

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

    The input schema covers all 4 parameters (100% coverage). The description adds extra meaning: it explains the effort levels in detail (exhaustive, full chain-of-thought, lighter thinking) and clarifies that show_reasoning adds a reasoning block. This adds significant value beyond the schema's descriptions.

    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 is for deep reasoning via DeepSeek V4 Pro with thinking mode. It differentiates from the sibling 'deepseek' by specifying when to use it: when deepseek is not sufficient, such as for judgment under ambiguity, architectural tradeoffs, etc. The verb 'reason' and resource 'DeepSeek V4 Pro' are specific.

    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?

    Explicitly says 'Use when deepseek is not sufficient' and lists scenarios. It also provides guidance on effort levels: defaults to effort=max for exhaustive reasoning, and suggests effort=medium or high for quicker reads. This clearly tells the agent when to use this tool versus alternatives.

    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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