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Server Quality Checklist

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  • Latest release: v1.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: delegate_task is a stateless raw model call, while run_agent_task spawns a full agent with workspace access. The descriptions explicitly contrast them and cross-reference each other, leaving no ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (delegate_task, run_agent_task), using a single naming convention that is predictable and readable.

    Tool Count4/5

    With only 2 tools, the set is slightly under the typical well-scoped range, but given the server's narrow purpose of offering two modes of DeepSeek interaction, the count is reasonable and not excessive.

    Completeness4/5

    The tool surface fully covers its stated domain: a simple model query and a full agent execution. No critical operations are missing, though there are no extra lifecycle tools (e.g., cancel/status) that a broader agent workflow might expect.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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
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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

  • Behavior4/5

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

    With no annotations, the description carries the full burden of disclosing side effects. It clearly warns 'it can actually modify files in the given workspace' and explains it spawns a one-shot `dsh --profile headless` process. It does not describe return values or blocking behavior, but the key safety-relevant aspects are covered.

    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?

    Three sentences, each earning its place: first states purpose, second lists capabilities and side effects, third gives usage guidance and alternative. Front-loaded with the core action, no filler or repetition.

    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 (agent with shell, file modifications, sub-agents) and lack of output schema/annotations, the description is thorough: it covers capabilities, side effects, prerequisites, and alternative tools. It does not mention the return/result format, which would be helpful, but it is otherwise complete for an agent-invocation 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 the parameters (task, profile, workspace, timeout_ms, harness_root) are already fully documented. The description adds minimal extra meaning beyond schema; it mentions the harness_root environment variable and the workspace scope, which aligns with but does not exceed schema 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 opens with a specific verb+resource: 'Run a complete DeepSeek Harness coding agent on a task.' It also explicitly distinguishes from the sibling tool, noting 'use delegate_task for quick model-only questions,' which clarifies its unique scope.

    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?

    Provides explicit when-to-use guidance: 'Use this for real coding/workspace work; use delegate_task for quick model-only questions.' It also states prerequisites (a built deepseek-harness checkout, DEEPSEEK_HARNESS_ROOT or harness_root) which tells the agent when this tool is appropriate and what setup is required.

    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 and discloses key behavioral traits: has NO tools, cannot touch files, stateless, and returns the raw model answer. It doesn't mention error handling, rate limits, or the exact response structure, but the critical limitations are clearly stated for a simple delegation tool.

    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?

    Four short, front-loaded sentences: purpose first, then limitations, then alternative tool, then stateless usage note. Every sentence earns its place with no filler or redundancy.

    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?

    For a simple stateless delegation tool with five straightforward parameters and a single sibling, the description covers purpose, when to use it, the alternative tool, capabilities/limitations, and context-passing guidance. The response format is only 'return its answer', but given the absence of an output schema and the simplicity of the expected output, this is sufficient.

    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%, which sets a baseline of 3. The description adds value beyond the schema by instructing that the task should carry all context and that the optional system prompt can contain instructions, which directly supplements the task and system parameter descriptions. It doesn't add details for max_tokens or reasoning_effort, but the schema already covers them well.

    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 opens with a specific verb+resource: 'Send a task or question to the raw DeepSeek model' and states it returns the answer. It explicitly contrasts with the sibling run_agent_task by noting that shell/file/sub-agent work should use that alternative, which clearly differentiates this tool's purpose.

    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 gives explicit usage context ('Fast and cheap') and names the exact alternative for when not to use this tool: 'For work that needs a shell, file edits, or sub-agents, use run_agent_task instead.' It also provides practical guidance by explaining the tool is stateless, so all context must be placed in the task or system prompt.

    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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