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zelinsky-alexander

MCP Drift Lab

by zelinsky-alexander
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    The tools are mostly distinct: read_demo_record reads a record, while write_demo_note and send_demo_message both write to the sink but with different purposes (note vs. simulated message). The two write tools could be slightly confused at first glance, but descriptions clarify the difference.

    Naming Consistency5/5

    All tool names follow a consistent verb_demo_noun pattern (read_demo_record, write_demo_note, send_demo_message), using lowercase with underscores. The verbs are clear and the pattern is uniform.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped for a synthetic demo environment. Each tool serves a clear purpose, and the count falls well within the expected 3-15 range.

    Completeness3/5

    The server provides a read operation for records but no corresponding write for records, and the write tools for notes/messages lack any read or list mechanism. This creates a notable gap, as written data cannot be retrieved, though the demo nature of the server mitigates the impact.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 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 failing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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

  • 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 transparency burden. It only says 'Read' without disclosing the return format, error behavior for invalid IDs, or explicit confirmation that it is a read-only operation with no side effects.

    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 concise sentence that immediately states the action and object. No filler or redundant details, making it highly efficient.

    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?

    The tool is simple (one parameter, no annotations, no output schema), and the description states the basic purpose. However, because there is no output schema, the description should ideally mention what the agent can expect as a return value or whether an error is raised for missing records.

    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 schema already covers 100% of the parameter with a clear description ('Synthetic record identifier'). The tool description adds only 'numeric identifier,' which restates the schema's integer type without providing additional context or constraints.

    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 uses a specific verb ('Read') and resource ('synthetic demo record') with a clear scope ('by numeric identifier'). This distinctly separates it from sibling tools like write_demo_note and send_demo_message, which involve writing and sending.

    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?

    Usage context is implied by the verb 'read' versus 'write' and 'send' in sibling names, but there is no explicit guidance on when to choose this tool over alternatives or any mention of prerequisites or exclusions.

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

  • Behavior3/5

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

    No annotations are present, so the description carries the burden. It discloses 'append-only', which is a key behavioral trait (notes cannot be overwritten/deleted), but does not mention permissions, return values, or error conditions. This is adequate for a simple write tool but leaves 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?

    Single sentence with every word informative; front-loaded action 'Store a note' and qualifier 'append-only' conveys key behavior efficiently.

    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?

    The tool is simple (one param) and the description covers its core nature (append-only write to a synthetic sink). No output schema exists, but for a write action, the lack of return value description is acceptable. It could benefit from clarifying when to use vs siblings or any side effects, but overall sufficient.

    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 'Synthetic note text.' for the note parameter. The description's 'Store a note' adds no additional meaning beyond the schema. Baseline 3 applies since schema fully covers parameter.

    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 uses the specific verb 'Store' and identifies the resource as 'note in the append-only synthetic research sink', clearly distinguishing it from sibling tools like read_demo_record and send_demo_message. The 'append-only' qualifier further clarifies the operation's nature.

    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?

    There is no explicit comparison to sibling tools or exclusions, but the description implies this tool is for writing notes rather than reading records or sending messages. The 'synthetic research sink' context gives some situational clue, but no when-to-use guidance is given.

    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 properly discloses the critical safety behavior: 'No message is transmitted.' It also clarifies the synthetic nature of the operation, which is important for a tool whose name might imply real sending. This goes beyond the schema.

    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 two short sentences, front-loaded with the action and ending with a critical safety caveat. Every word contributes value.

    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?

    For a simple tool with two fully described parameters, the description is sufficient: it states the action, the synthetic sink, and that nothing is actually transmitted. It lacks an explicit return value, but that is not essential for this type of operation.

    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 each parameter having a clear description. The tool description adds no additional parameter-level detail, so the baseline of 3 applies.

    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 records a simulated outbound message in the synthetic sink, using a specific verb and resource. It distinguishes itself from siblings (read_demo_record, write_demo_note) by focusing on sending a demo message.

    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?

    Usage context is implied by 'simulated' and 'synthetic sink' indicating a demo/testing environment, but there is no explicit guidance on when to use this tool versus alternatives. No exclusions or alternative references are provided.

    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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  • Evaluate tool definition quality.

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