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AlanHuang168

mcp-ess-proposal

by AlanHuang168
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Python
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Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Only one tool is exposed, so there is no possibility of confusing it with another tool. The tool's purpose is clearly stated and unambiguous.

    Naming Consistency5/5

    The single tool name follows a clean snake_case verb_noun pattern: generate_ess_proposal. There are no other names to introduce inconsistency.

    Tool Count3/5

    A single tool makes the server feel thin and leaves no room for composing related proposal operations. The scope is focused, but the tool surface is borderline for an MCP server.

    Completeness4/5

    The server covers the core proposal-generation workflow directly. Minor gaps exist around supporting operations like input validation or scenario comparison, but agents can work around them because the one tool returns a complete result.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 6 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.

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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 burden of behavioral disclosure. It adds two meaningful traits: 'deterministic calculation' (reproducible output, no randomness) and 'preliminary' (output is an early estimate, not a final commitment). It does not discuss side effects, but as a generation tool the absence of mutating language is sufficient.

    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?

    A single sentence communicates the tool's purpose, inputs, and method without any filler. The core action is front-loaded, and every word serves a purpose. This is an appropriately concise definition.

    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 rich input schema (all 8 parameters documented, required fields, anyOf constraint) and the presence of an output schema, the description adds the essential purpose and behavioral context. No needed information for correct invocation is missing, and there are no sibling tools causing routing ambiguity.

    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 baseline is 3. The description only groups parameters at a high level ('customer and consumption inputs') without adding syntax, constraints, or meaning beyond what the schema already provides. It neither harms nor significantly enriches parameter understanding.

    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 ('Generate') and a clear resource ('preliminary solar and energy-storage proposal'), and further specifies inputs ('structured customer and consumption inputs') and method ('deterministic calculation'). It leaves no ambiguity about what the tool does, even without sibling tools to differentiate against.

    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 gives clear context for when this tool is appropriate: generating a preliminary proposal from structured inputs. While it does not explicitly state when not to use it or name alternatives, there are no sibling tools, so exclusions are unnecessary and the implied usage is straightforward.

    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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  • 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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