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lendwise-fi

lendwise

by lendwise-fi
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TypeScript

Server Quality Checklist

67%
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  • Latest release: v0.1.3

  • Disambiguation5/5

    Both tools have distinct purposes: one lists available markets, the other optimizes allocation. There is no ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern: list_market_universe and optimize_allocation.

    Tool Count2/5

    Only 2 tools for a domain like yield optimization feels insufficient. The scope may be narrow, but typically more tools (e.g., for execution, historical data) would be expected.

    Completeness2/5

    The server lacks tools for executing allocations, retrieving market details, or managing portfolios. Significant gaps exist for practical use.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 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?

    Annotations already provide strong safety hints (readOnlyHint, idempotentHint, destructiveHint false), so the description's burden is lower. The description adds behavioral context beyond annotations by specifying what is returned (per-market amounts, blended APY, 6-month yield), which informs the agent about result structure. No contradictions.

    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 sentence that efficiently conveys the tool's purpose, inputs, and outputs. It is front-loaded and contains no fluff. Every part serves a 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 tool's complexity (3 parameters, no output schema, but with annotations), the description adequately explains what the tool does and what it returns. It does not cover edge cases or algorithmic details, but for a read-only optimization tool, the essential information is present. Could mention that the result is a simulation, 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 coverage is 100%, meaning all parameters are documented in the schema. The description does not add significant meaning beyond the schema; it only briefly mentions 'maximise yield' and 'target diversification', which align with schema descriptions. Baseline of 3 is appropriate.

    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: 'Split an amount across chosen markets to maximise yield at a target diversification, returning per-market amounts, the blended APY and a projected 6-month yield.' It uses a specific verb (split/optimize) and resource (allocation), and distinguishes from sibling 'list_market_universe' which lists markets rather than optimizing an allocation.

    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?

    The description implies the tool is used for optimizing yield given a diversification target, but it does not explicitly state when to use it versus the sibling tool 'list_market_universe' or provide when-not-to-use or alternative guidance. The usage context is implied but not clarified.

    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?

    Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds context about returning market counts and being the source for filter values, complementing annotations without contradicting them.

    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?

    Two sentences, each serving a distinct purpose: first describes what the tool does, second gives critical usage guidance. No fluff or wasted words.

    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 one optional parameter and no output schema, the description is adequate. It explains purpose and usage context, though it could explicitly differentiate from the sibling tool 'optimize_allocation'.

    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?

    Input schema has 100% coverage for the single parameter 'kind', including a description and enum. The tool description does not add additional parameter-specific information beyond what the schema provides, so baseline score 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 it lists every asset, chain, and protocol tracked by Lendwise with market counts. It distinguishes itself from the sibling 'optimize_allocation' by indicating this tool is for enumeration, not optimization.

    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 instructs 'Call this FIRST' and explains that filter values for find_best_markets must come from this tool, not from memory. This provides clear context on when to use it versus other tools.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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