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lends-mcp-server

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by LendsProtocol
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Lends Math Max Borrow

lends_math_max_borrow
Read-onlyIdempotent

Calculate the maximum leUSD debt you can borrow based on your collateral value and the borrow LTV ratio.

Instructions

Calculate maximum raw leUSD debt at a borrow LTV.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "valueLeusd": "<valueLeusd>",
      "borrowLtvBps": "<borrowLtvBps>"
    },
    "name": "lends_math_max_borrow"
  }
}

Input Schema

NameRequiredDescriptionDefault
valueLeusdYes
borrowLtvBpsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not contradict them and the safety profile is clear. The description adds the core computational intent but does not disclose output format, rounding behavior, or unit conventions. Given the annotations cover side effects, the description provides modest additional transparency.

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, front-loaded sentence with no filler or repetition. It states the action and the object efficiently, which is appropriate for a small math utility.

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 a simple pure function with only two parameters, and the annotations cover side effects, so the completeness bar is moderate. However, with no output schema and no parameter descriptions, an agent is left to infer input semantics and the meaning of the returned value. It is adequate for recognizing the tool's purpose but not fully sufficient for confident invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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

The input schema has 0% description coverage, and the description only alludes to 'leUSD' and 'borrow LTV' without explaining the two parameters directly. It does not clarify that borrowLtvBps is expected in basis points, what 'raw' means, or how the string-integer inputs should be interpreted. The parameter names carry most of the semantic weight here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Calculate') and names the resource ('maximum raw leUSD debt') along with the governing input ('borrow LTV'). It is clearly a math helper rather than a state-reading or transaction-preparation tool, and is distinguishable from siblings like lends_math_health_factor by name. However, it does not explicitly contrast itself with other math tools, and the qualifier 'raw' is domain jargon.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as lends_math_collateral_value or lends_prepare_borrow. There are no conditions, prerequisites, or exclusions stated, so an agent must infer usage from the tool name alone. This leaves the selection logic entirely implicit.

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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