R_net LP risk evaluator for DeFAI agents. IL + Breakeven Corridor O(1). L402 Lightning paywall.
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Average 2.9/5 across 1 of 1 tools scored.
With only a single tool, there is no possibility of confusion between tool purposes. The tool has a clear, specific function.
The single tool name 'evaluate_pool' follows a verb_noun pattern that is consistent and descriptive. There are no other names to compare against.
One tool is on the borderline of being too few for a typical server. It feels thin for a decision engine, which might be expected to provide multiple related operations.
The tool surface is severely limited, providing only a single evaluation operation. Obvious supporting functionalities such as data retrieval, comparison, or scenario analysis are missing, which could hinder an agent's ability to perform comprehensive assessments.
evaluate_poolCInspectEvaluate LP position risk and calculate R_net & breakeven corridor.
| Name | Required | Description | Default |
|---|---|---|---|
| apy | Yes | Annual Percentage Yield of the pool (e.g. 0.20 for 20%) | |
| days_held | No | Number of days the position has been held (default: 30) | |
| price_ratio | Yes | Current price ratio compared to entry price (e.g. 0.85 for 15% price drop) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It does not disclose whether the tool is read-only, whether it modifies state, or any side effects or limitations. The description only states the high-level action without behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no fluff. It is efficient but could be slightly expanded for completeness without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a calculation tool with no output schema, the description lacks details about what R_net and breakeven corridor represent, or what the output format is. The tool requires 'days_held' parameter, but its default is not mentioned in the description. Overall, incomplete for the given complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The tool description adds no additional information beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool evaluates LP position risk and calculates R_net and breakeven corridor, which is a specific verb-resource combination. It is distinct from the sibling tool 'get_latest_audit' by nature, though no explicit differentiation is made.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs. alternatives, nor any exclusions or prerequisites. The agent receives no context about scenarios where this tool is appropriate.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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