Laso Finance

send_feedback

Send feedback about the API

Tell the humans running Laso what worked, what was confusing, and what you wish existed. It lands on their dashboard, so it is the best channel for API friction and feature requests.

Requires at least one completed real action (a settled deposit, purchase, or withdrawal); at most 5 entries per 24 hours.

Input Schema

Name	Required	Description
`rating`	No	A 1-5 satisfaction rating. Optional; ignored when not an integer in range.
`endpoint`	No	Which API endpoint or route the feedback is about. Optional. Capped at 120 characters.
`feedback`	Yes	The main free-text feedback. Required. Sanitized and capped server-side (max 2000 characters).
`how_it_went`	No	How it went. Optional. Capped at 500 characters.
`what_they_want`	No	What you were trying to do. Optional. Capped at 500 characters.

Output Schema

Name	Required	Description	Default
`ok`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 26, 2026

removedInput schema / properties / auth_token

Removed value: -{
-  "description": "Laso credential. Only if the MCP connection has no Authorization header.",
-  "type": "string"
-}

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "ok": {
+      "type": [
+        "boolean",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}

AddedSep 26, 2026
RemovedSep 26, 2026
First observedSep 26, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The description adds meaningful behavioral context beyond annotations: the feedback lands on a human dashboard, there is a per-24-hour rate limit, and a real prior action is required. This is useful for an agent deciding whether and how often to call this tool. It doesn't cover authentication or side effects beyond emphasizing that it is a channel to humans, but it's still solidly transparent.

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?

Three concise sentences front-load the core purpose, then add audience and constraints. Every sentence earns its place and there is no filler or redundancy.

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?

For a simple feedback submission tool, the description covers purpose, audience, eligibility, and rate limits, while the input schema documents all parameters and an output schema exists. Nothing needed for correct invocation is missing.

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 description and example, so the description doesn't need to restate parameter details. The prose about 'what worked, what was confusing, and what you wish existed' loosely maps to the optional free-text fields but adds no syntax-level meaning. A baseline 3 is appropriate since the schema carries the heavy lifting.

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 action ('send feedback about the API') and the destination (the humans running Laso's dashboard). It also positions the tool as the channel for API friction and feature requests, which distinguishes it from all sibling tools that perform financial or account operations.

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?

It explicitly indicates when to use the tool (for feedback, friction, feature requests) and imposes concrete eligibility and rate-limit constraints (requires a completed real action, max 5 entries per 24 hours). It doesn't name alternatives or exclusions, but no comparable feedback tool appears among the siblings, so the guidance is adequate.

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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