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xrplme.online MCP Server

xrplme_submit_survey

Submit survey feedback from an AI agent. FREE (we pay you for testing!).

Args:
    agent_id: Your AI agent identifier
    country_slug: Country you tested (e.g., "thailand")
    data_quality: Rating 1-5
    completeness: Rating 1-5
    pricing_fair: "yes", "no", or "depends_on_country"
    would_pay_again: "yes", "no", or "maybe"
    broken_fields: Text description of any broken fields
    improvements: Text suggestions for improvements

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
completenessNo
country_slugYes
data_qualityNo
improvementsNo
pricing_fairNo
broken_fieldsNo
would_pay_againNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It states submission is FREE, which is a useful economic fact, but says nothing about whether submission is one-shot or updatable, what happens on duplicate agent_id/country pairs, or what the caller receives. For a mutation tool with zero annotation coverage this is a substantial gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action and benefit, followed by a compact args list; each line earns its place. The marketing parenthetical is minor waste but does convey that submission is free.

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?

For an 8-parameter write tool with no output schema and no annotations, the description documents all inputs reasonably but omits submission semantics (repeat submissions, required fields, relationship to get_survey_questions). Adequate but with clear gaps.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must carry all parameter meaning — and it does list every parameter with useful hints: country_slug example ('thailand'), data_quality 'Rating 1-5', and explicit allowed values for pricing_fair and would_pay_again. It omits agent_id format and does not mark which fields are required, which the schema conveys separately.

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 first sentence gives a clear verb+resource: submitting survey feedback. This is inherently separable from the sibling get_/list_ tools, though no sibling is named explicitly and the parenthetical 'we pay you for testing!' adds noise rather than clarity.

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?

The description never says when to submit versus when to gather data first — notably a sibling xrplme_get_survey_questions exists and presumably should be consulted before submitting, but this is not mentioned. No exclusions, prerequisites, or alternative guidance are provided.

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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