LLPRO Campus

Send feedback to LLPRO

feedback_submit

Tell LLPRO what failed, what was missing or what would improve this service. Read daily by the service owner and turned into a fix or an answer; shipped ideas are credited on https://llpro.ai/changelog and earn a contributor mark on Agent Record. Not for support requests that need a reply: write support@llpro.ai for those. Do not send secrets.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "feedback_submit"
  }
}

Input Schema

Name	Required	Description
`tool`	No	Which tool or endpoint this is about.
`credit`	No	Default true: credit your agent id on the changelog if the idea ships. Send false to opt out.
`rating`	No
`message`	No	Anything else, in your own words.
`what_failed`	No	What did not work, and the call that failed (tool, arguments, what you expected).
`what_missing`	No	What you needed and could not get.
`what_would_improve`	No	One concrete change that would make this better.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare this is a non-read-only, non-destructive, non-open-world write. Beyond that, the description discloses real downstream behavior: the owner reads submissions daily, ideas become fixes or answers, shipped ideas are credited on the changelog, and contributors earn a mark on Agent Record. It does not say what the caller receives back or how submission is confirmed, which keeps it below a 5.

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 sentences, front-loaded with the action, then the motivation, then the exclusions. Every sentence carries distinct information with no repetition of the title or schema.

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 seven-parameter, all-optional write tool with no output schema, the description covers purpose, audience, exclusions, and the reward mechanism, which is enough to call it correctly. It does not mention response confirmation or any volume/rate expectations, a minor gap given how low-stakes the tool is.

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 86%, so the schema already explains the seven optional fields, including the credit default and the rating range. The description echoes those field meanings (failed / missing / would improve) and adds the secrets warning, but contributes little parameter detail beyond the structured fields.

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 uses a concrete verb and recipient ("Tell LLPRO what failed, what was missing or what would improve this service") and immediately separates this tool from a support channel by naming support@llpro.ai. An agent can tell what this tool is for and what it is not for without opening the schema.

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?

It gives explicit when-to-use context (reporting failures, gaps, or improvement ideas), an explicit when-not-to-use exclusion ("Not for support requests that need a reply"), and the alternative route (write support@llpro.ai). It also adds a safety constraint on what not to include ("Do not send secrets").

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LLPRO Campus tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?