RateXp: A New Open-Source MCP Tool to Collect Feedback for Claude Agentic Skills

A fresh GitHub repository is trying to solve a quietly urgent problem for teams building agentic AI: how do you know if your Claude-powered skill actually works well in the wild? The answer, according to the newly released RateXp, is to weave a lightweight feedback loop directly into the skill's runtime.

What Happened: RateXp Enters the Agentic Skill Ecosystem

On April 1, 2025, a developer named HikmetB1 published the first commit of RateXp to GitHub — an open-source Python tool with zero stars at time of writing and a very specific mission. The repo describes it as “a feedback collection solution for agentic skills.” The core idea is simple but powerful: when you ship an agentic skill (defined via a SKILL.md file), you also pair it with a small MCP (Model Context Protocol) client. At the end of the interaction, that client prompts the user for a rating, and — with explicit consent — can collect the full conversation in a redacted form.

The repo is tagged with agentic-skills, claude, feedback, mcp, and trajectory, signaling a tight alignment with the growing ecosystem around Anthropic API and the MCP protocol for Claude-based agents. While still completely unproven, the appearance of RateXp reflects a maturing need: as agentic workflows move from demos to production, developers need structured feedback to iterate on prompts, tool use, and end-user experience.

Why Feedback for Agentic Skills Matters Right Now

Agentic skills — self-contained, reusable capabilities that let AI agents perform complex tasks — are becoming a focal point inside tools like Claude Code and custom MCP servers. But unlike a typical SaaS feature, an agentic skill’s behavior is probabilistic, context-dependent, and evolves across runs. Without direct signal from users, developers are flying blind.

RateXp addresses this by making feedback gathering a first-class part of the skill lifecycle, not an afterthought. This matters to:

• Developers fine-tuning prompts and MCP server logic — they need to know which trajectories succeed or frustrate.
• Founders and product leads building internal AI tools — they require guardrails and quality signals before scaling.
• Marketers and growth operators experimenting with agent-led workflows — they want to measure task completion and user satisfaction without building custom analytics.

How RateXp Likely Works (Based on the Repo)

The repository is sparse in documentation, but the summary and topic tags give a clear blueprint. The tool appears to be structured around a minimal MCP client that reads a SKILL.md definition, executes the skill, and then injects a rating prompt. The key privacy note — collecting the full conversation only with consent and with a redaction step — suggests basic safeguards for user privacy and compliance.

At a high level, the envisioned flow is:

1. You define your agentic skill in a standard SKILL.md file.
2. You bundle RateXp’s MCP client alongside the skill.
3. When a user interacts with the skill through a Claude-powered agent, the client surfaces a rating request at the end of the session.
4. If the user agrees, the conversation (redacted) is saved for later analysis.

The tool is written in Python, which lowers the barrier for developers already using Python-based MCP servers and the Anthropic API. No configuration details, storage backends, or dashboard are mentioned — the repo appears purely as a barebones utility at this early stage.

Who Stands to Gain the Most from RateXp

• Early-stage AI founders testing agentic MVPs with real users can get immediate quality signals without building custom telemetry.
• Developer tools teams using Claude Code internally want to understand which prompts and tool chains lead to accepted outputs and where users drop off.
• AI workflow builders experimenting with multi-step autonomous tasks need trajectory-level feedback to debug and optimize performance.

Anyone already investing in agentic skills for Claude will recognize the feedback gap. RateXp, even in its raw state, offers a pattern they can adopt or fork.

Practical Ways to Pilot RateXp in Your Workflow

Because the project is extremely new, rigorous production use isn’t advisable yet. However, curious teams can start experimenting:

• Integrate with a sandboxed internal skill: Wrap a low-risk agentic skill (like a documentation Q&A bot) with the RateXp MCP client and observe the feedback flow and redaction behavior.
• Compare against manual feedback collection: Use RateXp alongside a simple form to gauge whether embedded in-skill ratings yield higher response rates.
• Audit the code for privacy guarantees: Since consent and redaction are central to the offering, review how the client handles data before entrusting it with sensitive conversations.
• Extend for your storage needs: The barebones nature means you’ll likely need to add your own logging or database integration; treat it as a starting point, not a full solution.

Key Limitations and Open Questions

Given the repo’s age and lack of adoption, several risks and unknowns stand out:

• Production readiness: No tests, CI, or usage data exist. RateXp should be considered an experimental prototype.
• Consent UX: The exact mechanism for obtaining and recording consent isn’t documented. Poor implementation could violate privacy norms or platform terms.
• Redaction quality: The word “redact” appears but without specifics — does it strip PII via regex, a local LLM call, or a placeholder approach? Incomplete redaction is a serious risk.
• Skill format coupling: The tool appears tightly tied to SKILL.md, meaning skills that don’t adopt this exact convention may not benefit.
• No analytics pipeline: RateXp likely only collects the data; you’ll still need your own system to store, aggregate, and interpret ratings and conversation logs.
• Community and maintenance: A zero-star, single-commit project has no track record. Forks or community-driven improvements may be necessary for longevity.

How to Evaluate Feedback Infrastructure for Your AI Agents

Whether you adopt RateXp, fork it, or choose a different path, the underlying challenge is worth solving systematically. When assessing feedback tools for agentic AI, consider these dimensions:

• Consent and privacy integration: Does the tool make consent explicit and auditable? Is the redaction strategy transparent and robust enough for your data sensitivity?
• Signal type: A simple star rating may not capture nuanced failures like hallucination or missed tool calls. Look for ways to supplement with trajectory-level metadata.
• Ease of embedding: The tool should slot into your MCP server or agent runtime with minimal code changes — otherwise adoption among end-users will drop.
• Developer experience: Open-source Python code is friendly, but you’ll need logging, storage adapters, and possibly dashboards. The ecosystem for MCP observability is immature, so be ready to build integrations.
• Platform alignment: RateXp’s tight focus on Claude agentic skills and SKILL.md may fit well if you are already standardizing on the Anthropic / MCP stack. If you’re multi-model, a more agnostic approach might be warranted.

Projects like Claude Code and the Anthropic API ecosystem are rapidly advancing agentic patterns. Feedback infrastructure, though less glamorous than new model capabilities, will separate the experimental demos from reliable production agents. RateXp — tiny, unvalidated, and brand new — is one of the first signals that the community is starting to build that layer.

FAQ

What exactly is RateXp?

RateXp is an early-stage open-source Python tool that pairs an MCP client with your Claude agentic skill definition (SKILL.md) to ask users for a rating and optionally collect the redacted conversation for feedback purposes.

Does RateXp store user conversations automatically?

According to the repo summary, it only collects the full conversation if the user gives consent and the data is redacted. The implementation details of this redaction and storage are not yet documented, so assume manual inspection is required before using with sensitive data.

Which Claude tools can benefit from RateXp?

Any agentic workflow running inside Claude Code or custom MCP servers that can execute skills defined by SKILL.md files. If you’re building on the Anthropic API with MCP, it’s potentially compatible.

Is RateXp ready for production use?

No. It is a brand-new repository with no stars, no tests, and minimal documentation. Treat it as an experimental prototype and a promising pattern rather than a finished product.

What alternatives exist for feedback collection in AI agents?

At this early stage of agentic AI, most teams build custom telemetry using application logs, explicit user surveys, or monitoring tools. Purpose-built MCP-native feedback utilities like RateXp are just beginning to appear.