mcp server
Distil
Reversibly compress tool outputs to recoverable handles; expand to exact original bytes on demand.
Description as published by the maintainer. Source
- version 1.30.0
- active
active — Most recent push to the repository was 2026-08-08.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 11 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-08 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 2 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.30.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-25 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-07-25 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
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noisefloor — is this number real?
— last commit 2026-08-02, shares conformal-prediction
Is this number real, or is it noise? Peek-safe A/B tests, change detection, honest forecasts.
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llmtrim
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Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
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io.github.CodesWhat/portkey-admin-mcp
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Full Portkey Admin API MCP server — configs, prompts, keys, analytics, and more.
These share tags the maintainers applied themselves, such as agents, conformal-prediction, ai-infrastructure, llmops. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: agents, ai-infrastructure, anthropic, claude-code, conformal-prediction, context-compression, cost-optimization, llm, llmops, mcp, openai, prompt-caching, token-optimization.
This record as data
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