Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ClaudeClear MCP Server Rewrite this paragraph to remove the Claude watermark"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for ClaudeRewrite - the #1 Claude watermark removal tool.
rewrite_textRewrites text to remove Claude's statistical watermark. Preserves meaning, facts, numbers, and code while breaking the token pattern detectors flag.
strip_image_metadataRemoves C2PA "Made with Claude" credentials, EXIF, XMP, ICC, and all other metadata from images. Supports JPG, PNG, WebP, GIF, TIFF.
Related MCP server: Claude Memory
npx claudeclear-mcpVariable | Required | Description |
| Yes (for rewrite) | Your API key from removeclaudewatermark.com/account |
| No | API base URL (default: |
| No | Set to |
Add to your Claude Code settings:
{
"mcpServers": {
"claudeclear": {
"command": "npx",
"args": ["claudeclear-mcp"],
"env": {
"CLAUDECLEAR_API_KEY": "your-api-key"
}
}
}
}CLAUDECLEAR_MCP_HTTP=1 CLAUDECLEAR_API_KEY=your-key npx claudeclear-mcpServer listens on port 3311 by default.
MIT
Rewrites a passage of text at the sentence and clause level, preserving meaning, facts, numbers, names, and code, while removing the statistical pattern that Claude content detectors flag. Use this to clean text copied from Claude, Claude Code, or the Claude API before publishing.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to rewrite. Up to ~30,000 characters per call. For longer documents, split into parts and call multiple times. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It reveals important behavioral traits: rewriting happens at sentence and clause level, meaning/facts/numbers/names/code are preserved, and the goal is to evade Claude content detection. This goes beyond a generic 'rewrite' and gives the agent a realistic picture of the transformation, though it stops short of describing output format or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence front-loads exactly what the tool does and what it preserves, while the second sentence gives a concrete use case. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter text transformation tool, the description covers the purpose, the transformation behavior, and the intended use case. It does not explicitly state that the output is the rewritten text, but this is implied by 'rewrites a passage' and the use-case sentence. Minor gap, but overall sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the only parameter, text, including the upper bound of ~30,000 characters and the suggestion to split longer documents. The description adds no additional parameter-level meaning, so the baseline of 3 applies because schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('rewrites'), a precise resource ('a passage of text'), and the level of operation ('sentence and clause level'), plus the intended effect: removing the statistical pattern that Claude content detectors flag. This is clearly distinguished from the sibling tool strip_image_metadata, which operates on images rather than text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent when to use this tool: 'Use this to clean text copied from Claude, Claude Code, or the Claude API before publishing.' It does not explicitly state when not to use it or name alternatives, but the sibling tool is obviously about image metadata, so the usage boundary is clear enough without an explicit exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Removes C2PA 'Made with Claude' credentials, EXIF, XMP, ICC, and all other metadata from images. Supports JPG, PNG, WebP, GIF, TIFF. Free to use, no API key required. Returns the cleaned image as a downloadable file.
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | URL of the image to strip metadata from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the destructive effect (removes metadata), supported formats, the lack of authentication requirements, and the output as a downloadable cleaned image. Minor gaps remain, such as whether the original URL image is modified or just the returned copy, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences with no wasted words. The core action and metadata types are front-loaded, followed by format support, access requirements, and output behavior. Every sentence provides useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers input format, supported file types, authentication needs, and the returned result. It does not specify output file format or size limits, but these are not necessary for basic correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with image_url already documented as 'URL of the image to strip metadata from.' The description does not add new parameter-level detail, but the schema alone sufficiently explains the only parameter, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Removes') with a clear resource ('metadata from images') and enumerates the exact metadata types (C2PA, EXIF, XMP, ICC, and all other metadata). It also lists supported formats, making it unmistakably distinct from the sibling tool rewrite_text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: whenever an image needs its metadata removed. It provides supporting context like supported formats and no API key requirement, but it does not explicitly state when not to use it or compare it against alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.1rewrite_textstrip_image_metadataThe two tools are completely distinct: one operates on text content by rewriting, the other strips metadata from image files. There is no overlap or ambiguity in choosing between them.
Both tools follow the same verb_noun snake_case pattern (rewrite_text, strip_image_metadata), making the API predictable. The verbs clearly indicate the action and the nouns specify the target.
With only two tools the server is minimal, and while each tool serves a distinct purpose, the set feels thin for a general 'remove Claude traces' offering. It is acceptable for a narrow special-purpose server but is on the small side.
The server covers the two main content types that would carry Claude fingerprints: text and images. Minor gaps exist, such as document/PDF metadata cleaning or a verification/detection tool, but the core workflows are covered.
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