Scans text for personally identifiable information — emails, phone numbers, SSNs, credit card numbers, physical addresses, names — and returns a redacted version. Built for agents sanitizing user content, support tickets, logs, or documents before storage, sharing, or feeding into another LLM call. Pay-per-call via x402 (USDC on Base): $0.01/call, no account or API key. tools/list and /openapi.json are free for discovery.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.6/5 across 1 of 1 tools scored.
Only a single tool exists, so there is no possibility of confusion between tools. The tool's purpose is clearly defined and distinct.
With only one tool named 'redact', the naming is consistent and follows a simple verb pattern. No inconsistencies arise.
One tool is appropriate for a focused server dedicated to PII redaction. It efficiently covers the core functionality without unnecessary extras.
The single tool fully addresses the server's purpose by detecting and redacting PII in text and JSON. All essential operations for the domain are covered.
redactAInspectDetect and redact PII in text or JSON. Recognizers are pure pattern/checksum matching (regex + Luhn/mod-97/SSN-area validation) — no LLM calls, no external services. Person names and dates of birth are matched only via nearby context (e.g. 'Mr. Smith', 'DOB: ...'), not general NER, to keep false positives low.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | ||
| options | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains recognition method (pattern/checksum, no LLM), context-based matching for names/DOB, and explicitly mentions low false positives. Lacks details on side effects like whether output is a copy or destructively modifies input, but overall good transparency for a PII tool.
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, front-loaded with core purpose, followed by brief technical detail. Every sentence adds value with no redundancy.
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?
Given complexity (nested options, 2 params), description covers overall purpose and recognition behavior but misses parameter specifics and output details. With no output schema or siblings, more context would be beneficial for full agent understanding.
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 has 0% description coverage, and description adds no parameter details. The 'input' parameter is implied by 'text or JSON', but 'options' (mode, locale, entities) are completely absent from description, leaving the agent to infer from enum values alone.
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?
Description explicitly states 'Detect and redact PII in text or JSON', providing a specific verb and resource. It clearly distinguishes the tool's purpose without being generic or tautological.
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?
While description implies usage for deterministic pattern-based redaction (no LLM/external services), it does not provide explicit when-to-use or when-not-to-use guidance or alternatives, especially given no sibling tools are listed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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