secret-scanner
Server Details
Scan configs, files, or text for leaked secrets and obvious misconfigurations. Nothing stored.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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.
Tool Definition Quality
Average 4.7/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion. scan_for_secrets has a clear, singular purpose of analyzing text for secrets and misconfigurations.
The single tool uses a clear verb_noun pattern (scan_for_secrets), and there are no other tools to create inconsistency.
The server has only one tool, which is slightly below the typical range, but the tool is comprehensive and handles a wide variety of inputs and detections, making the count reasonable.
The tool covers the full scope of the secret scanner domain, detecting a wide range of credentials and misconfigurations and returning findings. There are no obvious gaps for the stated purpose.
Available Tools
1 toolscan_for_secretsScan for exposed secrets & misconfigurationsARead-onlyIdempotentInspect
Scan a pasted config, file, code snippet, or blob for exposed credentials and obvious security misconfigurations. Use whenever a user shares a .env, docker-compose.yml, nginx.conf, JSON/YAML config, or any text and asks "is this safe to share/commit?", "any leaked API keys/secrets?", or "what's misconfigured?". Detects cloud credentials, Stripe/GitHub/GitLab tokens, OpenAI/Anthropic/Gemini/Hugging Face/Groq/Replicate keys, private-key blocks, JWTs, DB connection strings, plus misconfigs like debug-on, 0.0.0.0 binds, disabled TLS verification, privileged containers, and weak passwords. Deterministic. It analyzes the provided text and returns findings only — it never stores, transmits, or requires any live credential.
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | A single blob to scan. | |
| files | No | Multiple named files to scan. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true), the description adds critical behavioral context: it is deterministic, returns findings only, and 'never stores, transmits, or requires any live credential.' This gives the agent confidence that the operation is safe and non-invasive, going beyond the annotations alone.
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 dense and front-loaded with the core action. Every sentence contributes useful information: what it scans, when to use it, what it detects, and behavioral guarantees. Despite its length, there is no fluff or 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 there is no output schema, the description compensates by mentioning 'returns findings only,' covering input types, detection categories, and safety guarantees. It fully equips the agent to select and invoke the tool correctly without needing additional context about return values or side effects.
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 schema already provides full descriptions for both parameters ('text' as a single blob, 'files' as multiple named files), so baseline is 3. The description adds context about what kinds of content can be scanned (e.g., config snippets, code) but does not clarify whether text and files are mutually exclusive, which would add value. It does not significantly enhance parameter-level meaning beyond the schema.
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 clearly states the tool's function with a specific verb ('Scan') and resource ('pasted config, file, code snippet, or blob') for detecting exposed credentials and security misconfigurations. It lists concrete detection categories (cloud credentials, API tokens, JWTs, misconfigs), making the purpose unmistakable. There are no sibling tools to differentiate from, so no distinction is needed.
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?
Explicit usage guidance is provided with example user intents ('is this safe to share/commit?', 'any leaked API keys/secrets?') and specific file types ('.env, docker-compose.yml, nginx.conf, JSON/YAML config'). It clearly tells the agent when to invoke this tool, making it easy to match against user requests.
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.
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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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