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Glama

secrets_scan

Scan text for hardcoded secrets (API keys, tokens, private keys). Returns redacted findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
min_entropyNoEntropy threshold for heuristic strings

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It states that the tool returns 'redacted findings', implying it modifies the text to hide secrets, but does not clarify if the input is modified or if it is read-only. It also lacks details on performance, rate limits, or permissions. This is adequate but not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, front-loaded with the core action and output. Every word adds value, with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple scanning tool with no output schema, the description covers the basic function and return value. However, it could mention that results are likely a list or structured output, and how redaction is performed. Overall, it is mostly complete for the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50%; only min_entropy has a description. The description does not explain the 'text' parameter format or constraints, nor does it add value to min_entropy beyond what schema provides. The description should compensate for the missing param description but fails to do so.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scans text for hardcoded secrets like API keys, tokens, and private keys, and returns redacted findings. It provides specific verb+resource and lists examples, distinguishing it from sibling tools which are mostly encoding/decoding or general utilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives or when not to use it. There is no mention of prerequisites, edge cases, or comparisons to similar tools like html_security_scan or password_strength.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3/5.0
Disambiguation5/5

Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.

Naming Consistency5/5

All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.

Tool Count1/5

193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.

Completeness4/5

Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.