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Glama

URL Encoder

url-encoder

URL encode/decode with parameter parsing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoEncode or decodeencode
textYesText to encode or decode
encodeModeNoEncoding mode (component recommended)component

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It states the core operation and mentions parameter parsing, but it does not explain the output format, side effects (or lack thereof), or how encodeMode affects behavior. For example, it is unclear whether decoding a query string returns raw text or parsed parameters, which is a significant gap for a tool with no annotations.

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 a single, concise sentence that immediately conveys the tool's purpose and unique feature. Every word earns its place, and it is well front-loaded. There is no unnecessary repetition of the tool name or schema details.

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

Completeness2/5

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

The tool is simple but the description is too sparse to be fully complete. It does not explain the expected output format (e.g., whether parameter parsing returns a structured object), nor does it clarify the differences between encodeMode values. Since there is no output schema, the description should compensate, but it only states the basic function. This leaves significant ambiguity for an agent deciding how to invoke the tool and interpret results.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description's mention of 'parameter parsing' adds some context about what the tool does with input text, but it does not provide specific parameter-level semantics beyond what the schema already documents. No additional parameter details are given.

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 explicitly states 'URL encode/decode' with a specific verb and resource, and adds 'parameter parsing' as a distinguishing feature. This clearly separates it from generic converters like base64-converter or string-transform among the sibling tools.

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

Usage Guidelines4/5

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

The description clearly implies the tool is for URL encoding/decoding, which provides clear context for when to use it. However, it does not explicitly state when not to use it or name alternatives (e.g., base64-converter for base64 or string-transform for other transformations), so it lacks explicit exclusions.

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

B3.1/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.

Naming Consistency4/5

Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.

Tool Count1/5

202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.

Completeness4/5

The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.

Resources