Validate a credit card number using Luhn algorithm.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | Credit card number |
| Name | Required | Description | Default |
|---|---|---|---|
| length | No | ||
| number | Yes | ||
| reason | No | ||
| is_valid | Yes | ||
| card_type | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. Only the algorithm is mentioned; it does not specify the return value (likely boolean), error handling, or limitations (e.g., no check for issuer or expiry).
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?
Single sentence directly stating the tool's purpose with no unnecessary words, front-loaded for quick comprehension.
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 the existence of an output schema (not shown), the description does not need to detail return values. However, it lacks context on what the validation entails (e.g., format, length) and any limitations, leaving some ambiguity for an AI agent.
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 provides 100% coverage with 'Credit card number', and the description adds 'using Luhn algorithm', which enhances understanding of how the parameter is validated.
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 action (validate) and the resource (credit card number) using a specific algorithm (Luhn), distinguishing it from sibling tools like format_credit_card.
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?
No guidance on when to use this tool versus alternatives like validate_email or validate_pattern. No mention of prerequisites or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes, such as multiple random generators (random_integer, random_number), duplicate hashing functions (hash_md5, md5_checksum), and near-identical tools (compare, compare_2, compare_decimals). The sheer number of tools and lack of clear boundaries make it difficult for an agent to differentiate.
Naming is highly inconsistent. There are duplicate tools with different names (camel_case vs to_camel_case, slug vs slugify), arbitrary suffixes like '_2', and mixing of patterns (e.g., generate_password vs password_entropy). No clear convention is followed.
With 572 tools, the server is massively overpopulated for any coherent purpose. It includes trivial endpoints (true_endpoint, null, hello_world) and numerous duplicates, far exceeding a well-scoped utility set.
While the server covers many domains (math, strings, dates, colors, etc.), the presence of duplicate and trivial tools indicates a lack of thoughtful curation. There are gaps in basic operations (e.g., no dedicated file or network tools), and many tools are redundant.
