Format a date string.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date string (ISO format) | |
| format | No | Output format | %Y-%m-%d %H:%M:%S |
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | ||
| error | No | ||
| format | No | ||
| original | No | ||
| formatted | 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?
No annotations are provided, so the description must carry the full burden. It only says 'Format a date string' without disclosing error handling, supported format strings, or edge cases. This is insufficient for a formatting 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?
The description is extremely concise but at the cost of necessary detail. It is under-specified, omitting critical information about parameters and behavior.
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 complexity of date formatting and the presence of many sibling date tools, the description is incomplete. It does not explain format string syntax, default behavior, or error handling, leaving significant gaps 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?
Schema coverage is 100%, providing parameter names and types. The description adds no extra meaning beyond the schema – it does not explain format string syntax or the default format. Baseline 3 applies.
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 states 'Format a date string' which is a specific verb+resource, but it is vague and does not distinguish from siblings like parse_date or datetime_to_unix. The name and schema hint at functionality, but clarity is limited.
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 is provided on when to use this tool versus alternatives (e.g., parse_date, date_to_timestamp). There is no mention of prerequisites or exclusions, leaving the agent to guess.
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.
