Calculate quartiles (Q1, Q2, Q3).
| Name | Required | Description | Default |
|---|---|---|---|
| numbers | Yes | Comma-separated numbers |
| Name | Required | Description | Default |
|---|---|---|---|
| q1 | Yes | ||
| q3 | Yes | ||
| iqr | Yes | ||
| max | Yes | ||
| min | Yes | ||
| count | Yes | ||
| numbers | Yes | ||
| q2_median | Yes |
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 provided; description does not disclose sorting behavior, handling of empty input, or edge cases. Under-specified for safe and correct use.
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 description is concise and front-loaded. However, it is extremely brief and could benefit from minimal expansion without being verbose.
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 simple input and presence of output schema, the description captures the core purpose. However, lacks behavioral context for statistical calculation (e.g., sorting, handling odd/even counts).
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?
Input schema describes parameter 'numbers' with type string and description. Schema coverage is 100%. Description adds no extra meaning beyond what schema provides, so baseline score of 3.
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?
Description clearly states verb 'calculate' and resource 'quartiles' with specific quartiles Q1, Q2, Q3. Distinguishes from sibling tools like calculate_mean, calculate_median, and calculate_percentile by naming the specific quartiles.
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 calculate_percentile or calculate_median. Does not mention prerequisites or output format.
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.
