Calculate Pearson correlation coefficient.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | Comma-separated X values | |
| y | Yes | Comma-separated Y values |
| Name | Required | Description | Default |
|---|---|---|---|
| x | No | ||
| y | No | ||
| code | No | ||
| count | No | ||
| error | No | ||
| r_squared | No | ||
| correlation | No | ||
| interpretation | 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 must fully disclose behavior. It only states the calculation, omitting important details such as input constraints (e.g., equal-length numeric arrays), potential errors from non-numeric data, or output format. The existence of an output schema reduces the burden, but the description should still hint at expected behavior.
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 a single sentence with no extraneous words. It is concise but may be overly brief for a statistical tool. Still, it earns its place without fluff.
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 tool's complexity (calculating correlation from two comma-separated strings) and the presence of an output schema, the description lacks key context: input format assumptions (numeric, equal length), handling of edge cases, and relationship to sibling tools. The agent may misinterpret input requirements.
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% with descriptions 'Comma-separated X values' and 'Comma-separated Y values'. The tool description adds no further semantic context (e.g., what X and Y represent, such as variables or data sets). Baseline score of 3 is appropriate since schema does the heavy lifting.
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 'Calculate Pearson correlation coefficient', which specifies the verb (calculate), resource (Pearson correlation coefficient), and distinguishes it from siblings like calculate_covariance or calculate_mean. However, it does not elaborate on the type of correlation beyond Pearson.
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., calculate_covariance for covariance, or other statistical tools). There is no mention of prerequisites or conditions that would influence tool selection.
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.
