Calculate variance.
| Name | Required | Description | Default |
|---|---|---|---|
| numbers | Yes | Comma-separated numbers | |
| population | No | Use population variance (N) vs sample variance (N-1) |
| Name | Required | Description | Default |
|---|---|---|---|
| mean | Yes | ||
| type | Yes | ||
| count | Yes | ||
| numbers | Yes | ||
| variance | 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It does not mention that the tool supports both population and sample variance via the 'population' parameter, nor does it describe error handling for empty inputs or non-numeric data. The description adds no behavioral context beyond the tool's basic function.
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 (two words) but lacks substance. While brevity is valued, the sentence does not earn its place because it repeats the tool name without adding informative value. It is not verbose, but it is under-specified.
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?
Despite the presence of an output schema (indicated by context) and full parameter descriptions in the schema, the description omits any mention of return format, edge cases, or practical usage. For a simple mathematical tool this is minimally acceptable, but given the number of sibling tools, additional context would improve completeness.
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 input schema has 100% description coverage, so the schema already documents both parameters ('numbers' as comma-separated values, 'population' as boolean with default false). The description adds no additional semantic meaning for the parameters. Per guidelines, baseline is 3 when schema coverage is high.
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 'Calculate variance' clearly states the tool's purpose (verb 'calculate' + resource 'variance'). However, it does not differentiate this tool from sibling statistical tools like 'calculate_mean' or 'calculate_stddev', which also follow the same pattern. The purpose is clear but lacks specific scope or distinction.
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 such as 'calculate_stddev' or 'calculate_covariance'. The description provides no context about prerequisites or typical use cases, leaving the agent to infer from the tool name alone.
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.
