Analyze a SQL query and return optimization suggestions including index recommendations and query rewrites.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | SQL query to analyze and optimize |
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?
Annotations already declare the tool read-only (readOnlyHint=true), idempotent (idempotentHint=true), and non-destructive (destructiveHint=false). The description adds value by clarifying that it returns suggestions (rather than executing or modifying) and specifying the types of suggestions (index recommendations and query rewrites). There is no contradiction with annotations.
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, front-loaded with the core action, and every phrase adds value. It is concise and immediately understandable.
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?
For a simple tool with one parameter and no output schema, the description adequately explains the purpose and the nature of the return value. The annotations cover safety characteristics. This is sufficient for an agent to select and invoke the tool correctly.
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 covers 100% of the parameter ('sql') with a clear description ('SQL query to analyze and optimize'). The tool description does not add additional meaning about the parameter itself; it only restates that it analyzes the query. Since schema coverage is high, the baseline of 3 is appropriate.
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 specifies the action ('Analyze') and the resource ('a SQL query'), and states the outcome ('return optimization suggestions including index recommendations and query rewrites'). This distinguishes it from sibling tools like scalix_db_query (which executes queries) and scalix_db_schema (which describes schema).
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?
Usage is implied: the user has a SQL query and wants optimization suggestions. However, there is no explicit guidance on when to use this tool versus alternatives, nor any mention of scenarios where it should not be used. The description states what it does but not why to choose it over neighboring tools.
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.
Tools are grouped by service prefix and generally target distinct resources/actions. A few execution surfaces could be confused (sandbox_run vs computer_exec vs fn_invoke; build_create vs fn_deploy vs run_deploy), and storage_list is overloaded for both buckets and objects, but descriptions clarify the boundaries well.
The scalix_ prefix plus snake_case is used throughout, and most tools follow <service>_<verb>_<noun>. Minor deviations like scalix_search, scalix_status, and scalix_usage omit a service-domain qualifier, but the overall pattern is predictable and easy to navigate.
53 tools is a very large surface. While the server covers a broad multi-service cloud platform, the count falls well beyond the 25+ threshold and will likely feel overwhelming; many service areas could reasonably be split into separate servers or trimmed.
Several service lifecycles have obvious gaps: the KV store has get/list/set but no delete, storage has upload/download/list but no delete for objects or buckets, functions have deploy/list/invoke but no delete/update, and cron has create but no list/delete. These missing operations create dead ends for agents managing common resources.
