Get the full column schema for a specific analytics table including column names, types, and descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | Table name to inspect, e.g. "purchases", "tickets" |
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 readOnlyHint=true and destructiveHint=false, establishing the safety profile. The description adds that the tool returns the 'full column schema' with names/types/descriptions, which is useful output context, but it does not disclose additional behavioral details such as errors or availability.
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?
One sentence, no filler, and the core action is front-loaded. Every word serves to clarify what the tool returns.
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?
The tool is low-complexity: one required parameter, fully documented schema, and annotations covering read-only/non-destructive behavior. The description also states what the response contains, so nothing an agent needs to invoke it is missing.
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 description coverage is 100% and the single 'table' parameter is already described with an example. The description adds nothing beyond the schema for parametr semantics, so 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 clearly states the action ('Get') and the resource ('full column schema for a specific analytics table'), and enumerates the returned content (column names, types, descriptions). It is unambiguous, though it does not explicitly distinguish itself from sibling tools.
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?
The description implies when to use the tool - when the agent needs the schema of a named analytics table - but it gives no explicit when-not criteria or alternatives. Among the many sibling tools, there is no guidance about how this tool relates to them.
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.
Most domain groups are distinct, but the campaign reporting cluster has three tools returning overlapping engagement metrics (get_campaign, get_campaign_metrics, campaign_report), and paid_ads_status vs paid_ads_health have fuzzy boundaries. The detailed descriptions mitigate but do not eliminate the risk of an agent calling the wrong tool.
All names use the nevent_ prefix and snake_case, and most CRUD operations follow verb_noun. However, several tools reverse the order or drop the verb entirely (segment_preview, segment_execute, campaign_report, paid_ads_status, analytics_query), making the convention mixed but still readable.
With 59 tools, this is far above the 25+ 'too many' threshold and falls into the 50+ extreme range. The broad domain coverage explains some of the size, but for an agent the set is likely to be overwhelming and harder to navigate than a more focused server.
The core marketing workflow (segments, templates, campaign creation, quote, schedule, metrics) is covered, but there is no way to update, cancel, or delete a campaign, and templates and segments lack delete operations. These are notable lifecycle gaps that agents will hit when users want to change or clean up resources.
