ms_ads_reports_listGet the available Microsoft Ads reports as CSV: a 'Report' name column and a 'Type' column. Type 'Report' is a performance report (time-bound metrics); type 'Bulk' is an entity/configuration snapshot. The two types use different report names and column naming.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, covering safety. The description adds valuable context about the CSV format and the meaning of the 'Type' column, including that the two types use different naming conventions. This goes beyond annotations and helps the agent interpret the output correctly.
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 two sentences, front-loaded with the core purpose and output format, then explains the two types. Every sentence earns its place; no wasted words. It is concise and well-structured.
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 listing tool with no parameters and no output schema, the description covers everything an agent needs: it specifies the CSV format, the columns, and the meaning of the two types, including the note about naming differences. No critical information 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?
The tool has zero parameters, so the schema coverage is trivially 100%. With no parameters to document, the description does not need to add anything. The baseline for zero parameters is 4, and the description correctly focuses on output semantics rather than parameters.
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 states a specific verb ('Get') and resource ('available Microsoft Ads reports') and defines the output as CSV with two named columns. It further distinguishes the two report types ('Report' vs 'Bulk') with their meanings, clearly differentiating from sibling tools like ms_ads_report_columns (which lists columns) and ms_ads_execute_report_query (which executes queries).
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 gives clear context about what the tool returns and the semantics of the two types, which helps an agent decide when to use it. However, it does not explicitly mention alternatives or when not to use it. The distinction between listing reports and executing them is implied but not stated, so a small gap remains.
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 tools have clearly distinct purposes, but the audit and report tools (e.g., audit_rule_output_table vs audit_rule_output_table_historical vs audit_rule_result vs audit_rule_result_campaign) are closely related and rely on detailed descriptions to differentiate.
Naming is generally consistent with snake_case and domain-prefixed groups (google_ads_*, ms_ads_*, perfmon_*), though a few tools are bare nouns (account, task, user) rather than verb-led, which slightly deviates from the dominant pattern.
23 tools is on the higher end but justified for a platform covering multiple ad platforms, audit reports, alerts, and user management; it remains navigable with clear groupings.
The tool set comprehensively covers account enumeration, audit results, report execution for major ad platforms, perfmon alerts, pacing targets, tasks, and users. Minor gaps exist (e.g., no create/update/modify operations), but the core analytics and monitoring surface is well covered.