aircall_get_call_sentimentsGet the AI-generated sentiment analysis of a call (Aircall AI Voice feature). Aircall REST: GET /calls/{id}/sentiments.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The call id. |
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, so the read-only nature is covered. The description adds that the data is AI-generated and part of the Aircall AI Voice feature, plus the endpoint. It does not disclose potential edge cases like missing sentiment data or authentication requirements, but with annotations present, this level of additional context is acceptable.
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-loads the core purpose, and includes the endpoint without any filler. Every word adds value: the feature context ('Aircall AI Voice') and the REST reference are both useful and concise.
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 single-parameter read-only tool, the description is nearly complete. It identifies the output (sentiment analysis), the source (AI), and the endpoint. No output schema exists, but the description doesn't need to detail return values for such a focused retrieval tool. It could mention what happens if sentiment data is unavailable, but that is a minor gap.
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 parameter 'id' is already described as 'The call id.' The description does not add new parameter semantics beyond what the schema provides, but it does imply the id is used in the URL path. Baseline 3 is appropriate since the schema carries the explanatory weight.
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 a specific action ('Get') and resource ('AI-generated sentiment analysis of a call'), and explicitly ties it to the Aircall AI Voice feature. It differentiates from sibling tools like aircall_get_call_summary and aircall_get_call_transcription by naming the specific output type.
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 usage context: this tool is for retrieving sentiment analysis, not other call insights. It provides the REST endpoint for reference, but does not explicitly state when to prefer it over sibling analysis tools or exclude alternatives. Still, the purpose is clear enough to guide 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.
Each tool targets a distinct resource and action: get/list/search for calls and contacts are clearly differentiated, AI features (summary, transcription, etc.) are unique per output, and write operations (create, update, tag, comment) are unambiguous. There are no overlapping or confusable tools.
The vast majority follow a consistent `aircall_<verb>_<noun>` pattern (e.g., get_call, list_users, create_contact). The only outlier is `aircall_ping`, which is a health check rather than a verb-noun resource operation, but it's a minor deviation from an otherwise uniform scheme.
With 25 tools, the server sits at the upper bound of what feels heavy. While each tool has a specific purpose, the set covers many resources (calls, contacts, numbers, tags, teams, users) plus multiple AI features on calls, making it borderline for maintainability and agent selection.
The tool surface is strong for reading resources and for call enrichment (AI, comments, tags), but there are notable gaps: contacts have create/update but no delete, and resources like numbers, tags, teams, and users are read-only. The absence of delete and limited write operations means agents cannot fully manage the domain lifecycle.