Get Workspace Usage. Returns credit usage broken down by product type over time. The response is a tabular structure with columns, column_types, column_units, and rows.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| filters | No | ||
| end_time | Yes | ||
| group_by | No | ||
| time_zone | No | ||
| start_time | Yes | ||
| interval_seconds | No |
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?
The description discloses the response structure (columns, column_types, etc.), which is useful. However, it doesn't mention any behavioral aspects like pagination, date range limits, authentication requirements, or rate limits. Annotations provide destructiveHint false, but the description adds only response format details. It doesn't go beyond what the schema implies, and there's no mention of potential side effects or constraints.
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, very concise and front-loaded with the core purpose. However, it's almost too minimal—it doesn't structure information beyond a single sentence alert. It is clear but could be expanded slightly for more guidance without losing conciseness. It's not padded, so it gets a moderate score.
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?
Given the tool's complexity (multiple parameters, filter objects, group_by options), the description is quite minimal. It doesn't mention default behaviors, time ranges, how to interpret results, or any examples. The presence of an output structure hint is good, but the description doesn't cover important context like what 'product' means, how to set time ranges, or what the grouping options are. The tool has 7 parameters but the description only mentions the response structure, leaving much to the agent's inference.
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 several parameters (account, filters, group_by, start_time, end_time, time_zone, interval_seconds) with zero descriptions in the schema. The tool description does not explain any of these parameters or their expected formats. For instance, start_time is a number but what format? Unix timestamp? Filters schema is complex but unexplained. The description adds no meaning beyond what parameter names imply, which is insufficient given the schema's complexity and low coverage.
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 tool retrieves workspace usage with credit usage broken down by product type over timechery. It specifies the resource (workspace usage) and the action (get/returns). It differentiates from siblings by focusing on usage breakdown by product type, which seems distinct from other tools like get_workspace_audit_logs or get_workspace_members. However, it doesn't explicitly contrast with siblings, so it's not a 5.
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 provides no guidance on when to use this tool versus others, nor any context about typical use cases or limitations. It says 'Get Workspace Usage' but doesn't discuss when this is preferred over other usage tools or what scenarios it's meant for. No exclusions or alternative references.
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.
There are many tools with overlapping purposes, such as multiple voice retrieval tools (get_voice_by_id, get_voices, get_user_voices_v2, get_library_voices) and several dubbing transcript segment editors with only subtle naming differences. The inclusion of platform-level tools (authenticate, connect, marketplace) alongside ElevenLabs API tools further blurs boundaries.
Naming is highly inconsistent. Most tools have the 'elevenlabs_' prefix, but some do not (authenticate, connect, marketplace, report_bug, show_version, toolkit_info). Several tools have truncated/random suffix names (e.g., elevenlabs_dubbing_target_transcript_segmen_b565e6, elevenlabs_get_pronunciation_dictionary_ver_45baf2), and one tool is in Portuguese (elevenlabs_list_accounts). This mixture of conventions and languages makes the pattern unpredictable.
With 155 tools, the server is extremely bloated. It mixes a comprehensive ElevenLabs API surface with unrelated MCP platform tools (marketplace, authenticate, report_bug, etc.) that belong in a separate toolkit. This is a severe mismatch between the apparent purpose (ElevenLabs audio services) and the sheer number of tools.
The ElevenLabs-specific tools cover a wide range of operations (text-to-speech, voice management, dubbing, pronunciation dictionaries, Studio projects, workspace administration, order management), making it fairly complete for those domains. However, the inclusion of unrelated platform tools and the lack of a clear focus mean that an agent would have difficulty navigating this large surface, and some operations like music finetuning or speech engines appear only partially covered.