Notas geradas por IA de uma gravação: resumo compacto, action items e tópicos-chave.
Bulk support: accepts file_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| file_id | Yes | ||
| file_ids | 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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations by disclosing bulk/batched execution via file_ids and by specifying what the returned notes contain.
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?
Two short sentences with no filler. The main purpose is front-loaded, and the bulk behavior is separated clearly.
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 read-only tool with no output schema, the description gives enough about the return content and batch support. However, the undocumented account parameter and the lack of any note about the file_id/file_ids interaction leave a noticeable gap for an agent preparing a call.
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 0%, so the description must carry parameter meaning. It explains file_ids for batched execution and file_id is inferable as the required single-file selector, but it leaves the account parameter completely undocumented and does not clarify the relationship between file_id and file_ids.
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 resource (AI-generated notes from a recording) and the content of those notes (compact summary, action items, key topics). This clearly distinguishes it from sibling tools like plaud_get_transcript and plaud_get_recording.
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 the use case: retrieve AI notes rather than raw transcript or recording. It also adds a concrete usage note about bulk execution via file_ids, but it does not explicitly state when to prefer this tool over siblings or provide exclusions.
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.
The Plaud-specific tools are clearly distinct, but 'marketplace' is a mega-tool that appears to cover search, invoke, install, subscribe, prompts, and bug reporting — overlapping with connect, toolkit_info, and the standalone report_bug tool. An agent could easily pick the wrong entry point for connection status, installed tools, or feedback.
There is a clean plaud_get_* / plaud_list_* cluster, but the platform tools mix verbs and nouns: authenticate, connect, marketplace, report_bug, show_version, toolkit_info. The naming is readable and the Plaud prefix helps, but the overall pattern is inconsistent.
Eleven tools is a reasonable count and mostly well-scoped for a Plaud integration plus MCP platform management. It is slightly heavy because 'marketplace' bundles many sub-capabilities into one tool while several small utility tools exist separately.
The Plaud retrieval surface covers user, recordings, recording details, transcript, and AI notes, which is sufficient for the main use case of accessing Plaud data. Minor gaps exist around delete/update operations, but recordings are created externally by the hardware, so read-focused coverage is acceptable.