stream_query_usersList/filter users in the app. e.g. filter {"role":{"$eq":"admin"}} or {"id":{"$in":["u1"]}}. GET /api/v2/users.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Sort spec, e.g. [{"field":"created_at","direction":-1}]. | |
| limit | No | Max users (<=100). Default 100. | |
| filter | No | User filter_conditions, e.g. {"role":{"$eq":"admin"}}. Default {} (all users). | |
| offset | No | Pagination offset. |
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 readOnlyHint annotation already covers the safety profile. The description adds the endpoint and filter examples, but does not disclose behavior such as pagination limits or response format. Given the annotation coverage, this is acceptable but not rich.
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 one efficient sentence plus a concrete filter example and endpoint. Every element earns its place and the core purpose is front-loaded.
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 has four parameters and no output schema, so the description could do more to explain return shape or pagination behavior. The endpoint and examples help, but explicit guidance on when to prefer this over sibling query tools 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%, so the parameters are already well-documented. The description provides a filter example that mirrors the schema's own examples but adds no new meaning for sort, limit, or offset beyond what the schema states.
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 uses a specific verb-resource pair ('List/filter users') and adds a concrete endpoint, so an agent can tell this is a user-query tool. It doesn't explicitly distinguish itself from siblings like stream_query_channels, but 'users' makes the target resource unambiguous.
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 clearly implies this tool is for retrieving/filtering users by giving examples of filter conditions and the GET endpoint. However, it does not explicitly state when not to use it or name alternative tools, leaving some inference to the agent.
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: app info, channel state, messages, reactions, replies, unread counts, and various query types. Even similar tools like get_channel vs query_channels are clearly separated by single-item vs list operations.
All tools follow the consistent pattern stream_<verb>_<resource> (e.g., stream_get_message, stream_query_channels, stream_send_message). Verb usage is coherent across the set.
With 13 tools, the set is well-scoped for a chat API server, covering core read, query, search, and write operations without excessive fragmentation.
Core chat workflows are covered: app info, channel access, messaging, reactions, threads, unread counts, and user/channel queries. Missing mutations like channel/message update or delete are notable but not severe, as the get-or-create channel behavior and send actions cover primary use cases.