stream_get_unread_countsFetch total and per-channel unread message/mention counts for one user. GET /api/v2/chat/unread.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | User id to fetch unread counts for. |
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 communicates that this is a non-destructive read, and the description's 'Fetch' and GET endpoint reinforce that without contradicting it. The description adds the endpoint and scope but does not disclose other behavioral context such as authentication requirements, rate limits, or how results are organized beyond the counts already named.
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 short sentences with no filler. The core function is front-loaded, and the second sentence adds the exact HTTP endpoint, so every part earns its place.
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 one-parameter, read-only tool without an output schema, the description supplies enough to call it correctly: what counts are returned (total and per-channel unread message/mention counts), the user scope, and the API path. No critical context appears missing for an agent selecting or invoking the tool.
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?
With 100% schema description coverage, the schema already documents user_id ('User id to fetch unread counts for'). The description's 'for one user' merely restates this parameter's meaning and adds no format, interpretation, or prerequisite detail.
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 names a specific verb ('Fetch') and a concrete resource ('total and per-channel unread message/mention counts for one user'), which clearly distinguishes this from siblings that get apps, channels, messages, reactions, or replies. It also supplies the API endpoint, so an agent knows exactly what resource is being read.
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?
Usage is only implied: the description says the tool fetches unread counts, so an agent can infer it is for that need. However, it does not state when to prefer it over sibling tools or mention any exclusions or alternatives, leaving the decision to inference.
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.