user_statusOnline / playing status for the given usernames (up to 100).
| Name | Required | Description | Default |
|---|---|---|---|
| usernames | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "usernames": [
+ "hikaru",
+ "magnuschess"
+ ]
+ },
+ {
+ "usernames": [
+ "penguingm",
+ "botvinnik",
+ "DrDrunkenstein"
+ ]
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "items": {
+ "properties": {
+ "id": {
+ "description": "User ID",
+ "type": "string"
+ },
+ "name": {
+ "description": "Username",
+ "type": "string"
+ },
+ "online": {
+ "description": "Online status",
+ "type": "boolean"
+ },
+ "playing": {
+ "description": "Currently playing",
+ "type": "boolean"
+ },
+ "streaming": {
+ "description": "Currently streaming",
+ "type": "boolean"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds specific behavioral context: it returns online/playing status for usernames. It does not mention error handling or rate limits, but the annotations reduce the burden.
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 that conveys the purpose efficiently, with no wasted words. It is front-loaded and well-structured.
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 an output schema (mentioned in context signals), so return values are covered. The description adequately covers what the tool does, though it could briefly mention the output format (e.g., a map of username to status). Complete enough for the task.
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 one parameter 'usernames' with 0% description coverage. The description adds meaning by stating 'for the given usernames' and 'up to 100', providing limits and usage context beyond the schema.
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 verb (status retrieval), resource (usernames), and scope (up to 100). It distinguishes from siblings like 'user_performance' or 'users' by specifying 'online/playing status'.
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 explicitly tells when to use the tool (to get online/playing status for a list of usernames) and implies a limit (up to 100). It does not explicitly state when not to use it or mention alternatives, but the context is clear.
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 server mixes chess tools with numerous data query tools from Pipeworx, causing significant overlap. Multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research have similar purposes, making it difficult for an agent to choose correctly. Chess tools are distinct but compete with many unrelated tools.
Tool names follow no consistent pattern: chess tools use mostly underscores (top_players, opening_explorer), Pipeworx tools use mixed styles (ask_pipeworx, deep_research, entity_profile), and memory/subscription tools use simple verbs (remember, subscribe). The naming is inconsistent across the set.
With 41 tools, the count is high and unfocused. A chess server would typically have 10-15 tools; the remaining 31 tools from Pipeworx are unrelated and overwhelm the set. The server tries to cover too many domains, making it bloated for its primary purpose.
The chess-specific tools (10) cover basic queries but lack deeper chess analysis (e.g., puzzles, board evaluation). The extensive Pipeworx tools are out of scope for a Lichess server, resulting in an incomplete surface for the expected domain and an excessive surface for unrelated data lookups.