unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Subscription id (uuid) returned by subscribe. |
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 provide idempotent, non-destructive, non-read-only hints. The description adds crucial context: the row is deactivated (not deleted) and historical events remain available via recent_alerts. No contradiction with annotations.
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 sentences, no unnecessary words. Every sentence provides value: action, ownership, behavioral detail.
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?
Covers purpose, usage constraint, and behavioral detail. No output schema, but the tool is simple (single parameter, no return value needed). Could mention error handling, but not necessary for core understanding.
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% (id parameter has a description). The description adds minimal extra meaning beyond mentioning the id is returned by subscribe, which is helpful but not essential.
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 ('Cancel') and identifies the resource ('subscription by id'). It clearly distinguishes from siblings like subscribe (creation) and list_subscriptions (listing).
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?
Explicitly states ownership enforcement ('only cancel your own subscriptions'), providing clear context for when to use the tool. Does not explicitly mention alternatives but the constraint is self-evident.
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.