list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
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, idempotentHint, and destructiveHint false. The description adds the scope ('caller'), return field list, and use case, but doesn't disclose additional behaviors like pagination or filtering semantics beyond the schema's include_inactive parameter.
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, front-loaded with the primary action and return format, followed by usage advice. No unnecessary words.
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 list tool with one optional parameter and no output schema, the description provides the return fields and usage context. Combined with annotations, it's sufficiently complete.
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 single parameter include_inactive is fully documented in the schema with a clear description and default. The tool description doesn't add additional parameter semantics, but schema coverage is 100%, so baseline 3 applies.
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 the specific verb 'List' with the resource 'the caller's active subscriptions', states the exact fields returned, and distinguishes from sibling subscribe/unsubscribe tools.
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 advises using this tool 'before adding more' (subscribe) or 'to find an id to cancel' (unsubscribe), providing clear context for when to invoke it, though it doesn't explicitly state 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.
Most tools have distinct purposes, but some overlap exists in the prediction market area (e.g., bet_research vs. polymarket_edges vs. polymarket_arbitrage) and in AI visibility checks. Descriptions help differentiate, but confusion is possible.
Names mix verb_noun (ask_pipeworx, generate_llms_txt) and noun_verb patterns (entity_profile, polymarket_edges). While some groups are consistent individually, the overall naming lacks a uniform convention.
With 32 tools covering a broad domain (data lookup, prediction markets, memory, subscriptions), the count is slightly high but still reasonable for the scope. Each tool has a specific role.
The tool set is comprehensive for the stated domain, including data retrieval, entity analysis, comparisons, research, and prediction markets. Minor gaps exist (e.g., no direct CRUD for user data besides memory), but the surface is mostly complete.