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, destructiveHint. Description adds return field details and optional parameter behavior, complementing annotations without contradiction.
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 concise sentences: first states purpose and return fields, second gives usage guidance. 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?
Given the simple one-parameter tool with rich annotations, the description fully covers purpose, return structure, and usage context.
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 coverage is 100%; description does not add additional meaning to the include_inactive parameter beyond what schema provides. Baseline score of 3.
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 'List the caller's active subscriptions' with a specific verb and resource, and distinguishes from siblings like subscribe and unsubscribe.
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 when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Does not explicitly mention alternatives but provides clear context.
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.
Several tool groups have heavy functional overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical to stable right now), ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all route around the same data-querying core. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.
Most tools follow a clear lowercase snake_case convention with family prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. Minor deviations exist — bare verbs like remember/recall/forget and the quirky generate_llms_txt — but the overall pattern is predictable and readable.
33 tools is already on the heavy side, but the real problem is scope: the server is named 'Emoji' yet only 2 of 33 tools relate to emoji, with the other 31 forming a sprawling data-research/prediction-market platform. The count feels mismatched with the server's apparent identity.
The data-research side is impressively broad, but the stated domain (Emoji) is barely covered — only lookup and keyword search with no listing, metadata, or classification features. The toolset is a grab bag of unrelated domains, so the overall surface is not complete for any single coherent purpose.