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 readOnly, idempotent, non-destructive. Description adds return fields and purpose but no extra behavioral traits like pagination limits. Adequate given 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: first states action and outputs, second gives usage guidance. No wasted 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 tool with one optional parameter, the description covers return fields and usage. Lacks mention of pagination or default filtering, but complexity is low.
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?
Input schema has 100% coverage; description does not add meaning beyond what the schema's description of 'include_inactive' already provides.
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?
Clearly states it lists subscriptions and specifies the returned fields. Differentiates from siblings like subscribe and unsubscribe by naming them in usage guidance.
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 tells when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Does not explicitly state when not to use, but context implies it's for listing active subscriptions.
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 tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research, which may confuse an agent about which to use for a given query. Additionally, the many prediction market tools (polymarket_arbitrage, polymarket_edges, etc.) have subtle distinctions that could lead to misselection.
Tool names are predominantly lowercase with underscores and follow a descriptive pattern (e.g., compare_entities, resolve_entity, scan_dependency). There are minor deviations like bet_research vs. research-related tools, but the overall pattern is consistent.
With 33 tools, the server covers many domains (data lookup, prediction markets, Montgomery County data, npm scanning, etc.), making it feel heavy. While each tool has a clear purpose, the broad scope borders on excessive for a single server.
The server provides a wide range of data access and analysis tools, but it lacks basic CRUD operations for its data sources (e.g., no way to create or update records in Montgomery County data or Pipeworx). Some domain coverage is incomplete (e.g., no tool for listing all Pipeworx tools, only discover_tools with top-N results).