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 indicate read-only, idempotent, non-destructive behavior. Description adds that it returns specific fields and defaults to active subscriptions, complementing the annotations without redundancy.
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 with no unnecessary words; first sentence defines behavior and output, second sentence specifies use cases. Exceptionally concise.
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 covers purpose, output fields, and usage context adequately. No apparent gaps.
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%, so baseline is 3. Description mentions 'active' but does not elaborate on the include_inactive parameter beyond what the schema 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?
The description clearly states it lists the caller's active subscriptions and enumerates the returned fields. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on 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 advises using this tool to review monitored subscriptions before adding more or to find an id to cancel, providing clear context for when it should be used.
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.
Each tool has a clearly distinct purpose with detailed descriptions. Overlapping areas like polymarket tools are well-differentiated by function (research, arbitrage, edge tracking, fill risk, cross-venue spread). The meta-tools (discover_tools, suggest_questions) further reduce ambiguity.
All tool names use a consistent snake_case pattern (e.g., ask_pipeworx, bet_research, polymarket_edges). The naming is descriptive and predictable, aiding agent selection.
34 tools is on the high side for typical MCP servers. While many are justified by the platform's breadth, the count slightly exceeds the ideal range, potentially overwhelming agents.
The tool set covers a wide array of domains (company data, drugs, prediction markets, economics, news, etc.) with CRUD-like operations for subscriptions and memory. Minor gaps exist (e.g., no direct social media or custom API tool), but overall the surface is well-matched to the server's data platform purpose.