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=true and destructiveHint=false, indicating safe read. The description adds return field details and default active-only behavior, which complements 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 extraneous words; information is front-loaded.
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 thorough annotations, the description covers purpose, return fields, default behavior, and usage context. No gaps given the tool's complexity.
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%, and the schema already documents the parameter. The description adds context by clarifying default behavior ('active' subscriptions) and parameter purpose, but does not significantly exceed schema info.
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' and enumerates returned fields. It distinguishes from siblings by explicitly mentioning use case 'review what you're monitoring before adding more or to find an id to cancel', which references subscribe and 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 provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It guides when to use (before subscribing/unsubscribing) but does not explicitly state when not to use or name alternatives.
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.
Every tool has a clearly distinct purpose with detailed descriptions that differentiate even closely related tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. The FRED and Polymarket tool sets are well-organized with unique responsibilities. No two tools appear to do the same thing.
Most tools follow a consistent verb_noun or noun_verb pattern in snake_case (e.g., resolve_entity, compare_entities, list_subscriptions). However, a few tools like 'forget', 'remember', and 'recall' deviate by being single verbs, and 'pipeworx_feedback' uses a noun_verb format. Overall, the naming is predictable but has minor inconsistencies.
With 37 tools covering a broad domain (economic data, prediction markets, company profiles, subscriptions, memory, etc.), the count is reasonable and justifiable. It is slightly above the typical sweet spot but not excessive, and each tool serves a specific purpose. The scope is broad enough to warrant this many tools.
The server provides a comprehensive surface for its domain, including CRUD-like operations for data querying (ask_pipeworx, deep_research), specialized tools for prediction markets (arbitrage, edges), and utilities (memory, subscriptions). Obvious operations like entity resolution, comparison, and change tracking are present. No critical gaps are apparent for the stated purpose of querying structured data and engaging with prediction markets.