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, idempotentHint=true, destructiveHint=false. The description adds return field details and default behavior (active only), which is useful but not critical beyond 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 with no wasted words: first sentence states purpose and output, second sentence gives usage guidance. Front-loaded and efficient.
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?
The tool is simple with one optional parameter and no output schema, but the description explicitly lists return fields, making it fully comprehensive for an agent to use.
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% and the description does not add extra parameter meaning. The baseline of 3 is appropriate as the schema already handles parameter documentation.
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' 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?
The description explicitly says when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' It provides clear context but does not 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.
Most tools have clearly distinct purposes, with only minor overlap between similar variants (e.g., ask_pipeworx variants) and related prediction market tools (bet_research vs polymarket_edges). Overall, an agent can reliably select the right tool.
Tool names mix verb_noun (e.g., resolve_entity), noun_noun (e.g., entity_profile), and adjective_noun (e.g., recent_changes) patterns, plus a few single verbs. While readable, the lack of a uniform convention adds ambiguity.
33 tools is large but justified by the broad domain coverage. The count is borderline high but still manageable with meta-tools (ask_pipeworx, deep_research) that reduce the effective surface.
The tool set covers a wide range of data sources and tasks, with meta-tools providing deep coverage. Minor gaps exist (e.g., no dedicated Kalshi-only tools, limited attention to non-Polymarket prediction markets), but the overall surface is thorough.