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, with no contradictions.
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 covers purpose and return fields, second gives usage guidance. No waste, well-structured.
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 param and full annotation safety, the description provides all necessary 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%, and description does not add meaningful detail beyond the schema for the one parameter. Baseline 3 applies.
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 'List the caller's active subscriptions' and enumerates return fields, distinguishing from siblings like subscribe/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: 'to review what you're monitoring before adding more or to find an id to cancel.'
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers, and the Polymarket cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) all target the same opportunity-finding use case. The detailed descriptions help, but an agent selecting among 6+ prediction-market tools or 4+ general-data entry points will frequently pick the wrong one.
Naming is a mix of snake_case verb_noun patterns (ask_pipeworx, discover_tools, scan_competitor_ai_presence), bare dictionary verbs (define, thesaurus), bare memory verbs (remember, forget, recall), and inconsistent version/feature suffixes (ask_pipeworx_beta, ask_pipeworx_grounded, polymarket_kalshi_spread). There is no consistent convention across the set.
A server named 'Merriam Webster' exposes 33 tools, but only 2 (define, thesaurus) are dictionary-related — the other 31 are Pipeworx data-research, prediction-market, and memory tools. This is an extreme mismatch between the server's apparent scope and its tool count; the dictionary surface could be served with 3-5 tools.
For the stated dictionary domain, the surface is thin: define and thesaurus cover word lookup, but miss audio pronunciation, example sentences, usage notes, word history, and word-of-the-day — common dictionary features. Meanwhile, the large Pipeworx cluster includes meta-tools, memory, subscriptions, and feedback, making the overall surface cluttered and leaving no single domain comprehensively covered.