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, and non-destructive behavior. The description adds value by specifying the exact return fields and the distinction between active and inactive subscriptions, though it omits potential details like pagination limits.
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: the first states purpose and return fields, the second provides usage guidance. Every sentence earns its place with no extraneous content.
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, the description fully covers what it does, what it returns, and when to use it. The optional parameter is documented in schema, and no output schema is needed given the listed fields.
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 for the single parameter include_inactive is 100%, and the description does not add meaning beyond what the schema already provides. Baseline score of 3 is appropriate.
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 states a specific verb ('List') and resource ('the caller's active subscriptions'), and explicitly lists the return fields, distinguishing it from sibling tools like subscribe and 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?
The description advises using this tool 'to review what you're monitoring before adding more or to find an id to cancel', providing clear context and when-to-use guidance relative to 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.
Several tools serve nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly functionally identical right now, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap as query/discovery entry points. The six prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together for agents looking to find or size a trade.
Naming is inconsistent: some tools use get_/list_/scan_ prefixes while others are bare nouns (polymarket_edges, entity_profile, recent_alerts), and the Pipeworx prefix appears only on some tools (pipeworx_feedback, pipeworx_trending) while equivalent tools are named ask_pipeworx or deep_research. Verb styles vary between imperative (validate_claim, resolve_entity) and descriptive (bet_research, polymarket_arbitrage).
34 tools is heavy for a server whose name (Meteors) covers only 3 of them. The bulk belongs to unrelated domains — Pipeworx data access, prediction markets, memory, npm scanning, llms.txt generation — making the surface feel like an unfocused grab bag rather than a deliberate product. Many of the 34 tools could be consolidated (e.g., the three near-identical ask_pipeworx variants).
The server has no coherent domain to assess completeness against: meteor data is limited to three lookups with no management/CRUD, memory tools have save/recall/delete but no update, and the Pipeworx surface lacks obvious editing or administrative operations beyond subscriptions. The prediction-market toolset is thorough, but it sits awkwardly beside unrelated utilities, leaving the overall tool surface feeling incomplete for any single stated purpose.