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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context by specifying the return fields and the scope ('caller's active subscriptions'), which helps the agent understand what to expect. No contradiction with 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?
The description is two sentences with no fluff. The first sentence states the action and return fields, the second provides practical usage context. Every word earns its place.
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 read-only list tool with one optional parameter, the description covers purpose, return fields, and usage. It lacks notes on pagination or ordering, but these are not critical given the tool's simplicity. The annotations and schema fill in the remaining 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 description coverage is 100%, so the single parameter include_inactive is already fully documented. The description adds no extra parameter semantics beyond implying the default is active subscriptions, which is already in the schema. This meets the baseline for high schema coverage.
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 the tool lists the caller's active subscriptions, naming a specific verb and resource. It also enumerates the return fields, which makes its functionality explicit. This differentiates it from sibling tools like subscribe/unsubscribe by positioning it as the read-only listing counterpart.
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 gives concrete usage guidance: use before adding more subscriptions (i.e., subscribe) and to find an id for cancellation (i.e., unsubscribe). This implies the appropriate context and alternatives, though it does not explicitly name the sibling tools or state when not to use it.
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 have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently described as identical), ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six Polymarket tools heavily overlap in finding and evaluating trading edges. Individual descriptions are detailed, but an agent could easily route to the wrong variant.
The set mixes conventions: Pipeworx tools mostly use verb_noun (ask_pipeworx, compare_entities, resolve_entity), but memory tools are bare verbs (remember, recall, forget), and the Ethereum tools are inconsistent (nft_metadata vs nfts_owned vs nft_owners, token_balances vs token_allowance). The lack of a uniform pattern makes the surface harder to predict.
At 40 tools, the server is oversized for the apparent core purpose of an Alchemy Ethereum interface. There is also significant redundancy: multiple ask/deep-research entry points and a dense suite of Polymarket analysis tools add bulk that could be consolidated.
The Ethereum side is mostly read-only convenience wrappers (NFTs, tokens, asset transfers) plus a generic eth_call catch-all, but lacks dedicated transaction sending, block/transaction detail, logs, or ENS conveniences. The rest of the tool surface is a sprawling collection of unrelated data-research, memory, and subscription features, making the overall implied domain incoherent and likely to leave obvious gaps for users expecting a focused Ethereum server.