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 and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond the schema by specifying the return fields and the scoping to 'active' subscriptions by default, which clarifies the tool's behavior without contradicting the 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, front-loaded with the primary action, followed by return fields and usage guidance. Every sentence earns its place with no redundancy or filler.
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, rich annotations, and no output schema, the description is complete. It covers the tool's scope, return format, and use cases. The parameter is fully documented in the schema, so the description does not need to elaborate further.
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% for the single parameter include_inactive, which has its own description in the schema. The tool description does not add additional parameter-specific semantics beyond the schema, but it implicitly clarifies the default behavior with 'active subscriptions'. The baseline of 3 is appropriate given 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 'List the caller's active subscriptions' with a specific verb and resource, and distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing existing subscriptions. It also lists the exact fields returned, making the purpose unmistakable.
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 explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to invoke this tool relative to subscribing (before adding) and unsubscribing (to get the id), effectively covering when and why 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.
Most tools have clearly distinct purposes, but there is some overlap among the numerous Pipeworx query and prediction market tools (e.g., polymarket_arbitrage vs. polymarket_edges vs. polymarket_fill_risk). Descriptions help differentiate them, so the ambiguity is minor.
Tool names use a mix of verb_noun patterns (e.g., list_subscriptions, validate_claim), phrases (ask_pipeworx, bet_research), and standalone nouns (pipeworx_feedback). While readable, the lack of a single consistent convention makes the set feel less cohesive.
33 tools is on the high side, with many highly specialized prediction market and Pipeworx management tools. The server's name 'emojihub' suggests a narrow focus, but the actual scope is much broader, making the count feel somewhat inflated for its apparent purpose.
The tool set covers a vast domain: factual data retrieval, company profiles, comparisons, claim validation, prediction market analysis, memory, subscriptions, and emoji lookup. Minor gaps exist (e.g., no direct tool for simple web search), but overall coverage is comprehensive.