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?
Given that annotations already declare readOnlyHint=true and destructiveHint=false, the description adds meaningful context by specifying the return fields and the 'caller's active' scope. It also notes the default behavior of excluding inactive subscriptions, which aligns with the include_inactive parameter but is not redundant with the schema or 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 concise sentences, front-loaded with the main action and then providing return fields and usage guidance. Every sentence contributes value without redundancy, making it exceptionally 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 (one optional parameter) and the annotations cover safety. The description explains the return fields (compensating for lack of an output schema), the scope, and when to use the tool. It is fully sufficient for an agent to select and invoke it correctly.
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?
The input schema has 100% coverage for the single optional parameter (include_inactive), with a description explaining it. The tool description does not add extra detail about the parameter beyond mentioning 'active subscriptions' in the purpose, which indirectly aligns with the default false. Since the schema already documents the parameter adequately, a 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 opens with the specific action 'List the caller's active subscriptions', clearly identifying the resource (subscriptions) and scope (caller's). It further lists the return fields, which distinguishes it from sibling tools like subscribe and unsubscribe that perform different actions.
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 clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implicitly differentiates from subscribe/unsubscribe tools but does not explicitly name alternatives or state when not to use the tool, so it falls short of a full 5.
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.
Multiple tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk) blurs together for an agent trying to pick one. entity_profile and recent_changes also both pull company data, and ai_visibility_check vs scan_competitor_ai_presence are single-vs-multi variants of the same probe.
Naming is mostly snake_case and readable, with many verb_noun forms (validate_iban, generate_llms_txt, resolve_entity). However, there are bare verbs (remember, forget, recall, subscribe, unsubscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and inconsistent prefixes (ask_ vs polymarket_ vs suggest_) that break a clear pattern.
33 tools is borderline-heavy for a data-research API, but the bigger issue is that the server is named Openiban yet contains only two IBAN tools and 31 unrelated Pipeworx/data tools. The count feels bloated and misaligned with the server's apparent identity, though not extreme enough for a 1 or 2.
For the Pipeworx data-research domain the surface is quite rich (query, deep research, entity profiles, comparisons, subscriptions, memory). For the server's stated IBAN purpose, only validate and suggest_iban exist — no generation, parsing, batch checks, or bank detail coverage — so the tool set is severely incomplete relative to the server name.