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 and destructiveHint false, so the description only needs to add context. It adds the scope ('caller's' subscriptions) and lists return fields, which goes beyond basic annotation coverage and gives useful behavioral context without contradiction.
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 main action, and every sentence adds value: the first states what it does and what it returns, the second explains when to use it. No wasted words.
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 good annotations, the description covers purpose, scope, return fields, and use cases. The lack of an output schema is compensated by listing the returned fields in the description, making it complete for an agent to select and invoke 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 schema description coverage is 100% for the single parameter (include_inactive), so the baseline is 3. The description does not add any meaning beyond the schema for this parameter, but doesn't need to since the schema fully explains it.
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 it distinguishes from sibling tools like subscribe/unsubscribe by focusing on listing. It also enumerates the return fields, making the tool's purpose unambiguous.
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 use cases: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It implies alternatives (subscribe/unsubscribe) without naming them directly, so it falls just short of fully explicit when-not-to-use guidance.
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 heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying Pipeworx catalog, and the five polymarket_* tools all analyze prediction-market opportunities and edge. While the individual descriptions are detailed, an agent could easily select the wrong tool without deep reading, particularly between ask_pipeworx and its beta/variant versions.
The naming style is a mixture of imperative verb phrases (translate, validate_claim, forget, generate_llms_txt), noun phrases (entity_profile, recent_alerts, polymarket_arbitrage), and brand-prefixed nouns (ask_pipeworx, pipeworx_trending, bet_research). While all names are lowercase with underscores, there is no consistent verb_noun or domain-prefix convention across the toolset, making the API feel grab-bag rather than designed.
The server is named 'Libretranslate' — a translation service that needs only translate, detect_language, and list_languages — yet it exposes 34 tools spanning data research, prediction markets, memory storage, subscriptions, dependency scanning, AI-visibility probing, and llms.txt generation. This is an extreme scope mismatch: the overwhelming majority of tools serve completely unrelated functions that have nothing to do with the server's apparent purpose.
If judged purely as a translation server, the core surface is present but thin: translate, detect_language, and list_languages cover basic use, though there are no batch, format, or language-details options. If judged as the broader heterogeneous toolset, the domain is incoherent — no single workstream is fully covered, and the unrelated tools (Polymarket betting, Pipeworx research, memory, subscriptions) create a muddled surface with obvious gaps in any single stated purpose.