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 destructiveHint=false, so the safety profile is known. The description goes beyond annotations by revealing the output structure (id, type, params, created_at, last_fired_at, fire_count) and the default behavior (only active subscriptions unless include_inactive). This adds useful behavioral context about what the caller will see and how the include_inactive parameter affects the result.
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: the first states the action and return fields, the second gives usage context. Every word earns its place, and it is front-loaded with the core purpose. There is no fluff or redundancy, making it highly efficient for an agent to parse quickly.
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?
Given the low complexity (one optional parameter), strong annotations, and absence of an output schema, the description compensates well by enumerating the return fields. It also provides situational context (when to use) and notes the active-by-default behavior. This is sufficient for an agent to understand what the tool does, what it returns, and when to invoke it without needing additional information.
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 only parameter (include_inactive) has a complete schema description, so the schema already covers its meaning. The tool description adds minimal extra value by implying the default (active only) via the term 'active subscriptions,' but it doesn't elaborate on the parameter's effect. With 100% schema coverage, the baseline of 3 applies, and the description does not meaningfully exceed 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 the action: 'List the caller's active subscriptions.' It specifies the resource (subscriptions) and scope (caller's active), and lists the return fields. It distinguishes itself from sibling tools like subscribe and unsubscribe by being the read-only listing tool, and it even mentions finding an id to cancel, which ties to unsubscribe but keeps the listing purpose distinct.
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 use the tool (before adding subscriptions, when seeking a cancel id). It implies that canceling itself is done elsewhere (via unsubscribe), but it doesn't name the alternative tool explicitly, so it misses the full 'when-not' clarity.
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.
There is notable overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread). While descriptions differentiate them, agents may struggle to choose the appropriate one without careful reading.
Most tools follow a snake_case verb_noun pattern (e.g., ask_pipeworx, compare_entities), but the Repology-specific tools break this pattern with simple nouns like maintainer, problems, project, and repositories. This inconsistency makes the overall naming feel mixed.
With 36 tools, the set is too large for a server focused on Repology package queries. Many tools are from the Pipeworx platform and include redundant variants (e.g., ask_pipeworx_beta, polymarket_edge_tracker), inflating the count without adding substantial new functionality.
For a server named Repology, the tool surface is severely incomplete: it lacks fundamental Repology operations like detailed package comparisons, version history exploration, and repository-specific queries. Even as a general data platform, there are gaps such as no batch export or aggregate statistics.