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=true and destructiveHint=false, so the agent knows it's a safe read. The description adds return field details, which is useful but not beyond what annotations cover. 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?
Two sentences, front-loaded with purpose, then returns, then usage. Every word earns its place with zero waste.
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 no output schema, the description provides the return fields and usage context. It covers essential information adequately, though more detail on pagination or rate limits could enhance completeness.
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 coverage is 100% and the parameter 'include_inactive' has a clear description. The tool description does not add extra meaning beyond the schema, so 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 clearly states the tool lists the caller's active subscriptions and specifies the returned fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes itself from sibling tools like subscribe/unsubscribe by hinting at its use for reviewing before adding or finding an id to cancel.
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 explicitly provides usage scenarios: reviewing subscriptions before adding more or finding an id to cancel. While it does not explicitly state when not to use, the context implies alternatives are subscribe/unsubscribe, which is sufficient.
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 form overlapping clusters that are hard for an agent to distinguish: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all cover factual/research lookups, and the five Polymarket tools also overlap heavily. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making misselection essentially guaranteed in that pair. The descriptions are detailed, but the boundaries between many tools remain unclear at the set level.
Almost all tool names are snake_case and mostly follow readable verb_noun or domain-specific patterns, such as ask_pipeworx*, resolve_entity, validate_claim, and polymarket_*. Minor deviations exist — bare verbs like remember/recall/forget/subscribe and noun-style names like spot_prices/co2_intensity — but there is no mixed casing and the overall pattern is predictable.
34 tools is already over the 25+ threshold for a well-scoped server, and the mismatch is much worse because only three tools (co2_intensity, spot_prices, query_dataset) relate to the advertised Energi Data DK domain. The other 31 tools appear to belong to an unrelated general-purpose Pipeworx platform, so the count is not appropriate for the server's stated purpose.
For the stated Energi Data DK domain, energy data access is partially covered: two dedicated tools plus query_dataset as a generic escape hatch for all ~100 datasets prevents hard dead ends, but there are no typed tools for most of those datasets and no energy-specific monitoring/alerting. If the real intended domain is the broader Pipeworx platform, coverage is much stronger, but then the server name is misleading.