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, idempotentHint=true, and destructiveHint=false, and the description adds useful behavioral context: it lists the caller's subscriptions (not all subscriptions) and specifies the return fields. This goes beyond the structured annotations without contradicting them.
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 primary action and output, the second gives concrete use cases. Every sentence earns its place with zero fluff or repetition.
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 tool's low complexity (one optional parameter), strong annotations, and explicit output field list, the description fully equips an agent to select and invoke the tool. No output schema exists, so listing the return fields is sufficient. The usage guidance completes the picture.
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 100% schema description coverage ('Include cancelled subscriptions in the response (default false)'). The tool description implicitly reinforces the notion of 'active' subscriptions but adds no new semantic detail, so the 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 uses a specific verb ('List') and resource ('the caller's active subscriptions'), clearly differentiating it from siblings like subscribe, unsubscribe, and recent_alerts. It also enumerates the exact fields returned, leaving no ambiguity about what the tool does.
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 implies when to use it relative to subscribe/unsubscribe, though it does not explicitly exclude cases or name alternatives, so it falls short of a perfect score.
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 are near-duplicates or heavily overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, several polymarket_* tools cover the same edge-detection domain, and scan_competitor_ai_presence is just a wrapper around ai_visibility_check. Agents will frequently struggle to pick the right tool.
Most tools use snake_case, but the pattern is inconsistent: verb_noun (estimate_electricity, resolve_entity), noun_phrase (entity_profile, recent_changes), brand-specific (ask_pipeworx, pipeworx_trending), and family-prefixed (polymarket_*). The naming is readable but lacks a single cohesive convention.
At 34 tools, the set is far larger than the 'Carbon Interface' name implies. It piles together carbon estimation, a massive data-router, prediction-market analysis, memory, subscriptions, and meta-tools, making the surface feel bloated and unfocused.
The carbon-estimation purpose is thin (only three estimators with no lifecycle), but the broader Pipeworx/data and prediction-market subdomains are fairly well covered. Gaps exist in each subdomain (e.g., no order placement for betting, no carbon scope beyond the three estimates), and the lack of a clear primary domain makes coverage hard to assess.