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 safety profile is covered. The description adds useful behavioral context by specifying the return fields and the default behavior (active-only, with include_inactive to include cancelled). 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?
The description is two sentences, front-loaded with the verb and resource. Every word earns its place, with no redundancy or fluff.
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?
There is no output schema, but the description lists all returned fields (id, type, params, created_at, last_fired_at, fire_count), which is essential for understanding the response. For a simple list tool with one optional parameter, it is sufficiently complete.
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 fully documents the single parameter (include_inactive) with a clear description, achieving 100% coverage. The tool description merely echoes the default active-only behavior, adding no significant meaning beyond the schema.
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 clearly identifies the resource ('the caller's active subscriptions'). It distinguishes from sibling tools like subscribe/unsubscribe by focusing on reading subscriptions rather than modifying them.
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: 'review what you're monitoring before adding more' and 'find an id to cancel.' This gives clear guidance on when to use, though it doesn't explicitly name alternative tools like subscribe or unsubscribe.
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.
Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve question-answering/discovery; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets; ai_visibility_check and scan_competitor_ai_presence overlap heavily. Only the three PeeringDB search tools are clearly distinct.
Naming mixes verb-led snake_case (search_networks, validate_claim, subscribe) with noun-phrase tools (entity_profile, bet_research, recent_alerts) and inconsistent prefixes (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded; polymarket_arbitrage vs polymarket_fill_risk vs polymarket_kalshi_spread). No consistent verb_noun pattern is present.
34 tools is heavy for a coherent surface, especially since the server is named 'Peeringdb' but only 3 of 34 tools relate to PeeringDB. The Pipeworx/Prediction-market/memory/AI-visibility tools form several distinct sub-domains that would be better split into separate servers or consolidated.
For PeeringDB, only search_exchanges/facilities/networks exist—no get-by-id, no facility/network details beyond search results, and no read/update operations. For the broader Pipeworx domain, coverage is fragmented: many meta-tools overlap while some obvious operations (e.g., updating a saved memory, deeper entity relationships) are missing. The domain is poorly scoped, making completeness hard to assess.