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. The description adds value beyond these by stating that only the caller's subscriptions are listed and by enumerating the return fields (id, type, params, created_at, last_fired_at, fire_count), which gives the agent a clearer picture of the response.
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 concise sentences, with the main purpose front-loaded. Every word earns its place, and the description is easily scannable.
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?
The description is complete for this simple tool: it specifies scope, return fields, and usage context. Even without an output schema, the field enumeration compensates. No notable gaps.
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 description coverage is 100% for the sole parameter include_inactive, which is fully explained in the schema. The description does not add any extra parameter semantics, 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 'List' with the specific resource 'subscriptions' and limits scope to 'the caller's active subscriptions'. This clearly distinguishes it from sibling tools like subscribe and unsubscribe, and the phrase 'Returns id, type, params...' clarifies the tool's function.
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 context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use it, though it does not explicitly mention alternatives or exclusions.
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.
The set contains many overlapping research and prediction-market tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, polymarket_arbitrage, polymarket_edges, etc.) whose boundaries are hard to distinguish despite long descriptions. The PDL enrich tools and memory tools are clear, but an agent would frequently struggle to choose the right query or market-scanning tool.
Naming conventions are mixed: there are consistent prefixes like pdl_ and polymarket_, but also arbitrary noun phrases like entity_profile, recent_changes, and bare verbs like remember, forget, and subscribe. There is no consistent verb_noun or action_resource pattern across the toolset.
33 tools is above the recommended range and spans several unrelated domains: PDL enrichment, Pipeworx data lookup, prediction markets, memory, subscriptions, and npm dependency scanning. This feels like several servers merged together rather than a well-scoped toolset.
For a server named Peopledatalabs, the PDL surface is severely incomplete: only pdl_person_enrich and pdl_company_enrich are provided, with no PDL search, identify, or list tools. The vast majority of tools are unrelated to PDL, so an agent expecting reasonable PDL API coverage would hit dead ends immediately.