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, openWorldHint, idempotentHint, and destructiveHint=false, so safety is clear. The description adds value by stating the caller's scope and the default behavior of returning only active subscriptions, plus the specific fields returned, which exceeds annotation coverage.
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 purpose and return fields, the second gives usage guidance. 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?
For a simple list tool with one optional parameter and no output schema, the description mentions the caller's scope, active-only default, and return fields. This is sufficiently complete to use the tool effectively, though it could optionally mention ordering or pagination.
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 describes the only parameter (include_inactive) with a clear description and default value. The tool description does not add any additional parameter meaning, so baseline 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 verb 'List' and the resource 'subscriptions', scoped to 'the caller's active subscriptions'. It also lists the return fields, making it distinct from sibling tools like subscribe and unsubscribe.
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 'to find an id to cancel'. While it doesn't explicitly name alternatives, it implies when to use this tool relative to subscribe/unsubscribe, giving clear contextual guidance.
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 have overlapping purposes, e.g., ask_pipeworx and ask_pipeworx_grounded are nearly identical, and entity_profile, compare_entities, and deep_research all perform multi-source lookups. An agent would struggle to distinguish between them.
Naming conventions are highly inconsistent, mixing snake_case (ask_pipeworx), lowercase (deep_research), and prefixed patterns (pipedrive_, polymarket_, pipeworx_). No unified verb_noun pattern exists across the set.
With 35 tools, the count is too high for a server named Pipedrive, which suggests a CRM focus. Many tools are unrelated to CRM (e.g., prediction market, weather, economic data), making the surface feel bloated and unfocused.
The Pipedrive subset lacks create/update/delete operations, leaving basic CRUD incomplete. However, the broader data lookup tools cover a wide range of domains (financials, drugs, patents), so overall coverage is moderate but not fully coherent with the server name.