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 provide readOnlyHint, destructiveHint, etc. The description adds value by detailing the returned fields (id, type, params, etc.) and the default active filter, which is not in the 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: first states purpose and returns, second provides usage guidance. Efficient and clear, though could be slightly more structured.
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 simplicity (list with one optional param, no output schema), the description covers all necessary aspects: purpose, return fields, parameters, usage context. Annotations handle safety. 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?
Schema coverage is 100% for the one parameter (include_inactive). The description mentions 'active subscriptions' implying default behavior, but does not add substantial 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 clearly states the tool lists the caller's active subscriptions, specifies the resource (subscriptions) and verb (list), and lists the return fields. It effectively distinguishes 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?
Explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Provides clear context for when to use the tool.
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.
Most tools have clearly distinct purposes with detailed routing guidance, but the three ask_pipeworx variants and overlapping company-focused tools (entity_profile vs compare_entities vs recent_changes) create some ambiguity. The descriptions are thorough enough that an agent can usually select correctly.
All names are snake_case and mostly descriptive, but they mix verb-first (ask_pipeworx, discover_tools), noun-first (entity_profile, gold_price), and domain-prefixed (polymarket_*, pipeworx_*) patterns. The style is consistent enough to be predictable, though not uniform verb_noun.
34 tools is well past the 25-tool threshold for "too many," and the server name (Nbp Pl) suggests a narrow Polish-bank scope while most tools cover unrelated domains like prediction markets, dependency scanning, and AI visibility. The breadth makes the set feel over-stuffed and unfocused.
The toolset covers a remarkably complete data-research workflow: general querying, grounded answers, deep research, entity resolution, profiles, comparisons, claim validation, change feeds, discovery, subscriptions, and memory. Minor gaps like subscription updating or a direct source catalog exist, but agents can work around them.