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, covering the safety profile. The description adds value by disclosing exact return fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying the default 'active' filter, which goes beyond 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 concise sentences: the first states the purpose and return fields, the second provides usage guidance. No unnecessary words; content is front-loaded and every sentence earns its place.
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?
Despite having no output schema, the description explicitly lists the return fields. Complexities are low (one optional parameter, no nested objects). The description covers what the tool does, what it returns, and practical use cases, making it complete for this simple tool.
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 has 100% coverage for the single parameter include_inactive, including its type and default behavior. The description does not add additional meaning about this parameter beyond what the schema already provides, so the baseline score of 3 applies.
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'), identifies the resource ('subscriptions'), and scopes to 'the caller's active subscriptions'. It distinguishes from sibling tools like subscribe and unsubscribe by noting it helps find an id to cancel, making its purpose unmistakable.
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 context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This explicitly states when to use the tool. It does not explicitly mention when not to use it or alternatives, but the context makes the intended usage obvious.
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: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share nearly identical routing, with beta explicitly described as currently identical to stable. The six Polymarket-related tools also form a dense cluster with subtle boundaries, and discover_tools/suggest_questions overlap in onboarding purpose.
Most tools follow a clear snake_case verb_noun pattern (check_password, resolve_entity, compare_entities, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are applied consistently. Minor deviations exist (ai_visibility_check, pipeworx_trending), but the overall convention is predictable.
32 tools is well past the 25+ threshold and the set feels bloated: several ask_pipeworx variants and Polymarket scanning tools could be consolidated, and unrelated utilities (check_password, scan_dependency, generate_llms_txt) are mixed into what is otherwise a data-research platform. The broad scope does not justify this many top-level entry points.
The main data-research workflow is well covered: routing, grounded verification, deep research, entity resolution/profiles, comparisons, recent changes, discovery, and feedback are all present. Memory and subscription lifecycles are also complete; minor gaps remain such as the lone password tool lacking generation or breach-checking companions, and no direct raw-fetch tool, but these are workable.