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 readOnly, idempotent, and non-destructive behavior, so the description need not repeat these. It adds value by specifying the exact return fields (id, type, params, created_at, last_fired_at, fire_count) and the scope ('caller's active'). This goes beyond annotations while not contradicting them.
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 core action, then return details, then usage guidance. Every sentence earns its place with zero redundancy. It is concise without sacrificing clarity.
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?
This is a simple, read-only tool with one optional parameter and strong annotations. The description covers the return format, scope, and practical use cases. It doesn't mention pagination or ordering, but for a tool like this, the provided context is nearly complete. The absence of an output schema is compensated by listing the return fields.
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 already describes the only parameter (include_inactive) at 100% coverage with a clear description. The tool description adds no new parameter-level semantics beyond the schema, but it does add context for when the parameter would matter (e.g., finding an id to cancel might need inactive subscriptions). This is baseline 3 as per rubric.
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 starts with a specific verb and resource: 'List the caller's active subscriptions.' It clearly defines the scope (caller's active) and distinguishes itself from sibling tools like subscribe and unsubscribe by being the listing counterpart. It also enumerates returned fields, making the 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 gives explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel,' which indirectly reference subscribe and unsubscribe. It does not explicitly name alternative tools, but the context strongly implies when to use this tool versus adding or removing subscriptions.
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.
Several tools deliberately overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the dog-photo trio plus a cluster of six prediction-market tools creates real selection ambiguity. Although descriptions are detailed, an agent could easily call the wrong variant.
All names are consistently snake_case and readable, with useful domain prefixes like polymarket_ and ask_pipeworx_. However, conventions are mixed: compare_entities is verb-first, entity_profile is noun-first, bet_research is object-verb, and random_image is adjective-noun.
35 tools is too many for a server whose name suggests a simple dog-photo service, and most tools are unrelated to that identity. The scatter across dog images, deep data research, prediction markets, npm scanning, memory, and llms.txt generation makes the set feel bloated rather than comprehensive.
The dog-image functionality is complete, and the subscription and memory lifecycles have paired operations. However, the server's true domain is incoherent, so completeness is difficult to assess; there are no major dead-ends within each cluster, but the unrelated utility tools create large topical gaps relative to the apparent dogceo identity.