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 and destructiveHint, making the safety profile clear. The description adds value by listing the exact fields returned (id, type, params, etc.) and the effect of the include_inactive parameter, without contradicting 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 functionality and return values, the second gives usage guidance. Every sentence adds value with no 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 read-only tool with one optional parameter and no output schema, the description fully covers its behavior, return format, and usage context. Annotations provide the remaining behavioral traits.
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 input schema has 100% coverage for the single parameter include_inactive, so the description need not add further semantics. The description implies its behavior (including inactive subscriptions) but does not repeat the schema. 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 'List the caller's active subscriptions,' which is a specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe, which are for adding or removing subscriptions.
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 explicitly advises using this tool to review subscriptions before adding more or to find an ID for cancellation. This provides clear context for when to use it, though it does not explicitly state when not to use it.
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 have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, ask_pipeworx_grounded is the same router with an extraction step, and discover_tools vs suggest_questions both serve as 'what can I do here' entry points. The polymarket_* family is more distinct, but the ask_pipeworx/deep_research overlap alone makes tool selection genuinely ambiguous.
The vast majority of tools follow lowercase snake_case with recognizable domain prefixes (ask_pipeworx*, polymarket_*, pipeworx_*), which is a decent pattern. However, there are bare-noun tools (events, locations, recall, forget) and mixed verb-first vs noun-first ordering (list_subscriptions vs entity_profile, scan_dependency vs polymarket_edges), so it is not perfectly uniform.
33 tools is well above the comfortable range and feels heavy even for a broad data-research platform. Many tools are narrow variations (five polymarket analysis tools, four ask_pipeworx variants, three memory tools) that could plausibly be consolidated or exposed as parameterized modes rather than separate top-level tools.
For the actual domain suggested by the tool names and descriptions—structured data research, entity profiling, claim verification, and prediction-market analysis—the surface is quite complete: lookup, grounded answers, deep research, comparisons, recent-change tracking, subscriptions, memory, and arbitrage/fill-risk analysis are all covered. However, relative to the server name 'Edmtrain', the event-discovery surface is extremely thin (only events and locations), which is a notable mismatch.