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, idempotentHint, destructiveHint false, so the safety profile is well-covered. The description adds that subscriptions are 'active' and lists return fields, but does not significantly expand beyond 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: first states purpose and return fields, second provides usage guidance. No filler, front-loaded with the most important information.
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 covers purpose, return fields, and usage context. No gaps remain.
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% description coverage for the only parameter (include_inactive), so the schema already explains it. The description adds no additional parameter details, keeping the baseline at 3.
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 states 'List the caller's active subscriptions' and lists return fields (id, type, params, etc.). It clearly identifies the verb (list), resource (subscriptions), and scope (caller's active). It distinguishes from siblings 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 when-to-use guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It does not explicitly state when not to use, but the context is clear enough.
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 purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-twins (beta currently matches the stable router exactly), and deep_research, entity_profile, recent_changes, and compare_entities all fan out across similar data sources. The long descriptions do help differentiate them, but an agent selecting quickly could easily pick the wrong variant.
Most tools use snake_case, but the verb style is inconsistent: get_api, list_providers, and validate_claim use verb_noun, while remember/forget/recall are bare verbs and polymarket_arbitrage, entity_profile, and bet_research are noun phrases. The pattern is readable but not predictable enough to infer behavior from the name alone.
At 35 tools, the surface is heavy, and many are hyper-specialized (five separate Polymarket tools, three ask_pipeworx variants, three memory tools). The breadth is defensible for a multi-domain data platform, but it goes past the comfortable 16-25 range and would benefit from consolidation.
The tool set covers the main research lifecycle well: discovery, routing, grounded answers, entity resolution, profiling, comparison, claim validation, change tracking, subscription management, and memory. Minor gaps exist—such as no explicit fetch-by-citation-URI tool and soft-failed patent coverage—but agents can generally work around them.