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 read-only and idempotent behavior. The description adds useful context about the default active-only filter and the exact fields returned, enhancing transparency beyond annotations without contradiction.
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 concise sentences. The first states purpose and return fields, the second gives usage guidance. Every word earns its place with no redundancy.
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?
With no output schema, the description's enumeration of return fields is essential and provided. The single optional parameter is fully covered by the schema, and the simple nature of the tool is adequately addressed.
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 description coverage is 100%, with include_inactive fully described in the schema. The tool description does not add any parameter-level details beyond the schema, 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 clearly states 'List the caller's active subscriptions' with a specific verb and resource, and it enumerates the returned fields. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on read-only listing.
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 use cases: 'review what you're monitoring before adding more' and 'find an id to cancel.' It does not explicitly name alternatives or state when not to use, but the context is clear given the sibling tools.
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, ask_pipeworx_grounded, and deep_research all serve as broad data-query routers. The Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) cover closely related trading/arbitrage functions with unclear boundaries. Even the Medicaid drug tools (medicaid_drug_state_market, medicaid_drug_trend, medicaid_drug_utilization) differ only subtly. Agents will struggle to choose correctly.
Most tools use snake_case and descriptive phrases, but the patterns are inconsistent: some are verb_noun (generate_llms_txt, list_subscriptions), some are noun_heavy (medicaid_drug_state_market, entity_profile), and some are single verbs (forget, recall, remember). Versioned names like ask_pipeworx_beta and ask_pipeworx_grounded add to the mix. No dominant convention emerges.
39 tools is far too many for a coherent set, especially for a server named 'Medicaid Intelligence.' A large portion of the tools (Polymarket arbitrage, npm dependency scanning, AI visibility checks, pipeworx meta-tools) are unrelated to the server's apparent purpose. The count feels bloated and unfocused.
For the Medicaid domain, the coverage is reasonable: drug utilization, enrollment, managed care, and plan market data are present. However, the server also tries to cover general data lookup, prediction markets, and entity research, making the overall surface feel scattered. Missing obvious Medicaid operations (e.g., provider data, claims, spending by state) suggest notable gaps if the stated purpose is Medicaid intelligence.