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 and destructiveHint=false, and the description adds the return fields (id, type, params, created_at, last_fired_at, fire_count) along with the fact that it lists active subscriptions. This goes beyond the annotations by giving a clear picture of the output without 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, with the main action first, followed by the output fields and usage guidance. Every sentence adds value, and the structure is highly scannable.
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 returned fields, compensating for that gap. Combined with the one optional parameter and strong annotations, this description fully covers the tool's context and behavior.
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%, and the single parameter include_inactive has a clear description in the schema. The tool description does not add additional parameter semantics beyond the schema, so the 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 uses a specific verb+resource ('List the caller's active subscriptions') and clearly distinguishes itself from sibling tools by stating it lists existing subscriptions rather than creating or canceling them. It also explicitly mentions the use case for canceling, reinforcing its purpose.
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 usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use this tool (before subscribing more or when needing an ID to unsubscribe) without needing to infer from sibling names.
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 tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions against the same data sources, and the Polymarket suite (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) has unclear boundaries. An agent would struggle to pick the right one.
Names mix verb phrases (search_pubmed, get_abstract, validate_claim) with noun phrases (entity_profile, polymarket_arbitrage, bet_research) and bare verbs (remember, forget). The ask_pipeworx family uses a non-standard prefix, and there's no consistent verb_noun pattern across the set.
37 tools is far too many for a server that presents as a PubMed tool. The majority are unrelated to biomedical literature (memory, subscriptions, prediction markets, real estate, etc.), making the surface feel bloated and unfocused.
The PubMed-specific tools form a complete lifecycle: search, citation metadata, abstract, full text, forward citations, and related articles. However, the server's broader domain is unclear and unevenly covered — many non-PubMed areas have partial coverage.