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 cover safety (readOnly, idempotent, non-destructive). Description adds default behavior (active only) and output fields, enhancing transparency 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 efficient sentences: first states purpose and output, second gives usage advice. No wasted words, front-loaded with key 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?
Given full schema coverage, rich annotations, and listed return fields, the description fully informs the agent about the tool's behavior and output without needing an output schema.
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?
Only one parameter (include_inactive) with full schema coverage. Description does not add more details beyond what the schema already provides.
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?
Clearly states it lists the caller's active subscriptions and specifies the returned fields. Differentiates from siblings like subscribe and unsubscribe by its review 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?
Explicitly advises use for reviewing monitored subscriptions before adding more or finding an ID to cancel, providing clear context for when to invoke.
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 form overlapping families: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research cover much of the same router territory, and five Polymarket tools overlap heavily on edge/arbitrage analysis. The descriptions are detailed, but an agent would regularly need to compare multiple near-equivalent candidates before choosing one.
Names are consistently snake_case and readable, but the conventions vary widely: verb_noun (search_papers, resolve_entity), noun phrases (entity_profile, polymarket_arbitrage, recent_changes), bare verbs (remember, subscribe, forget), and suffixed variants (ask_pipeworx_beta, ask_pipeworx_grounded). It is not chaotic, but there is no single predictable pattern.
Thirty-five tools is far too many for a server named Paperswithcode, especially since only four tools actually relate to papers while the rest cover data routing, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI visibility. The count feels bloated and the scope unfocused relative to the server's apparent identity.
The paper-discovery subdomain is reasonably covered with search, trending, detail, and implementation lookup, and the broader set includes discovery, memory, subscription lifecycle, and feedback tools. However, the overall surface is a patchwork of unrelated domains with no well-defined boundary, and paper datasets/models can only be counted rather than directly listed. Agents can work around most gaps, but the coverage is uneven.