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, and destructiveHint, covering safety and idempotency. The description adds the return fields and usage context but does not reveal additional behavioral traits beyond what annotations provide.
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, front-loading the purpose and return format, then adding usage guidance. Every sentence is informative and concise 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?
Given the tool's simplicity (no required params, optional boolean, no output schema, and thorough annotations), the description is complete enough. It could clarify what 'active' means or mention pagination, but omissions are minor.
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). The tool description does not add further semantics about the parameter, so it meets the baseline for schema coverage but does not exceed it.
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 explicitly states the tool lists the caller's active subscriptions and specifies the returned fields. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by mentioning use cases like reviewing before adding or canceling.
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 advises using the tool to review monitored subscriptions before adding more or to find an id to cancel, providing clear context for when to use it relative to siblings. It does not explicitly exclude other uses but is sufficiently directive.
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 clusters are hard to tell apart: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (beta currently behaves exactly like stable), and the six polymarket_* tools plus bet_research all overlap around edge and arbitrage discovery. The descriptions are detailed and help, but an agent must read carefully to avoid misselecting a sibling tool.
Names are consistently lowercase snake_case, but the verb/noun ordering is mixed: verb-first names (ask_pipeworx, list_categories, resolve_entity) coexist with noun-first names (polymarket_edges, entity_profile, pipeworx_feedback, recent_alerts) and bare verbs (remember, recall, forget). Readable overall, but the pattern is not systematic.
34 tools is well beyond the 25+ threshold and spans at least six loosely related domains (news, structured data research, prediction markets, AI visibility, memory, subscriptions), making the server feel like a multi-product grab bag. Meta-tools like discover_tools, suggest_questions, and pipeworx_trending add further navigation overhead.
Within each bundled domain the lifecycle is well covered: data lookup has routing, grounded mode, deep research, entity resolution, comparison, and claim validation; Polymarket has research, arbitrage, edge, fill-risk, and cross-venue spread tools; memory and subscriptions each have full CRUD-ish flows. Minor gaps like subscription updating or direct article-by-ID fetching are workarounds rather than dead ends.