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, idempotentHint=true, destructiveHint=false. The description adds context by naming the specific return fields and the default behavior of listing only active subscriptions, 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 sentences with no fluff; all information is relevant and front-loaded.
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 single optional parameter, comprehensive annotations, and description listing return fields in lieu of an output schema, the description is fully complete for this simple read-only tool.
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 coverage is 100% with a description for include_inactive. The description adds meaning by rephrasing 'include cancelled subscriptions' and notes the default false, complementing the schema.
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 the tool lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It effectively distinguishes from sibling tools 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 explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use context and implicitly when not to use.
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.
Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicates, while deep_research, entity_profile, compare_entities, and bet_research all route into the same underlying Pipeworx catalog. ai_visibility_check and scan_competitor_ai_presence also overlap heavily. Descriptions are detailed, but an agent could easily misselect among the research and market-analysis clusters.
All names use lowercase snake_case, but the pattern is mixed: verb-first names (search_articles, resolve_entity, validate_claim), noun-phrase domain tools (entity_profile, polymarket_fill_risk), brand-prefixed names (pipeworx_trending, ask_pipeworx), and bare verbs (recall, remember, forget). It is readable and consistent in style, but there is no predictable verb_noun convention and tool names do not reliably indicate their domain.
With 35 tools, this set is well beyond the 3-15 well-scoped range and even the 16-25 heavy range. Several clusters could be consolidated (three ask_pipeworx variants, five polymarket analysis tools, three memory tools), and unrelated utilities like generate_llms_txt and scan_dependency add to the sprawl.
For a data-research server, coverage is broad: universal lookup, grounded answers, deep research, entity profiles, comparisons, news search/sentiment/timelines, prediction-market analysis, and memory/subscription lifecycle tools are all present. The main gaps are direct raw-document fetching (e.g., full article text or a specific SEC filing body), but the universal ask_pipeworx router and pipeworx:// resource URIs let agents work around those.