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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. Description adds return fields and caller scoping, which is useful but not essential 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 filler: purpose, return values, and usage guidance included concisely.
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 simple tool with optional parameter and no output schema, the description covers all necessary behavioral and usage aspects.
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 covers the only parameter completely (100%). Description does not add extra meaning beyond implying 'active' default, so baseline score 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?
Description clearly states it lists the caller's active subscriptions and specifies the returned fields. It distinguishes from sibling tools like subscribe/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?
Explicitly advises using this tool to review subscriptions before adding more or to find an ID to cancel, providing clear context.
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.
Most tools have distinct purposes, but several clusters overlap: ask_pipeworx vs ask_pipeworx_beta are currently functionally identical, polymarket_arbitrage / polymarket_edges / polymarket_kalshi_spread all hunt mispricings via different mechanisms, ai_visibility_check is wrapped by scan_competitor_ai_presence, and discover_tools vs suggest_questions both serve tool discovery. The rich descriptions mitigate but do not eliminate misselection risk.
Names are all snake_case and follow recognizable conventions: verb_noun for actions (compare_entities, resolve_entity, validate_claim), domain-prefixed families (polymarket_*, pipeworx_*, recent_*, ask_pipeworx_*), and a few bare verbs (remember, recall, query). Minor deviations like bet_research (noun_verb) and noun-only names (datasets, metadata) break the pattern, but the overall scheme is predictable.
At 34 tools, this exceeds the 25+ threshold for 'too many' and bundles several distinct domains — general data querying, prediction markets, AI visibility, memory, subscriptions, open data, and npm auditing — into one server. The breadth is defensible for a data platform, but the agent-facing surface is sprawling and would benefit from splitting into focused servers.
The core data workflow is well covered: discover (discover_tools, suggest_questions), resolve (resolve_entity), query (ask_pipeworx), ground (ask_pipeworx_grounded, validate_claim), research (deep_research), compare (compare_entities), profile (entity_profile), and changes (recent_changes). Prediction markets, memory, and subscriptions each have full lifecycles. The main gap is no tool for fetching returned pipeworx:// citation URIs directly, plus a few soft-failing sources.