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 declare this as read-only/idempotent, so the description doesn't need to restate safety. It adds value by disclosing the exact return fields and the scope ('caller's active subscriptions'), which is useful behavioral context 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 cover purpose, output, and usage guidance. No wasted words; the most important information is 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?
With simple schema, strong annotations, and no output schema, the description provides enough detail to understand the tool's behavior and output, including return fields. It is complete for the tool's simplicity.
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% for the single optional parameter include_inactive. The description does not add parameter-level detail, but the schema already fully explains the parameter, so 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 explicitly states the tool lists the caller's active subscriptions, using a specific verb and resource. It also lists the returned fields (id, type, params, etc.), which clearly distinguishes it 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 provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies when to use it versus subscribe/unsubscribe, though it doesn't explicitly name the alternatives.
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.
The 7 NOAA-specific tools (stations, station_metadata, water_level, currents, met_obs, predictions, datums) are clearly distinct, but they are buried among ~31 Pipeworx platform tools with heavy internal overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route factual queries, while polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, and polymarket_kalshi_spread all analyze prediction-market opportunities. An agent cannot easily tell whether the generic question-answering or prediction-market tools are the right choice without reading long descriptions.
Most tools use snake_case, but the naming conventions are inconsistent: some use descriptive nouns (stations, datums, predictions), some use noun_verb pairs (water_level, met_obs), and the Pipeworx batch mixes vendor-prefixed names (pipeworx_feedback, pipeworx_trending), bare verbs (remember, forget, recall, subscribe, unsubscribe), and multi-word verbs (generate_llms_txt, scan_competitor_ai_presence, ask_pipeworx_grounded). No predictable pattern unifies the set.
38 tools is far too many for a server named 'Noaa Tides' — only 7 tools relate to NOAA tide/current data, and the other 31 are an unrelated general-purpose data platform (SEC filings, prediction markets, npm packages, AI visibility scanning, memory storage). The overwhelming majority of the surface has nothing to do with the server's stated purpose, making the count and composition a severe mismatch.
For the nominal NOAA tides domain, the surface is reasonably complete: station listing, metadata, observed water levels, currents, meteorological observations, tide predictions, and datums cover the core workflows. Minor gaps exist (e.g., no harmonic constituents or extreme water-level statistics tool), but the essential operations are present. The unrelated tools do not fill gaps in the NOAA domain — they are clutter rather than coverage.