hourly_dataHourly data.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| start | Yes | ||
| sensor | Yes | ||
| station_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Raw text response from API | |
| format | No | Indicates response is plain text (non-JSON) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. However, the description adds no additional behavioral context such as pagination, filtering behavior, or response characteristics, and merely repeats the noun phrase already implied by the name.
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?
While the description is extremely short, it is under-specified rather than concise. Two words add no operational value, and the description does not earn its place as a useful specification.
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?
Despite having annotations and an output schema, the description is entirely inadequate for a data-query tool with four parameters and three required fields. It provides no context about what hourly data is returned, how to filter it, or how it relates to sibling tools like daily_data or event_data.
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 description coverage is 0%, and the description provides no explanations for the four parameters (start, end, sensor, station_id). With required parameters and no semantic detail, the agent has no basis to understand what values to supply or how they affect the query.
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 'Hourly data.' is a direct restatement of the tool name and title, providing no verb or action. It fails to specify what the tool does with hourly data (e.g., retrieve, query, list) and does not distinguish it from sibling tools like daily_data or event_data.
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 no guidance on when to use this tool versus alternatives such as daily_data or event_data. There is no context, prerequisites, or exclusions, leaving the agent to guess the tool's intended use case.
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 are near-duplicates or have fuzzy boundaries: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, the raw data tools (daily_data, hourly_data, event_data, latest, reservoirs) all read as generic 'get data' operations, and the five polymarket_* scanners overlap in opportunity-finding. The verbose descriptions help for many composite tools, but an agent can still easily select the wrong variant.
The naming is predominantly consistent lowercase snake_case with strong prefixed families (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*) and clear verb_noun actions. Minor deviations like noun-only latest/reservoirs, ask_pipeworx lacking a separator, and generate_llms_txt keep it from a 5.
37 tools is well above the heavy threshold, and the count is inflated by redundant meta-tools, three router variants, six overlapping generic data fetchers, and six prediction-market tools. Many tools are purposeful, so it is not an extreme mismatch, but the surface would be much cleaner at roughly 20-25 tools.
The set covers the core data lifecycle well: discovery (discover_tools, suggest_questions), lookup (ask_pipeworx), grounding/validation (ask_pipeworx_grounded, validate_claim, search_within), entity workflows (resolve_entity, entity_profile, recent_changes, compare_entities), plus memory and subscription CRUD. Minor gaps like no explicit fetch-by-citation tool and a limited subscription type set prevent a 5.