event_dataEvent-based irregular 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, idempotentHint, and destructiveHint, but the description adds no new behavioral context. It doesn't disclose return format, limitations, or any other runtime behavior. The phrase 'irregular data' is a data property, not a behavior, so the description contributes nothing 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?
The description is extremely short, but this is under-specification rather than conciseness. A single noun phrase cannot adequately describe a functional tool with four parameters. It lacks structure and substance, so it does not earn its place.
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 4-parameter schema, no parameter descriptions, and an output schema that isn't described, this description is wholly insufficient. An agent cannot reliably invoke this tool based on the provided information.
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 does not explain any of the four parameters (start, end, sensor, station_id). The examples provide some implicit hints, but the description itself is silent, so it fails to compensate for the lack of schema documentation.
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 'Event-based irregular data' provides no verb or action, only a vague noun phrase that essentially restates the tool name. It fails to distinguish this tool from siblings like daily_data or hourly_data, making it a tautological restatement with no real purpose clarity.
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?
There is no guidance on when to use this tool versus alternatives. The description and annotations contain no mention of appropriate scenarios, exclusions, or alternative tool names. With siblings like daily_data and hourly_data, the agent has no way to choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.