Return key support/resistance price levels for a US ticker from recent daily pivots (swing highs/lows) plus nearby round-number levels, with the latest close for context. HEURISTIC levels for research, not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | US ticker (e.g. 'TSLA'). | |
| lookback_days | No | Trailing daily bars to derive levels from (default 180). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent, non-destructive nature. The description adds meaningful context beyond annotations: the levels are heuristic, derived from daily swing highs/lows, complemented by round numbers, and explicitly not investment advice. This sets accurate expectations about reliability and methodology.
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 wasted wording. The first sentence front-loads the output and methodology, and the second delivers the heuristic/research disclaimer. Every clause earns 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?
For a simple two-parameter read-only tool with no output schema, the description adequately conveys what is returned: support/resistance levels, round-number levels, and latest close. It does not specify the exact response structure, but the simple output nature makes that a minor gap.
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 100%: both 'symbol' and 'lookback_days' are already documented in the input schema. The tool description adds little parameter-level meaning beyond restating the US-ticker scope and the daily-pivot derivation, so the baseline score of 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 clearly specifies the verb 'Return' and the resource 'key support/resistance price levels', and it explains the methodology ('recent daily pivots') and the inclusion of round-number levels and latest close. It is readily distinguishable from raw price tools like stock_quote or stock_history, but it does not explicitly differentiate itself from similar analytic siblings by name.
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 implies its use case by naming support/resistance levels and says the output is 'HEURISTIC levels for research, not investment advice,' which gives context about appropriate interpretation. However, it does not explicitly state when to prefer this tool over alternatives or which sibling to use for related but different data.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.