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). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond those flags: the levels are 'HEURISTIC,' derived from recent daily pivots with round-number levels, and the latest close is included for context. No contradiction with annotations exists.
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, no filler. The main function comes first, followed by methodology and a caveat. Every clause contributes meaning, and the most important caveat ('not investment advice') is placed at the end without padding.
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, this description covers the output type, calculation source, and heuristic nature; the schema covers parameters, and there is no output schema to explain. The only notable omission is explicit differentiation from alternative technical-analysis tools, but nothing critical to correctly invoking the tool is missing.
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%: the schema already documents symbol with an example and lookback_days with a default value. The tool description does not add any extra parameter nuance beyond what the schema provides, so the baseline 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 opens with a specific verb ('Return') and names the exact resource ('key support/resistance price levels for a US ticker'), then details the method ('recent daily pivots (swing highs/lows) plus nearby round-number levels'). It also mentions the latest close and adds a clear heuristic/research caveat, making the tool instantly distinguishable from siblings like stock_quote or candlestick_signals.
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 phrase 'HEURISTIC levels for research, not investment advice' supplies clear context about when and how to use the output, and the overall scope implies a technical-analysis use case. However, it does not explicitly name alternatives (e.g., bounce_scanner, stock_history) or state exclusions, so it stops short of full routing guidance.
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 tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.