get_watchlistFetch a single watchlist (full ticker set + criteria) by its name, not an id (case-insensitive). NOT_FOUND if the name is unknown to this user. Tier: sp500+ (sample rejected).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Watchlist name to fetch (case-insensitive, 1–80 chars). |
| Name | Required | Description | Default |
|---|---|---|---|
| _meta | Yes | Provenance envelope — data lineage for every MCP response | |
| watchlist | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. Description adds critical behavioral details: lookup by name (not id), case-insensitive, NOT_FOUND error, and tier restriction, going beyond what annotations provide.
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, front-loaded with the core action, no filler. Every phrase adds value: fetch, full ticker set, criteria, name vs id, case-insensitive, error condition, tier restriction.
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 single-param fetch tool with output schema present, description fully covers lookup method, error handling, and access constraints. No missing context needed for correct invocation.
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 already describes the single parameter with high coverage (100%). Description adds minimal extra meaning ('by name, not an id' and 'case-insensitive' which is also in schema). Baseline 3 appropriate as schema does heavy lifting.
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?
Clearly states verb 'Fetch', resource 'watchlist', and key detail: by name (not id), case-insensitive. Distinguishes from sibling tools like list_watchlists and delete_watchlist.
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?
Implies usage context (requires name, returns NOT_FOUND if missing, tier restriction sp500+), but lacks explicit guidance on when to use this vs. alternatives like list_watchlists or save_watchlist.
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.
Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.
All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.
With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.
The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.