Pre-aggregated stats from your Trading Journal — counts, win rate, P&L, profit factor, and emotion analytics. A calm mirror, no judgement.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing that stats are pre-aggregated (not computed on demand) and personally scoped to 'your' journal. The 'calm mirror, no judgement' phrase is stylistic but reinforces a non-reactive, read-only experience.
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 a single, front-loaded sentence listing the core content, followed by a short, memorable UX phrase. Every part earns its place: it captures what the tool does, what data is returned, and the tone of the tool.
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 zero-parameter tool with no output schema, the description adequately covers what the user gets: counts, win rate, P&L, profit factor, and emotion analytics. It could be more precise about the exact data shapes or update frequency, but for a simple stats endpoint it is reasonably complete.
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?
This tool has zero parameters, so there is no parameter semantics burden on the description. The schema description coverage is 100% vacuously, and the tool requires no input. The description appropriately focuses on output content rather than inputs.
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 identifies the tool as returning pre-aggregated stats from the user's Trading Journal and enumerates the key metrics included (counts, win rate, P&L, profit factor, emotion analytics). It is specific about the resource and content, though it does not explicitly distinguish it from siblings like get_my_briefing.
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 usage: when you need your Trading Journal stats. It does not explicitly state when to use this tool over alternatives or provide exclusion criteria, but the phrase 'Pre-aggregated stats from your Trading Journal' gives a clear context among the many sibling data tools.
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.
With 57 tools, most have distinct purposes (market data, journaling, watchlists). A few related tools (get_price_alignment vs get_crypto_price_alignment, get_market_pulse vs get_market_movers) could cause miselection, but descriptions clarify asset types and scope.
Tools predominantly use get_/list_/create_/update_/delete_ verbs with clear noun suffixes. Minor deviations like start_here, whoami, add_to_watchlist, and remove_from_watchlist break the pure verb_noun pattern, but the style is consistent and readable.
At 57 tools, the server is far beyond the well-scoped range. While the domain is broad, this many tools will overwhelm an agent's capability to select accurately, and many could be consolidated (e.g., separate financial statements).
The server covers watchlist CRUD, trading journaling, market data (fundamentals, statements, news, transcripts), analysis tools, and community features. No obvious dead ends; lifecycle coverage is thorough for the described domain.
