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ai_trading_journel_mcp

find_trades

Search and filter trading journal entries by symbol, setup, date range, status, outcome, and emotion, then return a compact list with the total count.

Instructions

Search trades. Returns a compact list plus the total count.

Input Schema

NameRequiredDescriptionDefault
sortNotime, net_pnl, r_multiple, volume, holding; prefix - for descending-time
limitNo
setupNo
periodNoRolling window ending today. Ignored when date_from/date_to are given.
sourceNo
statusNoall
symbolNoInstrument, e.g. EURUSD or XAUUSD (broker suffixes are ignored)
date_toNoEnd date (YYYY-MM-DD), inclusive
emotionNo
mistakeNo
outcomeNo
date_fromNoStart date (YYYY-MM-DD), inclusive
directionNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the safety profile, and 'Search' implies a read-only operation, which is reasonable. It also discloses the return shape (compact list plus total count), but does not explain whether limit truncates the list while total count reflects all matches, or how pagination beyond the first page works.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with zero filler, front-loaded with the core action. The return-shape sentence is mildly redundant given an output schema exists, but it costs little.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values needn't be detailed, but for a 13-parameter search tool with no annotations and thin schema descriptions, the definition is under-specified. Critical semantics such as list-vs-total behavior and filter interactions are left entirely unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 38% across 13 parameters, so the description should compensate, yet it mentions no parameters at all. It adds no meaning for the unenumerated filters (setup, emotion, mistake, source) or for how status/period interact with date_from/date_to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Search trades') that an agent can act on immediately. It does not distinguish itself from siblings like get_trade or get_performance, so the agent must infer that this returns a filtered list rather than a single record or an aggregate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no exclusions, and no routing among the 16 siblings. Nothing tells the agent to prefer get_trade for a known ID, get_performance for aggregates, or get_calendar for date-bucketed views.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.