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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful non-annotation context: it is informational only, not financial advice, and the full grading tier is gated behind an external room/token. It does not tell the agent what happens when optional inputs like mfe_points or stop_loss are omitted, which materially changes what can be scored.

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 analytical core is front-loaded, but the sentence is overloaded with promotional and gating content ('FREE preview + Pro Council Grade', named models @gpt/@deepseek/@kimi/AnansiNet, '$ANANSI') that does not help an agent decide or invoke correctly. Two of the four sentences are marketing rather than specification.

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?

There is no output schema, so the description carries the burden of conveying what comes back; it names the scored dimensions, which partially covers that. But with 16 optional parameters driving which checks run, it never explains the shape of the result (grade, score components, pass/fail on rule compliance) or how missing inputs degrade the analysis.

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% across all 16 parameters, with enums and bounds already documented, so the baseline of 3 applies. The description lists the analytical dimensions (R-multiple capture, rollover window, stop floor, time-stop) which loosely maps to fields like entry_utc_hour and stop_loss, but adds no format or dependency guidance beyond the schema.

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 names the resource and the analysis performed: a 'trade postmortem & MFE/MAE excursion expectancy analyzer' that scores entry/exit efficiency, stop placement vs ATR/MFE/MAE benchmarks, R-multiple capture and rule compliance. That is specific enough for an agent to know this is an evaluative tool on a single trade rather than a market-data or sizing tool. It does not explicitly contrast itself with the sibling council_grade, so it stops short of 5.

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?

Usage is only implied — the tool is framed as a 'FREE preview + Pro Council Grade' with an 'upgrade path' to full Council grading, which hints that it is the light-weight pre-check before council_grade. However there is no explicit statement of when to call this versus council_grade, position_size, or prop_firm_rules, and no exclusions or prerequisites.

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