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alekskram

dydx-agent-gateway

Fills Review

fills_review
Read-only

Analyze the latest fills for execution quality: maker/taker split, traded volume, average fill size, and top markets. Returns a summary or 'no fills' when no fills exist.

Instructions

Execution review from the latest fills: maker/taker split, per-market distribution, traded volume, avg fill size. (Per-fill PnL is not exposed by the indexer; use trader_pnl_stats for win-rate.) Key fields: fills_sampled (count), maker_share_pct (0-100), sampled_volume_USD, avg_fill_USD, top_markets (top 5 by fill count). Returns {"summary": "no fills"} for accounts with no fills. Example: fills_review(address="dydx1m9hg73dtn5ku8ulmj8rjmdqh0hk7uuhawc69cn")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
addressYes
subaccountNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already show read-only, non-destructive, open-world behavior. The description adds valuable behavioral details: the indexer does not expose per-fill PnL, the tool returns a 'no fills' summary for empty accounts, and returned fields are sampled aggregates.

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

Conciseness5/5

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

The description is dense but every sentence earns its place: purpose, exclusions, key fields, edge case, and example. It is front-loaded and avoids filler while still giving enough detail for an agent.

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

Completeness3/5

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

For a read-only aggregation tool with an output schema, the description covers the main return fields and edge behavior. However, the lack of parameter documentation for `limit` and `account` leaves a real gap for correct invocation beyond the minimal address example.

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 description coverage is 0%, so the description should explain parameters. It gives an example for `address`, but `limit` and the `account` parameter are not semantically described. The 'Key fields' section refers to response fields, not input parameters.

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

Purpose5/5

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

The description clearly states what the tool produces: an execution review from latest fills, with maker/taker split, per-market distribution, volume, and average fill size. It also differentiates itself from sibling `trader_pnl_stats` by explicitly stating per-fill PnL is not exposed.

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

Usage Guidelines4/5

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

It gives an explicit alternative: use `trader_pnl_stats` for win-rade or per-fill PnL. It also describes the no-fills edge case. It does not enumerate every alternative like `raw_fills`, but the context is clear enough for routing.

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

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