Crypto Data & Market Analysis Agent

get_execution_cost

Read-onlyIdempotent

What a given trade size actually costs to execute right now, walked through the live order books of Bybit and OKX. Every other tool here reports where price is; this one reports the price YOU would get at YOUR size. Returns, per requested notional and separately for buying and selling: the average fill price, slippage against mid in basis points AND in dollars, the spread, the depth sitting within 1% of mid, and which venue is cheapest for that size. The floor is half the spread; anything above it is the size eating through levels. A size the book cannot absorb comes back as "filled": false with the amount that could actually be filled, never an extrapolated price. This is the number that decides whether a correct thesis still makes money after costs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`coin`	Yes	Coin symbol, e.g. "BTC", "ETH", "SOL". The USDT perpetual is read.
`sizesUsd`	No	Trade sizes to price, in US dollars. Defaults to 10000, 50000 and 250000. At most five.

Output Schema

TableJSON Schema

Name	Required	Description
`coin`	Yes
`venues`	Yes
`bandPct`	No	Half-width of the depth band around mid, in percent.
`cheapest`	Yes	Per requested size, the venue with the lowest slippage among those that can fill it.
`sizesUsd`	Yes
`unavailable`	Yes	Venues with no usable book for this coin. Never dropped silently.
`unavailableDetail`	No	The same venues with the reason each one failed, so a missing venue is diagnosable.

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint, the description discloses extensive behavioral details: it walks through live order books, defines the floor as half the spread, and explains that if a size cannot be absorbed, it returns 'filled': false with the actual fillable amount instead of an extrapolated price. No contradiction with annotations.

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 well-structured and front-loaded with the core purpose, then differentiates from siblings, summarizes the output, covers an edge case, and states its decision-making value. Every sentence earns its place despite the length.

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

Completeness5/5

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

For a tool of this complexity, the description is remarkably complete. It covers behavior, outputs, venue comparison, and failure handling. The output schema provides return-value structure, and annotations cover safety, so no significant gaps remain.

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

Parameters3/5

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

Schema coverage is 100% with descriptions for both coin and sizesUsd. The tool description adds context about output (per requested notional, buying/selling) but does not materially change the meaning of the parameters themselves, so baseline 3 is appropriate.

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 identifies the tool as reporting execution cost for a given trade size using live order books from Bybit and OKX. It explicitly distinguishes this from siblings: 'Every other tool here reports where price is; this one reports the price YOU would get at YOUR size.'

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

Usage Guidelines5/5

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

The description explicitly contrasts this tool with all others, making it clear when to use it: when the agent needs cost-at-size rather than just a price quote. It reinforces the use case by stating 'This is the number that decides whether a correct thesis still makes money after costs.'

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

All Crypto Data & Market Analysis Agent tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct data domain—network health, market prices, news, DeFi, derivatives per-venue vs aggregate, economic calendar, ETH address, whale flows, execution cost, sentiment, implied volatility, macro rates, market brief, dominance, history, and traditional market quotes. The only similar pair (get_derivatives vs get_derivatives_aggregate) is clearly differentiated by level of detail, so there is no real ambiguity.

Naming Consistency5/5

All tools follow the get_<domain> pattern with descriptive noun phrases (e.g., get_btc_network, get_eth_whale_flows, get_market_brief). No mixed verb styles or casing conventions appear, making the naming fully predictable and consistent.

Tool Count4/5

At 17 tools, the surface is slightly above the ideal 3-15 range, but the breadth of the domain—spot, derivatives, on-chain, macro, sentiment, and execution—justifies each tool. It's borderline but each tool earns its place in a comprehensive market analysis agent.

Completeness5/5

The toolset covers nearly every major facet of crypto market analysis: prices, history, dominance, derivatives, on-chain activity, DeFi, macro, economic calendar, sentiment, implied volatility, execution cost, and a composite brief. Minor gaps like historical OHLCV or multi-chain on-chain analytics are not critical given the agent's stated focus.

Resources

Need Help?