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mev_intel

Requires a ChainHelix API key as bearer token, or 2 cents per call paid over Binance b402 in USDT, USDC, USD1 or U on BNB Smart Chain, or over x402 in USDC on the Base network. MEV event rates per chain and type vs the trailing weekly pace. window in hours, default 24, max 168. Types overlap and must not be summed: a backrun transaction also emits an arbitrage row. NFT coverage is ERC-721 only, so nft_mev excludes all ERC-1155 activity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNo
windowNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does an excellent job: it discloses authentication requirements, payment options, the comparison baseline ('trailing weekly pace'), output interpretation caveats ('Types overlap and must not be summed'), and the ERC-1155 exclusion. These are non-obvious behaviors that could otherwise cause incorrect usage.

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 three dense sentences with no filler. Every clause adds operational value, from auth and payment to window constraints, overlap warnings, and NFT coverage limitations.

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

Completeness4/5

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

For a read-only data tool with an output schema, the description covers auth, payment, window semantics, and behavioral caveats. It does not state what happens when `chain` is omitted or enumerate the MEV types, but the output schema likely covers return structure, leaving only minor gaps.

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

Parameters5/5

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

The schema provides zero property descriptions, so the description must compensate for both parameters. It fully specifies `window` with units, a default, and a maximum. The `chain` parameter's meaning is self-apparent from its enum of chain names, and the description reinforces the 'per chain' notion, making the two parameters fully understandable.

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?

The core function is stated as 'MEV event rates per chain and type vs the trailing weekly pace', which clearly identifies the resource being measured. However, there is no explicit verb like 'get' or 'fetch', and it does not distinguish itself from the sibling `mev_bots` beyond the obvious name difference.

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

Usage Guidelines3/5

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

The description provides practical usage constraints such as 'window in hours, default 24, max 168', the overlap warning for event types, and the ERC-721 NFT coverage limit. But it never says when to prefer this tool over a sibling or when not to use it, so alternatives are not addressed.

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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TDQS

A3.5/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists among report tools (situation_report, deep_report, market_state) and spec tools (attest_spec, proof_spec). These could confuse an agent selecting the most appropriate endpoint.

Naming Consistency4/5

Tool names use consistent snake_case and are descriptive, but follow mixed patterns: verb_noun for actions (buy_key, list_attestations) and bare nouns or noun phrases for data endpoints (prices, congestion, wall_map). This is readable but not fully uniform.

Tool Count3/5

With 26 tools, the set is heavy and covers multiple domains (attestation, market data, key management, webhooks). It sits just above the borderline for comfort, but the breadth is justifiable given the server's multi-purpose nature.

Completeness5/5

The tool surface provides full lifecycle coverage for attestation (create, read, verify), key management (buy, claim, status), and webhooks (register, status, unregister), plus a wide range of market data endpoints. There are no obvious gaps or dead ends.

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