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chain_batch_reads

$0.09 via x402: up to TEN on-chain reads for ONE payment, run in parallel — native balance, nonce, contract-code check, ERC-20 balance, block number and gas price in any combination. Reconciling a wallet normally costs one payment per read; this is the same data for a fraction of the calls and the latency. Per-read failures are reported individually with a partial flag, so a batch never claims success it did not have.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNobase|ethereum|optimism|arbitrum|polygon|gnosis (default base)
readsNoComma-separated ops, max 10: balance:0xADDR | nonce:0xADDR | code:0xADDR | erc20:0xTOKEN:0xWALLET | blocknumber | gas
x_paymentNo

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses the price ($0.09 via x402), parallel execution, max of ten reads, and per-read failure reporting with a partial flag, explicitly stating 'a batch never claims success it did not have.' This is rich behavioral context beyond the schema.

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?

Three concise sentences, each adding value: cost/cap/parallelism, cost/latency comparison, and failure semantics. No filler or redundancy. The most important details are front-loaded.

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?

Given moderate complexity and no output schema, the description covers the essential context: what operations are supported, how failures are handled, and the payment requirement. It does not explicitly describe the return format, but for standard on-chain reads this is largely self-explanatory. The absence of auth details is mitigated by the payment mention.

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

Parameters4/5

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

The input schema already covers 'chain' and 'reads' with formats, and the description reinforces those formats by enumerating the read types. The 'x_payment' parameter is not in the schema description, but the description mentions the $0.09 payment mechanism, which indirectly explains its purpose. This adds some meaning beyond the schema.

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 the tool performs batching of on-chain reads: 'up to TEN on-chain reads for ONE payment, run in parallel' and enumerates specific read types (balance, nonce, code, ERC-20, block number, gas price). This distinguishes it from sibling single-read tools like chain_native_balance or get_chain_nonce.

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?

The description provides a clear use case: batch multiple reads to save cost and latency, stating 'normally costs one payment per read; this is the same data for a fraction of the calls and the latency.' It does not explicitly name alternatives or state when NOT to use it, but the comparison implies batch use is preferred for multiple reads.

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

C2.7/5.0
Disambiguation2/5

Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.

Naming Consistency2/5

Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.

Tool Count1/5

At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.

Completeness3/5

The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.