$0.09 via x402: read a raw contract storage slot (eth_getStorageAt) across 6 chains — inspect contract state directly, for audit, proxy-resolution and advanced trading agents. Pass address + slot.
| Name | Required | Description | Default |
|---|---|---|---|
| slot | Yes | Storage slot, hex or decimal | |
| chain | No | base|ethereum|optimism|arbitrum|polygon|gnosis (default base) | |
| address | Yes | Contract address 0x... | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses read-only behavior, cost ($0.09 via x402), and multi-chain support. However, it does not mention error handling, response format, or potential edge cases, leaving room for improvement.
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 description is a single, front-loaded sentence with no fluff. It conveys purpose, cost, chains, use cases, and required parameters efficiently, earning every word's place.
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?
For a simple read tool, the description covers essentials: what it does, cost, chains, and use cases. Without an output schema, it could mention the return format, but the description is sufficient for an agent to select and invoke it correctly.
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 75% (address, slot, chain described; x_payment not). The description adds 'Pass address + slot' and cost context for x_payment, but does not enrich the core parameter semantics beyond what the schema already provides.
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 clearly states the tool reads a raw contract storage slot via eth_getStorageAt, with specific use cases (audit, proxy resolution, advanced trading). This distinguishes it from sibling chain_* tools like chain_native_balance or chain_batch_reads by naming the exact resource and method.
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?
The description provides clear context by naming target use cases ('for audit, proxy-resolution and advanced trading agents'), implying when to use it. However, it does not explicitly state when not to use it or name alternatives, so it lacks full exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.