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pretrade_full_report

$0.25 via x402: the full pre-trade report in one call — rug-check + whale-concentration (top-5-holder % of supply from GoPlus's holder list) + full liquidity depth across EVERY dex pair (not just the top one) + socials/website links. Same free upstreams as the $0.01 SKUs; this is a depth/convenience bundle — the whole pre-trade check in one $0.25 call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoethereum|base|bsc|polygon|arbitrum|optimism|avalanche (default ethereum)
addressYesToken contract address 0x...
x_paymentNo

Schema Changelog

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

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description adds significant behavioral context: it discloses the $0.25 cost via x402, the data sources (GoPlus holder list, all DEX pairs), and that it uses same free upstreams as cheaper SKUs. This goes beyond what annotations would typically provide.

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?

Every sentence in the description adds value: pricing, purpose, specifics of included checks, and contrast with cheaper versions. It is front-loaded with key information and avoids redundancy.

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?

The description lists what the report contains (rug-check, whale-concentration, etc.), which gives a good idea of the output, but it does not specify the response format, schema, or error handling. Given no output schema, this is a moderate gap.

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?

The description does not add any meaning beyond the input schema. Two of three parameters (chain, address) are already documented in the schema, but the x_payment parameter is left undescribed in both schema and description, and the description offers no parameter-level guidance.

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 a comprehensive pre-trade report including rug-check, whale-concentration, liquidity depth, and socials/website links. It distinguishes itself from cheaper SKUs by being a depth/convenience bundle, making its purpose unambiguous.

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 implies that this tool is for a full pre-trade check when you need depth and convenience, but it does not explicitly state when to use it versus sibling tools like token_security_check or dex_token_data, nor does it provide when-not-to-use guidance.

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.