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connect_token

$0.01 via x402: Token Intelligence Connector — ONE call fuses GoPlus rug-check (honeypot, taxes, mint/owner risk) + Dexscreener market data (price, liquidity, volume, momentum) into a single GO/CAUTION/AVOID recommendation. The aggregator call trading & sniping agents make instead of chaining rug-check + dex separately.

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
  2. Removed
  3. Added
  4. Removed
  5. Added

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that the call is paid ($0.01 via x402), that it combines two third-party data sources, and that the output is a categorical recommendation. It doesn't mention failure modes or exact return formatting, but the core semantics are clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences and packs in purpose, data sources, output, cost, and alternative behavior. It is slightly promotional and starts with price, but it remains tight and front-loads the high-level function.

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 simple paid aggregator with one required parameter, the description covers what the tool does, what it costs, what it returns, and how it differs from chaining alternatives. Without an output schema, it names the recommendation categories, which is sufficient guidance for invocation.

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 already describes address and chain, with 67% coverage. The description adds the aggregator context and the payment mechanism, but the x_payment parameter remains unexplained in both schema and description. Baseline 3 is fair because the description partially compensates but doesn't fully cover the unknown parameter.

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 names a concrete resource ('token intelligence'), a specific action/outcome ('fuses GoPlus rug-check + Dexscreener market data into a single GO/CAUTION/AVOID recommendation'), and an aggregation role. This clearly separates it from sibling tools like token_security_check and dex_token_data.

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?

It states the intended use case: the aggregator call trading and sniping agents make 'instead of chaining rug-check + dex separately.' This gives an explicit alternative, though it does not spell out when an agent should instead call token_security_check or dex_token_data alone.

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.