agentdata-nl

llm_chat

LLM chat completion per call — no account, no API key, no token math. Three flat-priced tiers: fast $0.002 (DeepSeek v4 Flash), smart $0.02 (GPT-5.4 mini), reasoning $0.03 (DeepSeek v4 Pro). Send OpenAI-style messages, get the assistant reply with finish_reason and token usage. Input capped per tier (16k-32k chars); the 402 quotes the exact tier price up front. Model or source unavailable means a 503 and you pay nothing. USDC on Base.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No	Price tier: fast $0.002 (DeepSeek v4 Flash), smart $0.02 (GPT-5.4 mini), reasoning $0.03 (DeepSeek v4 Pro)	fast
`messages`	Yes	OpenAI-style chat messages; combined content capped at 16k chars (fast) or 32k chars (smart/reasoning)
`max_tokens`	No	Output token cap; tier maxima: fast 1024 (default 512), smart 2048 (default 1024), reasoning 4096 (default 2048, minimum 256 — thinking tokens are spent first)
`temperature`	No	Optional sampling temperature (0-2)

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations provided, the description fully bears the burden of behavioral disclosure. It covers pricing per tier, input/output token caps, error conditions (402/503), payment method (USDC on Base), and return format (assistant reply with finish_reason and token usage). No contradictions or gaps.

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 a single, well-structured paragraph that front-loads the most critical information (no account, no API key, no token math) and includes all necessary details without redundancy. Every sentence earns its place.

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

Completeness5/5

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

Despite no output schema, the description sufficiently explains the return format. Given the tool's complexity (multiple pricing tiers, input caps, error handling), the description is complete enough for an agent to select and invoke the tool correctly.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining pricing tiers in context, combined input character limits per tier, and output token defaults. It also clarifies that thinking tokens are spent first for reasoning tier.

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 'LLM chat completion per call' and distinguishes itself from sibling tools (which are mostly business/check tools) by highlighting unique features like no account needed, flat pricing tiers, and OpenAI-style messages. No confusion about what this tool does.

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 clear context for when to use this tool (for chat completions without accounts or API keys) and explains pricing and input caps. However, it does not explicitly state when not to use it or mention alternatives, but since no sibling tool offers similar functionality, the guidance is adequate.

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

A4.1/5.0

Disambiguation5/5

Every tool targets a distinct action and domain: country-specific company checks (check_ch_company, check_fr_company, etc.), insolvency checks, crypto signals, x402 network tools, etc. Descriptions are detailed and make boundaries clear, with no two tools appearing to do the same thing.

Naming Consistency5/5

Tool names follow very consistent patterns: 'check_<country>_<entity>' for registers, 'crypto_*' for crypto intelligence, 'x402_*' for x402 network functions, 'screen_*' for screening, and a few free-form names like 'verify_eu_vat' and 'lookup_lei' that still fit the verb_noun style. No mixing of conventions.

Tool Count4/5

39 tools is high but justified by the broad scope: the server aggregates many country-specific checks, insolvency registers, crypto tools, x402 monitoring, and auxiliary functions (like phone number buying, LEI lookup). Each tool serves a clear purpose, and the count is not excessive given the coverage. A slight reduction could be possible by merging some country checks, but overall it's reasonable.

Completeness4/5

The server covers major European company registers (CH, UK, FR, NL, NO, PL, CZ, FI), insolvency checks (NL, FR), sanctions screening, VAT validation, crypto market intelligence, and x402 network tools. It acknowledges gaps (e.g., no German check) and provides fallback tools like 'screen_eu_supplier' and 'file_agent_want'. Minor missing pieces (e.g., Italian company check) keep it from a perfect 5.

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