This MCP server provides access to 106+ agent-payable micro-services across AI/ML, web, security, blockchain, and utility domains — all payable via x402 or direct crypto with no human in the loop.
Service Discovery & Catalog
List all available services (ddg_list_services) with pricing, view public resource index (ddg_public_resource_index), fetch specific DDG manifests/docs (ddg_fetch_public_resource), and check agent/service/rail status (ddg_agent_status).
AI & Model Services
List available models (ddg_list_models) and local runtime options like Ollama, llama.cpp, LM Studio, vLLM (ddg_list_local_runtime_options).
Run paid AI model routes (ddg_run_paid_model) for chat completions, LLM judging, embeddings, summarization, sentiment analysis, translation, and image generation.
Request new Ollama models for operator review (ddg_request_ollama_model).
Payments & Orders
Quote payment cost without executing compute (ddg_quote_payment), submit paid orders (ddg_submit_order), poll order status (ddg_order_status), and fetch completed artifacts (ddg_order_artifact).
Run a $0.01 transaction smoke test (ddg_tx_smoke_test) and review checkout/payment conformance (ddg_checkout_conformance).
Security & Safety
Review the MCP security profile (ddg_mcp_security_profile), browse the security service catalog (ddg_security_service_catalog), and run free static AI skill/workflow safety scans (ddg_skill_safety_scan).
Key Paid Services Available
Web/Network: Web search, URL fetching, screenshots, DNS lookups, WHOIS, IP geolocation, robots.txt checks.
Security: Threat check, SSL cert info, HTTP header analysis, TLS audit, subdomain enumeration, prompt injection scan, MCP tool security audit.
Blockchain: Contract ABI lookup, EVM RPC proxy, price feeds.
Compute/Docs: Python code execution (Docker sandbox), PDF extraction, OCR, QR code generation.
Utilities: Hashing, base64 encode/decode, UUID generation, timestamps, random data, JSON validation, text diff, language detection.
Stripe payment rail integration for processing payments (not yet live).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@io.github.daedalusdevelopmentgroup/ddg-agent-services-mcp run a payment readiness audit on my agent"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Pay-per-call AI agent services gateway. DDG gives agents one x402/direct-crypto entry point for callable tools, OpenAI-compatible routes, readiness audits, MCP security checks, discovery repair, receipts, and marketplace-ready proof artifacts.
124 x402/direct-crypto services for AI agents. The largest agent-payable service surface in the x402 ecosystem — from $0.001 utilities (DNS, hash, UUID) to $0.01 social/financial/agent-infra services. All fully automated with zero human in the loop. Includes an OpenAI-compatible gateway (/v1/chat/completions, /v1/models, /v1/embeddings).
https://agents.daedalusdevelopmentgroup.comfrom ddg_agent_services_mcp import ddg
client = ddg(agent_id="my-agent", private_key="0x...")
result = client.post("/v1/site-audit", {"url": "https://example.com"})Or configure once via environment:
export DDG_AGENT_ID="my-agent"
export DDG_PRIVATE_KEY="0x..."from ddg_agent_services_mcp import ddg
client = ddg() # reads from envDrop-in replacement for openai-python — point any OpenAI client at DDG:
from ddg_agent_services_mcp import create_openai_client
client = create_openai_client(agent_id="my-agent", private_key="0x...")
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)Supported routes: GET /v1/models, POST /v1/chat/completions, POST /v1/embeddings
Or use the standard openai package directly:
from openai import OpenAI
client = OpenAI(
base_url="https://agents.daedalusdevelopmentgroup.com/v1",
api_key="ddg-x402",
default_headers={"X-Agent-Id": "my-agent"},
)pip install ddg-agent-services-mcp
# With framework support:
pip install ddg-agent-services-mcp[langchain] # or crewai, openai-agents, autogen, etc.
pip install ddg-agent-services-mcp[all-frameworks] # everythingfrom ddg_agent_services_mcp.tools import create_langchain_tools
tools = create_langchain_tools(
agent_id="my-agent",
private_key="0x...", # Your EVM wallet key (Base USDC)
)
# Pass tools to your LangChain agent8 frameworks supported: LangChain, CrewAI, OpenAI Agents SDK, AutoGen, PydanticAI, LlamaIndex, Google ADK, and MCP.
{
"mcpServers": {
"ddg-agent-services": {
"command": "npx",
"args": ["-y", "@smithery/cli@latest", "install", "0xcircuitbreaker/ddg-agent-services-mcp"]
}
}
}Or direct HTTP: https://mcp.daedalusdevelopmentgroup.com/mcp
Related MCP server: x402-mcp
Rail | Status | Networks |
x402 | ✅ Live | Base, Polygon, Arbitrum, World Chain, Solana (USDC) |
direct_crypto_auto | ✅ Live | 13 asset families: EVM/stablecoins (ETH, USDC, USDT), BTC, BCH, LTC, DOGE, SOL, TRX, XRP, XLM, ALGO, DOT, ZEC, XMR |
direct_crypto_manual | ✅ Live | Operator-confirmed fallback |
MPP/Tempo | ✅ Live | Settlement-proven |
Service | Price | Description |
| $0.01 | Search Twitter/X posts via public syndication |
| $0.01 | Search Reddit posts and comments |
| $0.01 | Get a Reddit post with top comments |
| $0.01 | Get transcript/subtitles for a YouTube video |
| $0.01 | Search Hacker News stories and comments |
Service | Price | Description |
| $0.01 | Current stock price, OHLC, 52-week range (Yahoo Finance) |
| $0.01 | Historical OHLCV candles (Yahoo Finance) |
| $0.01 | Gold, silver, oil, copper, wheat, etc. |
| $0.01 | Foreign exchange rates for any pair |
| $0.01 | Search SEC EDGAR filings by ticker/query |
Service | Price | Description |
| $0.01 | Webhook delivery with retry, HMAC signing, delivery proof |
| $0.01 | Schedule one-shot or recurring HTTP tasks via systemd |
| $0.01 | Playwright browser automation: navigate, click, fill, extract |
| $0.01 | Extract structured JSON from any URL using fetch + LLM |
| $0.01 | Detect content changes at a URL (hash-based diff) |
Service | Price | Description |
| pay-per-call | OpenAI-compatible chat completions gateway |
| free | List available model aliases |
| $0.0005 | 768-dim vectors (Ollama nomic-embed-text) |
| $0.03 | Stable Diffusion v1.5 on GPU |
| pay-per-call | Bounded agent-task endpoint (local runtime) |
| $0.02 | Multi-model consensus via llm-judge |
| $0.01 | Neutral judge for multi-model consensus |
| $0.005 | Local LLM summarization |
| $0.002 | Sentiment analysis |
| $0.003 | Language translation |
| $0.001 | Language detection |
Service | Price | Description |
| $0.005 | SearXNG aggregator (20+ engines) |
| $0.002 | Raw content + headers from any URL |
| $0.001 | Quick HEAD liveness check |
| $0.001 | robots.txt compliance check |
| $0.001 | IP → country/city/ISP |
| $0.001 | DNS records (A/AAAA/MX/TXT/NS) |
| $0.002 | Domain registration data |
| $0.002 | Extract hyperlinks from a page |
| $0.002 | Clean markdown extraction |
| $0.005 | Headless Chromium screenshot |
Service | Price | Description |
| $0.005 | URL/wallet reputation (URLhaus + TLS) |
| $0.002 | SSL certificate chain + expiry |
| $0.001 | Security header analysis |
| $0.005 | Subdomain discovery via CT logs |
| $0.002 | TLS version + cipher suite audit |
| $0.01 | Prompt injection vulnerability scan |
| $0.05 | MCP server security audit |
Service | Price | Description |
| $0.002 | Verified ABI from block explorers |
| $0.005 | EVM RPC proxy (Base/Ethereum) |
Service | Price | Description |
| $0.01 | Python in Docker sandbox (no network) |
| $0.005 | Text extraction from PDFs |
| $0.005 | Image text extraction (Tesseract) |
| $0.001 | QR code PNG generation |
| $0.03 | Text-to-image (Stable Diffusion) |
Service | Description |
| SHA-256/MD5/BLAKE2 hashing |
| Encode/decode base64 |
| UUID v1/v3/v4/v5 |
| Current time in all formats |
| Secure random data |
| JSON Schema validation |
| Infer JSON Schema from sample |
| Text comparison/diff |
| Language detection |
| Crypto/forex prices |
See pricing.json for all 124 services.
Surface | URL |
AI manifest |
|
x402 discovery |
|
OpenAPI spec |
|
llms.txt |
|
Pricing |
|
Status |
|
Agent catalog |
|
Component | Hardware |
Payment edge | T620 (48 cores, 377GB RAM, 24/7) |
GPU (SD + embeddings) | T620 GTX 1080 8GB |
LLM inference | T620 Ollama (24 models) |
Code execution | Docker isolated containers |
Web search | SearXNG (self-hosted, 20+ engines) |
Email relay | Postfix |
Node | Alienware RTX 3080 8GB (secondary) |
MIT
Return DDG's machine-readable service/rail/MCP status document.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only states what is returned, not any behavioral traits (e.g., read-only, authentication needs, rate limits).
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?
Single sentence, no filler. Every word earns its 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?
Given zero parameters and presence of an output schema, the description is mostly complete. Lacks behavioral or usage context but adequate for a simple retrieval tool.
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?
Input schema has zero parameters, so schema coverage is 100%. The description adds context about the returned document, which is sufficient. Baseline 4 for 0-param tools.
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 returns a machine-readable status document. It uses a specific verb ('Return') and resource ('DDG's machine-readable service/rail/MCP status document'), distinguishing it from siblings like ddg_order_status.
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?
No guidance on when to use this tool vs alternatives. No context on prerequisites, limitations, or typical use cases is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return DDG's public checkout conformance profile without spending money.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds that the tool involves no cost, hinting at non-destructive behavior. However, no annotations provided, and description lacks details on read-only nature, auth requirements, or what the profile contains.
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?
Single sentence, front-loaded, no wasted words. Highly concise for a zero-parameter tool.
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?
Given zero parameters and presence of output schema, the description provides sufficient context for purpose and cost. Could elaborate on conformance profile but not essential.
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?
No parameters exist; schema coverage is 100%. Baseline 4 applies as description adds no parameter info, which is acceptable.
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?
Explicitly states the tool returns a 'public checkout conformance profile' and emphasizes it doesn't cost money, distinguishing it from sibling tools that involve payments.
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?
Implied usage: use when you need the conformance profile without spending money. No explicit when/when-not or alternatives, but straightforward due to zero parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch an allowlisted DDG public manifest/doc by id or ddg:// URI with redaction and size caps.
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description solely carries the burden of behavioral disclosure. It mentions 'redaction and size caps' but does not explain what they entail, whether the operation is read-only, or how errors are handled for non-allowlisted resources.
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 redundant words. However, it could be expanded slightly without sacrificing conciseness to cover missing details.
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?
Given the tool has one required parameter and no annotation, the description omits output expectations, error scenarios, and the impact of size caps/redaction. The presence of an output schema does not fully compensate for missing explanations.
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 coverage is 0% so the description must compensate. It suggests the parameter accepts an id or ddg:// URI but does not specify format, validation, or examples. The single parameter 'resource' remains ambiguous despite the hint.
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 uses a specific verb 'Fetch' and identifies the resource as 'allowlisted DDG public manifest/doc', clarifying both the action and the object. It distinguishes from sibling 'ddg_public_resource_index' which lists resources, so the purpose is clear.
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 does not provide any explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites like obtaining an ID or URI from an index. The context is implied but not directly stated for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List free-seat status plus requestable local runtimes such as Ollama, llama.cpp, LM Studio, OpenAI-compatible servers, and vLLM.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, authentication requirements, or resource constraints. It only hints at a read-only operation by listing status.
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 one concise sentence with front-loaded verb 'List', but includes vague phrasing like 'such as' and ends with 'etc.' which slightly reduces precision.
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?
Given 0 parameters and an existing output schema, the description adequately covers the tool's purpose and output. It could mention what 'free-seat status' means, but overall it is complete.
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?
There are zero parameters with 100% schema coverage, so the description adds value by explaining the tool's output. The baseline for 0 params is 4, and the description provides sufficient context.
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 lists 'free-seat status plus requestable local runtimes' and provides specific examples (Ollama, llama.cpp, LM Studio, etc.), making its purpose distinct from sibling tools like ddg_list_models and ddg_list_services.
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 implies usage (to see available local runtimes) but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List local/free Ollama models and queryable paid/account-backed route labels.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states what is listed but does not disclose any behavioral traits such as authentication requirements, side effects, or performance implications. For a read-only list, this is minimally adequate.
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?
Single sentence with no wasted words. Verb is front-loaded. Efficiently conveys the tool's scope.
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?
Given the tool has no parameters and an output schema exists, the description sufficiently covers what the tool does. Could be slightly improved by noting it is a discovery step before using request/run tools, but is already complete enough for a simple list endpoint.
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?
Tool has zero parameters. Baseline for 0 params is 4. Schema covers all parameters (none), so description adds no parameter information beyond that.
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?
Description uses specific verb 'List' and identifies resources: local/free Ollama models and queryable paid/account-backed route labels. Clearly distinguishes from sibling tools that request or run models.
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?
No guidance on when to use this tool vs alternatives. Does not mention prerequisites or context, such as that it should be used to discover available models before requesting or running.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List DDG live/manual services from the public pricing and catalog surfaces.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It only states the tool lists from 'public surfaces,' implying no side effects, but does not mention authentication, rate limits, or whether data is live or cached.
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?
Single sentence with the action verb 'List' front-loaded. Entirely concise with no wasted words.
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 parameterless tool with an output schema, the description is adequate but leaves gaps: it does not define 'live/manual services,' clarify the difference between 'pricing' and 'catalog' surfaces, or explain how this relates to sibling tools.
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?
There are zero parameters, and schema description coverage is 100%. The description adds meaning by explaining what the tool lists, compensating for the lack of parameters.
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 lists 'DDG live/manual services' from 'public pricing and catalog surfaces,' specifying the verb 'List' and the resource 'services.' It is distinct from sibling list tools like ddg_list_models, but no explicit differentiation is provided.
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?
No guidance on when to use this tool versus alternatives such as ddg_list_models or ddg_list_local_runtime_options. The description lacks context about prerequisites, scenarios, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return this MCP wrapper's local security controls and publication gates.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the tool returns data but does not state it is read-only, any permissions needed, or potential error cases. However, for a zero-parameter retrieval, the omission is less critical; a 3 is fair as it communicates basic behavior.
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 sentence of 10 words, front-loaded with the action 'Return', and no redundant phrases. Every word earns its 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?
Given the tool has an output schema (not shown), the description need not detail return values. However, it does not define 'local security controls' or 'publication gates,' which may require domain knowledge. For a simple 0-param tool, this is nearly complete.
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?
The tool has zero parameters and schema coverage is 100% (trivially). Description adds no parameter details, but baseline for 0 params is 4. No additional information needed.
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 returns local security controls and publication gates, using the specific verb 'Return' and identifying the resource. The name 'security_profile' aligns well, and it is distinct from sibling tools like 'ddg_security_service_catalog' which likely covers broader services.
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?
No guidance is provided on when to use this tool versus alternatives like ddg_security_service_catalog or ddg_skill_safety_scan. The description implies usage for checking security controls but lacks explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch an agent-scoped DDG order artifact when ready.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | ||
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only says 'fetch', implying a read operation. It does not disclose what happens if the artifact is not ready, required permissions, or potential side effects. The behavioral disclosure is minimal.
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 sentence with no extraneous words, efficiently conveying the core action and key constraints. It is front-loaded and earns its 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?
Given the tool's moderate complexity (two params, many siblings), the description omits preconditions, error handling, and the nature of the artifact. The output schema is present but does not compensate for missing usage and behavioral context.
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 coverage is 0%—the description does not explain the parameters. The phrase 'agent-scoped' hints at agent_id's role, but order_id is left implicit. No detail on format or constraints is added beyond the schema.
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 verb 'Fetch' and the resource 'DDG order artifact', and specifies scoping ('agent-scoped') and condition ('when ready'). It distinguishes from sibling tools like ddg_order_status (which provides status) and ddg_submit_order (which submits orders).
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 phrase 'when ready' implies usage context but does not explicitly state preconditions (e.g., check order status first) or provide alternatives. No exclusions or comparisons to siblings are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Poll an agent-scoped DDG order status URL.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | ||
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral aspects such as polling frequency, side effects, or required permissions. The term 'poll' implies repeated calls, but that is not explicit.
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 concise sentence, front-loaded with the key action and resource. No unnecessary words, but could include more detail without losing conciseness.
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?
Given the presence of an output schema, the description need not detail return values. However, it lacks information on operational context like polling etiquette or error handling, making it adequate but not complete.
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 0%, so the description must compensate. It only hints at 'agent-scoped' but does not explain the parameters like agent_id or order_id meaning, default values, or usage.
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's action (poll), the resource (DDG order status URL), and the scope (agent-scoped). It effectively distinguishes from siblings like ddg_submit_order or ddg_order_artifact.
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?
No guidance on when to use this tool vs alternatives, no conditions or exclusions provided. Sibling tools exist but no differentiation is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List allowlisted DDG public manifests/docs available as MCP resources.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It correctly indicates a listing operation but adds no details about safety, rate limits, or response behavior. Since it's a simple read-only list, the description is adequate but minimal.
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?
Single sentence with 8 words, front-loaded with the action verb 'List'. No redundancy or filler; every word earns its 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?
Given no parameters and an existing output schema, the description is adequate but could be more helpful by explaining what 'MCP resources' entails or linking to the fetch tool. It's complete enough for a simple list but lacks context for new users.
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?
No parameters exist (schema coverage 100%). Description adds context by specifying the scope ('allowlisted... available as MCP resources'), which helps understand the output. Baseline for 0 params is 4.
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's action ('List') and the specific resource ('allowlisted DDG public manifests/docs available as MCP resources'), distinguishing it from siblings like ddg_fetch_public_resource that fetches a specific resource.
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?
Usage is implied (discovery of available resources), but no explicit guidance on when to use vs. alternatives (e.g., ddg_fetch_public_resource for a specific resource). No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the payment challenge for a supported DDG protected route without executing backend compute.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | /v1/model/chat-completions | |
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool does not execute backend compute, which indicates it is non-destructive but lacks details on authentication requirements, rate limits, or consequences of misuse.
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 sentence of 15 words, making it concise and front-loaded. It earns its place by providing essential purpose information, though it could add parameter context without significant bloat.
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?
The tool has 2 optional parameters and an output schema. The description adequately conveys the core action but omits context about the output schema or how the payment challenge is used. It is minimally viable but could be more complete.
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 0%, but the description does not explain the parameters (path and agent_id). With 0% coverage, the description should compensate by describing parameter roles, but it does not, leaving the agent with only schema defaults.
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 returns a payment challenge without executing backend compute, specifying the verb 'return' and resource 'payment challenge'. It distinguishes from sibling tools that execute actions (e.g., ddg_submit_order), but could be clearer about what a 'payment challenge' entails.
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?
No guidance on when to use this tool versus alternatives like ddg_submit_order or ddg_run_paid_model. The description implies it's for quoting without execution, but does not explicitly state use cases, when-not-to, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Describe the planned free receipt-verification tool contract.
This is intentionally marked not-live until /v1/receipt-verify is implemented
and backed by payment-edge audit/state reconciliation.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | ||
| receipt_hash | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even without annotations, the description transparently communicates that the tool is a design contract and not yet functional. This key behavioral trait (non-live, planning stage) is clearly stated, helping the agent avoid misuse.
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?
Two concise sentences: the first states the tool's purpose, the second adds essential context about liveness. No redundant or irrelevant information.
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?
Given that this is a design tool with an output schema, the description should mention what the output represents (e.g., a contract specification). It is silent on the output, leaving the agent to guess. However, for a non-live planning tool, the core purpose and limitation are covered.
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?
With 0% schema description coverage, the description adds no information about the two parameters (order_id, receipt_hash). The agent only knows their names and types, missing details on format, purpose, or constraints.
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 states 'Describe the planned free receipt-verification tool contract,' which is a clear verb-resource pair indicating a design/planning tool. It distinguishes itself from sibling operational tools like ddg_order_status and ddg_submit_order by being explicitly non-live and design-focused.
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 context on when not to use the tool ('intentionally marked not-live until /v1/receipt-verify is implemented'). However, it does not explicitly state when to use it or compare it to alternative tools for receipt verification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Queue a local model/runtime request. This never auto-downloads by public request.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| reason | No | requested by agent swarm | |
| expected_size_gb | No | ||
| runtime | No | ollama | |
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only mentions that it never auto-downloads, but omits details on queue behavior, permissions, or error handling. The description does not reveal important behavioral traits.
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 brief (two sentences) and front-loaded with the core purpose. However, it is under-specified for the tool's complexity, and additional parameter context would be valuable.
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?
Given 5 parameters with 0% schema description coverage and no annotations, the description is insufficient. Even though an output schema exists, the description fails to explain what the tool does in enough detail for correct invocation.
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 0%, and the tool has 5 parameters. The description adds no meaning beyond the parameter titles, failing to explain fields like reason, runtime, agent_id, or expected_size_gb.
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 it queues a local model/runtime request and explicitly notes it never auto-downloads by public request. This distinguishes it from sibling tools like ddg_run_paid_model or ddg_list_models.
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?
No explicit guidance on when to use this tool vs alternatives. It implies it's for requesting local models that aren't auto-downloaded, but does not clarify prerequisites or contrast with tools like ddg_list_local_runtime_options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a paid model/chat or agent-run route after caller supplies valid payment headers.
| Name | Required | Description | Default |
|---|---|---|---|
| route | Yes | ||
| prompt | Yes | ||
| payment_headers | No | ||
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It mentions the need for valid payment headers but does not disclose side effects, error behavior, or reversibility. This is adequate for a straightforward run tool but lacks depth.
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 sentence with no wasted words, front-loading the core purpose and precondition. Efficient for a simple tool.
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?
With 4 parameters (2 required), no parameter descriptions, and only minimal behavioral info, the description is incomplete. Although an output schema exists, the description adds no details about the return value or process.
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 0%, and the description does not explain any parameter (route, prompt, agent_id, payment_headers). It only mentions payment headers indirectly, leaving the agent uninformed about required parameters.
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 action ('run') and the resource ('paid model/chat or agent-run route'), and specifies the precondition of valid payment headers. This distinguishes it from sibling tools like ddg_list_models or ddg_request_ollama_model.
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 implies when to use (when payment headers are available) but does not explicitly contrast with alternatives or state when not to use it. Context signals show no sibling differentiation guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return DDG's AI-agent cybersecurity service catalog.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states it returns a catalog, with no disclosure of behavioral traits like read-only nature, authentication needs, or rate limits.
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?
A single sentence of 9 words, very concise and front-loaded with the action and resource. No unnecessary words.
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?
Given no parameters and an output schema that covers return values, the description is minimally complete. It could mention the output type (list) but is sufficient for a simple catalog retrieval.
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?
No parameters exist, and schema coverage is 100%. The description adds no parameter information, which is acceptable per guidelines for zero-parameter tools.
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 verb 'Return' and the specific resource 'DDG's AI-agent cybersecurity service catalog'. It distinguishes from sibling tools like ddg_list_services by specifying 'cybersecurity', though it doesn't explicitly differentiate.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run the free static-only DDG AI skill/workflow safety scan.
The scan never executes submitted code and redacts secret-like evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| skill_markdown | Yes | ||
| label | No | mcp-client-submission | |
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the scan never executes code and redacts secret-like evidence, providing key safety and privacy behaviors.
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?
Two sentences, front-loaded with purpose, no wasted words. Highly efficient.
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?
Missing parameter descriptions and usage prerequisites, but output schema exists and safety scan concept is simple. Adequate but not comprehensive.
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 coverage is 0%, but description adds no explanation for the three parameters (skill_markdown, label, agent_id). Agent cannot infer their meanings from description alone.
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?
Description clearly states the tool runs a safety scan, with specific verb 'run' and resource 'DDG AI skill/workflow'. No sibling tool shares this purpose.
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?
Indicates the scan is static-only and never executes code, but does not explicitly state when to use this tool versus alternatives. However, sibling tools are distinct, so context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a paid operator-reviewed DDG order after caller supplies valid payment headers/proof.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes | ||
| request | Yes | ||
| payment_headers | No | ||
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behaviors. It mentions 'operator-reviewed' indicating a manual step, but does not detail what happens after submission (e.g., asynchronous, returns order ID). The presence of an output schema reduces the burden, but more context on the review process would improve transparency.
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 sentence of 14 words, making it concise. However, it is too sparse given the tool's complexity and the lack of schema descriptions, leaving critical details unaddressed.
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?
With 4 parameters, a nested object, and an output schema, the description is incomplete. It does not explain the 'request' object, how to obtain 'service_id', or the format of 'payment_headers'. The output schema may help, but the description should still provide overall workflow context.
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 0%, so the description must compensate. It only explains 'payment_headers' (as 'payment headers/proof'), but fails to describe 'request', 'agent_id', and 'service_id'. This is a significant gap for a 4-parameter tool with nested objects.
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 action 'Submit' and the resource 'paid operator-reviewed DDG order', with a prerequisite condition. This distinctively differentiates it from sibling tools like ddg_order_status or ddg_quote_payment.
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 implies when to use: after obtaining valid payment headers/proof. However, it does not explicitly state when not to use or list alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Exercise the one-cent DDG transaction smoke-test route with caller-supplied payment headers.
| Name | Required | Description | Default |
|---|---|---|---|
| payment_headers | No | ||
| agent_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It mentions 'smoke test' but does not clarify side effects (e.g., whether a real charge occurs), permissions needed, or idempotency. The agent lacks critical safety information.
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?
Single sentence, directly states the core function. No unnecessary words. Front-loaded with the key action and context.
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?
Despite having two parameters and an output schema (unseen), the description provides no information about return values, error cases, or prerequisites. For a tool with zero annotation coverage, the description is too minimal to fully inform the agent.
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 0%, so the description must explain parameters. It only mentions 'payment_headers' as 'caller-supplied' but does not explain format or role of 'agent_id'. Agent_id is left completely undocumented, and payment_headers lacks detail on required keys or structure.
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?
Description clearly states the tool exercises a specific 'one-cent DDG transaction smoke-test route' with caller-supplied payment headers. This distinguishes it from sibling tools like ddg_submit_order (for actual orders) and ddg_quote_payment (for quotes), making the purpose very clear.
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?
No guidance on when to use this tool versus alternatives. It does not mention that this is a test route not for production use, nor does it compare with sibling tools like ddg_submit_order. The description assumes implicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0ddg_agent_statusddg_checkout_conformanceddg_fetch_public_resourceddg_list_local_runtime_optionsddg_list_modelsddg_list_servicesddg_mcp_security_profileddg_order_artifactddg_order_statusddg_public_resource_indexddg_quote_paymentddg_receipt_verify_designddg_request_ollama_modelddg_run_paid_modelddg_security_service_catalogddg_skill_safety_scanddg_submit_orderddg_tx_smoke_testScored across 18 tools
Each tool has a clearly distinct purpose, with no overlapping functionality. Even related tools like ddg_list_local_runtime_options, ddg_list_models, and ddg_list_services target different aspects of the system.
All tools follow a consistent 'ddg_verb_noun' pattern using lowercase with underscores. The naming is uniform and predictable, making it easy for an agent to infer tool behavior from the name.
With 18 tools, the count is slightly above the ideal range of 3-15, but the broad scope of the server (status, orders, models, security, payments, etc.) justifies the number. Each tool covers a distinct function without feeling bloated.
The tool set covers a wide range of operations but lacks some lifecycle management features. For example, there is no tool to list orders or update/cancel them, and no tool to manage models beyond requesting. This leaves some potential dead ends for workflows.
Payment decisions, durable evidence, x402 resource discovery and live gateway status for AI agents.
Paid token risk and security intelligence for AI agents over MCP with x402 payments.
Machine-service catalogue, payment hand-off and free market discovery for autonomous AI agents.