This server offers a suite of edge-computing and AI-powered tools via the 0mod API Gateway, with automatic micropayment handling. Capabilities include:
Stealth web fetching: stealth_dom fetches web pages from the edge, bypassing simple IP blocks.
PII redaction: airgap_scrub removes sensitive data like SSNs, phone numbers, emails, and ZIP codes.
HTML cleaning: rag_shrink converts raw HTML to clean Markdown for RAG contexts.
Code denoising: code_denoise strips comments, docstrings, and sourcemaps from code.
Domain checks: domain_check queries RDAP for domain availability and WHOIS status.
DEX price data: dex_price_summary returns real-time token price, volume, and liquidity from decentralized exchanges.
Sentiment analysis: x_sentiment analyzes social and market sentiment for topics or tokens.
Image OCR: image_ocr_shrink extracts text and tables from images.
Text embeddings: embed_text generates 768-dim embeddings for semantic search; embed_multilingual provides 1024-dim multilingual embeddings.
Summarization: summarize_text produces structured summaries in bullet, paragraph, or executive format.
Provides tools that leverage Cloudflare's edge network and Workers AI for headless web fetching, HTML-to-markdown conversion, OCR, sentiment analysis, and summarization.
Click on "Install 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., "@@zeromodern/mcp-server-0mod Check if example.com is available"
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.
Model Context Protocol (MCP) server wrapping the 0mod API Gateway edge tools for any AI agent. HTTP 402 micropayments on Base EVM are handled automatically.
0mod gateway utilities use x402 HTTP 402 micropayments on Base EVM:
Network: Base Mainnet (eip155:8453)
Asset: USDC on Base
Environment Variable: PAYER_PRIVATE_KEY=0x... (or EVM_PRIVATE_KEY / X402_PRIVATE_KEY)
When PAYER_PRIVATE_KEY is present in the environment, tool calls transparently sign payment authorizations and execute with zero manual intervention.
Related MCP server: 20-tool MCP server
Node.js >= 18
npm >= 9
npm install @zeromodern/mcp-server-0modclaude_desktop_config.json)Add to your Claude Desktop configuration:
{
"mcpServers": {
"0mod": {
"command": "npx",
"args": ["-y", "@zeromodern/mcp-server-0mod"],
"env": {
"PAYER_PRIVATE_KEY": "0x_your_private_key_here"
}
}
}
}Run directly via npx:
PAYER_PRIVATE_KEY=0x_your_private_key_here npx -y @zeromodern/mcp-server-0mod💡 Pricing: For live per-call pricing and endpoint status across all tools, visit api.0mod.com or fetch
https://api.0mod.com/api/v1/discovery.
Tool Name | Description | Input Schema Example |
| Headless web page fetch from Cloudflare edge |
|
| Redact SSN, phone, email, ZIP via Workers AI |
|
| Strip HTML boilerplate to clean Markdown for RAG |
|
| Remove comments, docstrings, sourcemaps from code |
|
| Query RDAP registry for domain availability |
|
| Real-time DEX token price, volume, liquidity |
|
| Social & market sentiment scoring |
|
| Vision OCR text and table extraction |
|
| 768-dim text embedding generation |
|
| 1024-dim multilingual text embedding generation |
|
| Executive TL;DR document summarization |
|
🤖 MCP Server (Any AI Agent): @zeromodern/mcp-server-0mod
🟣 ElizaOS Plugin: @zeromodern/eliza-plugin-0mod
🔵 Coinbase AgentKit Provider: @zeromodern/agentkit-provider-0mod
⚡️ Live Gateway Service: api.0mod.com
This server is registered on Smithery for one-click deployment.
Server not connecting: Verify your MCP client supports stdio transport. Check that PAYER_PRIVATE_KEY is passed in the env block.
Authentication / Payment errors: Ensure PAYER_PRIVATE_KEY is set with a valid Base EVM private key holding a USDC balance for x402 micropayments.
Timeout errors: Micropayment verification on Base adds network latency. Increase your MCP client's request timeout if needed.
MIT
Redact SSN, phone, email, and ZIP codes using Workers AI
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text content containing sensitive PII |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It mentions 'using Workers AI' but does not clarify whether the operation is read-only, modifies input in place, returns new text, or how redaction is performed. This lack of safety and outcome details is a significant gap.
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 filler words. It conveys the essential action and scope efficiently, earning a maximum score for 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 tool's simplicity (one parameter, no output schema), the description is mostly sufficient but omits expected return behavior or side effects. It does not state whether the text is returned redacted or mutated in place, which is crucial for agent usage. The lack of annotations further reduces completeness, so a middle score is appropriate.
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 schema covers 100% of parameters, but its description ('Text content containing sensitive PII') is vague. The tool description adds concrete meaning by specifying which types of PII (SSN, phone, email, ZIP codes) are targeted, thereby enriching the parameter's semantic beyond the schema baseline.
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 identifies the action (redact) and the target data (SSN, phone, email, ZIP codes), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools by naming alternatives or unique constraints, so it falls just short of a 5.
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 given on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description simply states what the tool does without contextualizing its appropriate use cases, leaving the agent without direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Strip comments, docstrings, whitespace, and sourcemaps from code files
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Code content to clean | |
| language | No | Programming language |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It lists what gets stripped but does not disclose whether the operation is lossy, what happens with invalid input, whether it modifies in place, or what the return value looks like. The lack of output schema further amplifies this gap.
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 that directly states the tool's purpose. There is no redundant information or filler.
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 no annotations and no output schema, yet the description provides no information about return values, error behavior, or language handling. While the tool is relatively simple, the description still leaves essential details unstated.
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 100% for both parameters (code and language), so the schema already documents them fully. The description adds no additional meaning or constraints beyond the schema, resulting in a baseline score of 3.
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 ('Strip') and clearly identifies the resource ('comments, docstrings, whitespace, and sourcemaps from code files'). This distinguishes it from sibling tools, which generally target other data types or operations.
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?
There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of exclusions. The description is purely a definition without any contextual usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch real-time DEX price, 24h volume, liquidity, and top pair stats across chains
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Token symbol or contract address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It states that the tool fetches real-time data points, implying a read-only operation, but it does not mention potential limitations, such as the ambiguity of token symbols or whether an exact contract address is required. Some details are missing but the core behavior is conveyed.
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 that lists the tool's primary outputs without any redundancy. It is front-loaded and immediately comprehensible, earning a high score for structure.
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 is simple with one parameter and no output schema. The description lists the intended outputs (price, volume, liquidity, pair stats), which gives a sufficient overview of what the tool returns. It lacks details on response structure or error handling, but for this level of complexity it is reasonably 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 input schema provides full coverage for the single parameter 'query' with the description 'Token symbol or contract address.' The tool description adds no additional meaning beyond what the schema already states, so the baseline 3 is appropriate.
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 the specific verb 'Fetch' and clearly identifies the resource: real-time DEX price, 24h volume, liquidity, and top pair stats across chains. It effectively distinguishes this tool from the unrelated sibling tools by focusing on DEX market data.
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 usage guidance is provided, such as when to use this tool versus alternatives. However, the purpose is clear enough that the usage is implied: when a user needs DEX token metrics. Since there are no closely related sibling tools, the lack of exclusions is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query global RDAP registry from edge for domain availability and WHOIS status
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Target domain name (e.g. example.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full behavioral burden. It discloses that the tool performs a query (read-only) and explains the data source (RDAP) and edge execution, but omits details such as return format, potential errors, or whether the data is live or cached. This is adequate but not rich.
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, tightly worded sentence with no filler words. It is front-loaded with the verb and resource, making it immediately scannable and 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?
For a simple tool with one parameter and no output schema, the description provides sufficient context: it states what it does, what data source it uses, and what information is returned (availability and WHOIS status). A slightly more detailed explanation of the output format would push it to a 5, but it is complete enough for this simplicity level.
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 input schema already provides 100% coverage for the only parameter, domain, with a clear example. The description adds no extra meaning to the parameter itself beyond the schema, only explaining the overall query scope. Thus, it meets the baseline but does not exceed it.
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 "Query" and identifies the resource "global RDAP registry" along with the intended outcome "domain availability and WHOIS status." This clearly distinguishes the tool from its unrelated siblings and fully clarifies its 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?
The description implies a clear usage context: it is for looking up domain registration data via RDAP. While no explicit alternatives or exclusions are listed, the sibling tools are entirely unrelated, so no points are deducted for missing comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates 1024-dimensional dense vector embeddings for multilingual & long text via BAAI BGE-Large
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description provides some behavioral context (dimensionality, model, multilingual/long support) but lacks details on output format, batch handling, or length 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, front-loaded, 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?
For a one-parameter tool with no output schema, the description is adequate but missing details like return structure and potential text length limits.
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%, and the description adds some meaning by indicating the text should be multilingual/long, but it does not explicitly describe the parameter's behavior or accepted formats beyond what the schema already shows.
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 generates 1024-dimensional dense vector embeddings, and specifies multilingual/long text and the BAAI BGE-Large model. This distinguishes it from the sibling embed_text tool.
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 the tool is for multilingual and long text but does not explicitly state when to prefer it over alternatives like embed_text, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates 768-dimensional dense vector embeddings for RAG & semantic search via BAAI BGE-Base
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only mentions the model and vector dimension, but does not describe output format, input constraints, or side effects. This is insufficient for a generation tool.
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, focused sentence that quickly conveys the core function and model. No filler or redundant 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 the lack of annotations and output schema, the description should explain the return format and batch behavior, but it does not. It also omits differentiation from embed_multilingual, making it incomplete for tool selection.
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 says nothing about the 'text' parameter or how to use it. The description fails to compensate for the low schema coverage, leaving parameter semantics entirely unexplained.
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 generates 768-dimensional dense vector embeddings and identifies the model (BAAI BGE-Base). However, it doesn't explicitly distinguish this from the sibling tool embed_multilingual, so it lacks explicit sibling differentiation.
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 mentions usage for RAG and semantic search, providing clear use context. But it does not state exclusions or compare with alternatives like embed_multilingual, so usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract clean text and table markdown from images via Workers AI Vision Llama 3.2
| Name | Required | Description | Default |
|---|---|---|---|
| imageUrl | Yes | Public image URL to parse |
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 the primary behavior (extracting text and tables) and the underlying model (Llama 3.2 via Workers AI), which gives some context. However, it does not mention limitations, failure modes, or output structure beyond 'clean text and table markdown', so transparency is partial.
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 a clear verb and object. Every word is informative, and there is no wasted text.
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 is simple with one parameter and no output schema, so the description should explain return values more fully. It mentions text and table markdown but not the exact format or limitations. The name 'shrink' is also left unexplained, creating a minor gap.
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 100%, so baseline is 3. The description does not add extra parameter details beyond the schema's clear definition of 'imageUrl'. It does not compensate with additional context, so a 3 is appropriate.
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 extracts clean text and table markdown from images, using a specific verb and resource. It distinguishes itself from sibling tools which focus on text, embeddings, or code, making its purpose unmistakable.
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 usage is implied by the description—it's for OCR on images—but no explicit 'when to use' or 'when not to use' is provided. There is no mention of alternatives or exclusions, so it lands at the 'implied usage' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compress raw HTML to clean markdown & headings for RAG context windows
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | Raw HTML content to parse |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of explaining behavior. It clearly states the transformation from HTML to markdown/headings, which is the core behavior. However, it does not disclose any limitations, error conditions, or performance characteristics, so it partially meets the need.
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 that is precise and free of filler. Every word contributes meaning: 'compress' indicates action, 'raw HTML' specifies input, 'clean markdown & headings' specifies output, and 'RAG context windows' clarifies purpose.
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 a simple signature (one input, no output schema), and the description adequately covers what, why, and the output format. It does not mention edge cases or failure modes, but for a straightforward transformation tool, this is sufficient. A score of 5 would require explicit handling of malformed HTML or size limits, which may be unnecessary here.
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 schema has one parameter 'html' with a clear description 'Raw HTML content to parse', which covers 100% of parameters. The description adds no additional detail about the parameter beyond what the schema provides, so the baseline of 3 applies.
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 'compress' (transform), the resource 'raw HTML', and the output 'clean markdown & headings' for the specific use case of 'RAG context windows'. This distinguishes it from sibling tools like image_ocr_shrink or code_denoise, which have different input types and purposes.
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: it is for converting raw HTML to markdown/headings for RAG context windows. It does not explicitly state alternatives or when not to use it, but the purpose is evident, earning a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch web pages from Cloudflare edge bypassing simple IP blocks
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target URL to fetch |
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 the 'bypassing simple IP blocks' behavior, but omits critical details such as whether the operation is read-only, potential rate limits, legal/ethical implications, or failure modes. This lack of transparency could lead an agent to misuse the tool.
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 that front-loads the verb and includes the key qualifier. Every word earns its place, with no redundancy or fluff.
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 is simple with one parameter, but with no output schema and no annotations, the description does not explain the return format or any prerequisites/constraints. The core purpose is clear, but an agent may need more information to invoke it confidently beyond a basic fetch.
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 schema already provides 100% coverage for the single parameter ('Target URL to fetch'). The description adds no further parameter meaning, so it meets the baseline for high schema coverage.
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 identifies the action ('Fetch web pages') and the distinguishing characteristic ('from Cloudflare edge bypassing simple IP blocks'). It differentiates itself from sibling tools by specifying this niche, making it unambiguous what the tool does.
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 gives clear context for use: fetching web pages that are IP-blocked by Cloudflare. It does not explicitly state when not to use it or name alternatives, but the context is sufficient to infer appropriate usage, especially given no sibling performs a similar function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Executive TL;DR text summarizer producing structured bullet points via Workers AI Llama 3.1
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Source text payload to summarize | |
| format | No | Summary output format style | |
| maxLength | No | Target word count limit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full transparency burden. It discloses the use of Workers AI Llama 3.1, but fails to mention that the output format can vary (bullets, paragraph, executive) or any limitations, making the behavior only partially transparent.
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 that efficiently conveys the core purpose. There is no wasted wording, though some details are omitted for the sake of 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?
With no annotations and no output schema, the description must provide more context. It fails to mention the supported output formats, which are critical for correct tool invocation, and does not explain return value structure or limitations.
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 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it does not clarify how 'format' or 'maxLength' interact with the summarization process.
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 identifies the tool as a text summarizer, distinguishing it from sibling tools that handle other tasks. However, it states 'producing structured bullet points' while the schema allows 'paragraph' and 'executive' formats, which is somewhat misleading about the full capability.
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?
There is no guidance on when to use this tool versus alternatives, no mention of prerequisites, and no exclusions. The one-sentence description does not help an agent decide between this and sibling tools like rag_shrink or code_denoise.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze market & social sentiment for topics/tokens using Workers AI Llama 3.1
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Topic, ticker, or text sample to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It states the tool analyzes sentiment but does not describe what the output looks like, whether it has side effects, or any rate limits/authentication requirements. This is a minimal level of transparency, similar to the 'update_drive' example.
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 redundant words. It front-loads the core action ('Analyze') and resource ('market & social sentiment') and adds the model for extra context. 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's simplicity (one parameter, no output schema), the description is passable but has a clear gap: it never explains the return value or format. The agent is left unsure whether the result is a score, classification, or text. With no output schema and no annotations, the description should at least mention what the analysis yields.
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 input schema already provides full coverage of the only parameter 'topic' with a clear description ('Topic, ticker, or text sample to analyze'). The description adds context about sentiment and the model, but it does not elaborate further on the parameter's semantics or acceptable formats. This meets the baseline of 3 for high schema coverage.
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 'Analyze' with a clear resource ('market & social sentiment for topics/tokens') and even identifies the model ('Workers AI Llama 3.1'). This makes the tool's purpose immediately clear and distinct from its siblings, which are focused on other domains like domain checking, price summaries, or text embedding.
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 no guidance on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or comparison to siblings like summarize_text or dex_price_summary. The only implied usage is from the tool name and the verb 'analyze', but no explicit context or conditional instructions are given.
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.
v1.2.0airgap_scrubcode_denoisedex_price_summarydomain_checkembed_multilingualembed_textimage_ocr_shrinkrag_shrinkstealth_domsummarize_textx_sentimentScored across 11 tools
Most tools target distinct tasks (scraping, redaction, compression, sentiment, embeddings), and descriptions clarify their purposes. The only near-overlap is between embed_text and embed_multilingual, but their language/vector-size differences make them distinguishable.
Tool names mix verb-noun patterns (embed_text, summarize_text), noun-compounds (dex_price_summary, x_sentiment), and adjective-noun/cryptic forms (stealth_dom, rag_shrink, airgap_scrub). No consistent naming convention is used across the set.
With 11 tools, the count is within a reasonable range and not excessive. However, the tools span diverse domains (web/data, cleaning, AI analysis), giving the set a somewhat scattershot feel rather than a focused toolkit.
There is no clear domain or lifecycle model; the tools are a random assortment of utilities. Obvious operations are missing (e.g., search, translation, storage) that would make workflows coherent, and the breadth of unrelated features prevents a sense of complete coverage.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Pay-per-call (x402/USDC-Base) web + crypto data tools for AI agents: audit, extract, crypto, DeFi.
Pay-per-use web extract, token prices, and wallet balances via x402 USDC micropayments.
Production-grade MCP gateway delivering 8 real-time AI tools with instant x402 micropayments settled in USDC on Base Mainnet or SPL-USDC on Solana. Features Basescan contract auditing, wallet analytics, headless browser scraping, and pre-scraped oracle data feeds.
Production-grade MCP gateway delivering 8 real-time AI tools with instant x402 micropayments settled in USDC on Base Mainnet or SPL-USDC on Solana. Features Basescan contract auditing, wallet analytics, headless browser scraping, and pre-scraped oracle data feeds.
