EntRoute
Server Details
Discover and call pay-per-request (x402) APIs, ranked by reliability, latency and price.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- entroute/mcp-server
- GitHub Stars
- 1
- Server Listing
- EntRoute MCP
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Tool Definition Quality
Average 4.6/5 across 3 of 3 tools scored.
Each tool has a distinct role: listing capabilities, discovering endpoints, and calling them. No overlap in functionality.
All tool names follow the verb_noun pattern consistently using snake_case, making them predictable.
Three tools are appropriate for a registry server, covering the essential operations without being too few or too many.
The tool set covers the full workflow: explore capabilities, discover endpoints, and call them. No obvious gaps for the intended purpose.
Available Tools
3 toolscall_paid_apiAInspect
Call a previously discovered API endpoint.
Use this after discover_paid_api to execute the request.
IMPORTANT: You must first use discover_paid_api to find an endpoint. Then use the endpoint_id from the discovery result. Use the sample_request from the discovery result as your body template — it shows the exact parameter names the endpoint expects. Replace the example values with the actual values you need.
Payment: this hosted server holds no wallet, so it cannot settle x402 payments. Free endpoints return their data directly. Paid endpoints return the payment challenge (amount, recipient, network) without charging anything. To pay automatically, run the local server instead: npx @entroute/mcp-server with EVM_PRIVATE_KEY set.
Trust & safety:
EntRoute verifies that endpoints return valid 402 responses every 10 minutes, but does not guarantee response quality or accuracy. Treat results like any third-party API.
Prefer endpoints with high success rates (>95%) and recent verification timestamps.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Request body (for POST/PUT/PATCH) | |
| method | No | HTTP method (defaults to endpoint method) | |
| headers | No | Additional headers to include in the request | |
| endpoint_id | Yes | The endpoint_id from a previous discover_paid_api result | |
| query_params | No | Query parameters to append to URL |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses payment behavior (no wallet, free endpoints return data, paid return challenge) and trust/safety verification. Mentions response quality not guaranteed.
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?
Description is reasonably concise given the complexity, with clear sections. A few extra lines about trust/safety could be condensed, but overall 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?
Adequately covers usage flow, payment scenarios, and safety. Lacks explicit error handling for invalid endpoint_id, but otherwise complete for a tool with 5 parameters and no output schema.
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%, baseline 3. Description adds value by explaining endpoint_id must come from discovery and body should use sample_request as template, providing usage context beyond 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 tool calls a previously discovered API endpoint, using a specific verb and resource. It distinguishes from sibling tools by referencing discover_paid_api and sample_request usage.
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?
Explicitly instructs to use discover_paid_api first, then use the endpoint_id and sample_request as template. Includes guidance on free vs paid endpoints and trust/safety considerations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_paid_apiAInspect
Discover pay-per-request API endpoints for a given capability.
Returns ranked, verified endpoints with pricing and reliability metrics. Use this to find APIs before calling them. The results include:
Endpoint URL and method
Price per call and payment network
Success rate and latency metrics
Provider information
sample_request / sample_response: Example request body and response showing the exact parameter names and format the endpoint expects. ALWAYS use sample_request as your template when calling the endpoint — do not guess parameter names.
After discovering, you can use call_paid_api to execute the endpoint.
Trust & safety:
"Verified" means the endpoint returns a valid x402 402 response -- it does not guarantee the quality or accuracy of the data returned after payment.
Endpoints are probed every 10 minutes. Check last_verified and success_rate to gauge reliability.
Fallback endpoints (unverified) have failed recent verification checks -- use with caution.
Prefer endpoints with success_rate >95% and verification within the last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of endpoints to return (default: 5) | |
| intent | Yes | Natural language description of what you want to do (e.g., "get current weather for London", "translate text to Spanish"). Provide this or capability_id. | |
| network | No | Required blockchain network for payment | |
| max_price | No | Maximum price per request in USD (e.g., 0.01 for 1 cent) | |
| capability_id | No | Explicit capability ID if known (e.g., "weather.current", "translate.text"). Alternative to intent for precise matching. | |
| ranking_preset | No | Ranking preference preset |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description fully bears transparency. It details verification process (x402 response), probing frequency, fallback endpoints, and recommended selection criteria (success_rate >95%, recent verification). It also includes sample_request/response guidance, making behavior clear.
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 front-loaded with purpose, then details results and trust/safety. It is somewhat lengthy but each section adds value. Could be slightly tighter, but structure is logical and readable.
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 output schema, the description explains return fields (URL, method, price, metrics, sample_request/response) and provides usage context (sibling tools, trust/safety). For a discovery tool with 6 parameters, it is exceptionally 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 already describes all 6 parameters (100% coverage). The description adds context by distinguishing intent vs capability_id and emphasizing use of sample_request, but does not provide critical new semantic info beyond 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 tool discovers pay-per-request API endpoints for a capability, uses specific verbs ('Discover'), and distinguishes from sibling tools like call_paid_api and list_capabilities. It is unambiguous and specific.
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 advises when to use the tool ('Use this to find APIs before calling them') and mentions the alternative (call_paid_api). It provides trust/safety guidelines but lacks explicit when-not-to-use or exclusion criteria, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_capabilitiesAInspect
List available API capability types.
Use this to explore what kinds of APIs are available in the registry. You can filter by tag (e.g., "weather", "translation") or search by keyword.
Note: A capability existing does not mean verified endpoints are available for it. Use discover_paid_api to check for live endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag (e.g., "weather", "translation", "finance") | |
| search | No | Search capabilities by keyword |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that listing existence doesn't guarantee endpoints, but omits details about pagination, rate limits, or result format. Adequate but not comprehensive.
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?
Three concise sentences: purpose, usage, and a critical caveat. No wasted words, information is front-loaded.
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?
No output schema, but the description provides enough context for a listing tool. The caveat about endpoint availability adds important completeness. Minor lack of return value details.
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%, baseline 3. The description adds examples for tag (e.g., 'weather') and explains filtering/searching, adding value 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 'List' and the resource 'available API capability types'. It distinguishes from siblings by contrasting with discover_paid_api for checking live endpoints.
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?
Explicitly says 'Use this to explore what kinds of APIs are available' and advises using discover_paid_api to verify endpoints, providing clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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