x402_stableenrich_dev_api_exa_search_39[PAID inbound $0.01 → then proxy $0.0110] Exa Search - Neural search across the web Upstream: https://stableenrich.dev/api/exa/search Method: POST
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Optional JSON body for non-GET requests | |
| query | No | Optional query string parameters for the upstream HTTP request |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses cost ('PAID inbound $0.01 → then proxy $0.0110'), indicates it is a POST request, and mentions an upstream URL, which gives some insight into its operation. However, it does not describe the return format, side effects, authentication requirements, or rate limits. Since no annotations are provided, this partial disclosure is insufficient for full 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 relatively concise, containing the core purpose, cost, and method in a single sentence. However, the structure is somewhat cluttered by mixing pricing notation, tool type, and technical details without clear separation. It is efficient overall but not impeccably organized.
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 description fails to provide essential context for using the tool effectively. It omits expected output (search results format), any required input parameters beyond generic body/query, and error handling or edge cases. This leaves the agent without sufficient information to invoke the tool confidently.
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 describes 'body' and 'query' only as generic optional JSON and query string parameters, repeating the schema structure without explaining what data they should contain or how they relate to the search functionality. The description does not elaborate on parameter usage, making them semantically empty.
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 is an 'Exa Search' tool for 'neural search across the web', providing a specific verb and resource. This distinguishes it from other search tools in the sibling list, such as 'search_bazaar' or generic weather tools, by explicitly describing the neural search 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 explicit guidance on when to use this tool over alternatives like other search tools (e.g., 'search_bazaar' or 'x402_api_exa_ai_search_35'). The description does not mention any conditions, use cases, or reasons to prefer this tool, leaving the agent to infer when it is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The core dispatcher tools are distinct, but the 84 auto-discovered wrappers contain dozens of near-identical news, weather, and translation endpoints. An agent cannot reliably tell many of these apart without reading provider-specific details, and misselection is likely.
Core tools use a readable verb_noun pattern, but most tools are machine-generated `x402_<domain>_<path>_<number>` names with opaque numeric suffixes. The two naming schemes are mixed, and the generated names are not intuitive.
95 tools is an extreme mismatch for a coherent MCP surface. The bulk are auto-registered third-party proxies that duplicate the same few categories, creating massive prompt bloat and selection overhead.
For an x402 dispatcher, the core lifecycle is well covered: discovery, search, quote, route-and-call, stats, paywall status, and cashflow/P&L. Minor gaps such as no deregistration or detailed per-transaction drill-down exist, but main workflows do not dead-end.