x402_api_exa_ai_search_35[PAID inbound $0.0077 → then proxy $0.0077] Exa /search endpoint Upstream: https://api.exa.ai/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 that this is a paid operation, names the upstream endpoint, and specifies POST, which adds useful behavioral context. However, with no annotations present, it leaves important behavioral details unstated such as authentication expectations, response behavior, failure modes, and whether any user-visible side effects occur.
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 compact and all elements add value: cost, endpoint, upstream URL, and HTTP method. It is not bloated, though it reads more like a machine-generated proxy label than a human-friendly explanation.
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, lack of output schema, and a generic input schema, the description does too little to explain how the Exa search request should be formed or what the caller can expect in return. It identifies the upstream but leaves critical invocation and response details to inference.
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 descriptions for the two generic parameters, body and query, so the baseline is 3. The description does not add Exa-specific parameter semantics or examples, but the stated POST method does help reinforce that the body parameter may be the appropriate place for the Exa search payload.
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 a specific resource (Exa /search endpoint), the upstream URL, and the HTTP method (POST). It is easy to understand what the tool does at a high level, though it does not differentiate itself from the sibling x402_stableenrich_dev_api_exa_search_39, which appears to target the same endpoint.
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 about when to use this tool versus any sibling, including the other Exa search wrapper. It does not state use cases, exclusions, or alternative tools, so the agent must infer usage entirely from the name and endpoint.
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.