FREE. Fetch a PulseNetwork service's OpenAPI spec and return a condensed view (title, description, every path with method, price, and summary) plus the URL of the full spec. Use after catalog_search to see exact request/response shapes. No wallet or payment needed.
| Name | Required | Description | Default |
|---|---|---|---|
| vertical | Yes | Vertical id from discover/catalog_search, e.g. "onchainpulse" or "travelpulse" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool is FREE, requires no wallet/payment, and details the return structure (title, description, paths with method/price/summary, and full spec URL). This is strong behavioral disclosure for a read-only spec fetcher.
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, front-loaded with 'FREE' and the action. Every word adds value, with no redundancy or irrelevant 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?
For a simple one-parameter tool with no output schema and no annotations, the description provides adequate context: purpose, use case, return format, and cost. It is sufficiently complete, though it could briefly mention potential errors 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?
The schema already covers the single 'vertical' parameter fully with examples and source. The description adds context by mentioning 'Use after catalog_search', but does not introduce new parameter semantics beyond what the schema provides. 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 clearly states the tool fetches a PulseNetwork service's OpenAPI spec and returns a condensed view plus the full spec URL. It distinguishes itself from sibling service-specific tools by focusing on spec retrieval rather than the services themselves.
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 after catalog_search to see exact request/response shapes. It gives clear context but does not mention when not to use or alternative tools, leaving a minor gap.
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.
Each tool has a unique domain prefix (e.g., airdroppulse, alphapulse, arbipulse) making them mostly distinguishable at a glance. A few adjacent verticals like careerpulse vs talentpulse or marketpulse vs dealpulse have overlapping themes, but their descriptions clarify the distinct focus. The utility tools (catalog_search, discover, get_openapi_spec, x402_troubleshoot) are also clearly distinct in role. However, the sheer number of similar 'pulse' names could still cause misselection without reading descriptions.
The dominant naming convention is `<domain>pulse` (e.g., climatepulse, cryptopulse, edupulse), which is highly consistent and predictable. Exceptions like catalog_search, discover, get_openapi_spec, x402_troubleshoot, and stateedge break the pattern, but these are few and serve obvious utility purposes. Overall, the convention is clear and easily learnable.
With 80 tools, the server presents an extremely large and potentially overwhelming surface. While each tool represents a distinct intelligence vertical and navigation aids exist (catalog_search, discover, get_openapi_spec), the count far exceeds the typical 3-15 range for coherent agent use and even the 'heavy' 16-25 range. The burden of selecting the correct vertical from 80 options is significant, despite clear naming.
The server offers an exceptionally broad and deep coverage of domains, from finance and health to agriculture and gaming. Each vertical includes multiple endpoints that address core operations for its domain, such as search, analysis, comparisons, deterministic checks, and even action-oriented tools like letter generators and physical mail. The presence of free discovery and troubleshooting tools fills potential gaps, leaving no obvious dead ends in the overall tool surface.