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Saylor Innovations: pay-per-call data

Search all paid API endpoints

search_endpoints
Read-onlyIdempotent

Search the full Saylor catalog of pay-per-call data endpoints (x402, no account or API key). Solana token rug risk and holder concentration, token prices on 25 chains, ERC-20 balances, academic papers, U.S. case law/statutes/regulations, medical and HCPCS codes, drug facts, protein function (UniProt), AlphaFold and PDB structures, ClinVar variant significance, Reactome pathways, rare diseases (Orphanet), CVE/exploit intel, Polymarket/Kalshi odds, perp funding rates, stocks and SEC data, web page to markdown, sanctions screening, dictionary, chemistry, books, Bible/Quran, live aircraft, museum art, crypto token/wallet/DeFi data and more. Free. Returns url, example call, a free sample response URL, price and description; then call get_endpoint for payment details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYesWhat you need, e.g. "icd-10 code", "case law", "token price", "arxiv".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context: cost (free), auth requirements (x402, no account/key), and return fields plus the next step, though it omits pagination and rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads purpose and return information, but the middle catalog list is very long. While each domain adds coverage detail, the list could be trimmed without losing the core message.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description explains exactly what is returned (url, example call, sample response URL, price, description) and the next step (get_endpoint). Annotations cover safety, so the definition is complete for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%: the 'query' parameter is documented in the schema, but 'limit' is not. The description's long list of domains indirectly hints at query content but adds no syntax or limit guidance, so it does not compensate for the gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search) and resource (Saylor catalog of pay-per-call data endpoints), and distinguishes itself from get_endpoint by noting the follow-up call. The catalog listing makes the scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes its role in the workflow ('then call get_endpoint for payment details'), but does not explicitly compare to sibling list_data_endpoints or list_categories, leaving the agent to infer when to search versus list all.

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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