SursaTech (Sursa Technology Private Limited) is an AI-native product engineering and consulting company founded in 2017 in Kathmandu, Nepal, building production AI agents, RAG and knowledge systems, AI-enabled SaaS products, automation and integrations, and QA leadership for clients in Nepal, Japan, Norway, the United States and the United Kingdom, and operating its own products (Humafu, AcademyKit, Buy Me a Momo). Its public machine surface is an A2A 1.0 agent — the SursaTech AI Advisor — served as a conformant agent card at /.well-known/agent-card.json with a JSON-RPC endpoint at api.sursatech.com/api/a2a: ten skills covering company knowledge (profile, services, portfolio, process, pricing), lead-requirement capture, tentative project estimates and guarded consultation booking, payment and cancel/reschedule flows. Any agent can self-register anonymously for an opaque bearer token; the provider publishes RFC 8414/OIDC authorization-server metadata, RFC 9728 protected-resource metadata, an RFC 9727 api-catalog, an agentskills.io skill index and an llms.txt, but no OpenAPI, SDK, MCP server, status page, pricing for the API, or legal pages.
SursaTech is listed with 1 API on the APIs.io network. Tagged areas include AI Agents, A2A, AI Consulting, Product Engineering, and Software Development.
SursaTech’s developer surface includes pricing, documentation, authentication, and 26 more developer resources.
Regulatory Posture applies to this provider. Its tags matched the
Horizontal (data, software, accessibility, platform) regime, so
Regulatory Posture carries 15 points of the composite.
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applicability map is public and we will correct it.
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for
this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero:
never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and
several others — scorable at all.
The six quality facets above are damped to 85 points between them,
because the conditional facet above carries the other
15. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 85% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
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Agent readiness — checks not met, largest first (exact, from this profile):
Idempotency+9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Request/Response Examples+7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
Delegated User Identity+6 pts — a served discovery document listing authorization_code in grant_types_supported (served), else an oauth2 authorizationCode flow or openIdConnect declared in an OpenAPI (documented)
5 more unmet checks in the Agent Readiness table above.
Kin Score — facets with the most composite points still available (weight × headroom):
The Kin Score Market layer reads a provider inside its market:
its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted:
not part of the Kin Score or agent readiness.
Registered MCP server and declares compatibility are report only — not weighted.
Undeclared means we found nothing we can read, so there is no number: we never infer integrations.
Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.
An A2A 1.0 agent (JSON-RPC 2.0 over HTTP POST, streaming declared) exposing ten skills: get_company_profile, get_services, get_portfolio_projects, get_development_process, estim...
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