Fenris MCP Server

Fenris provides real-time insurance data and predictive AI solutions that power quoting, risk assessment, and personalized customer experiences. Their MCP platform offers prefill, predictive scoring, and data enrichment across auto, property, and commercial insurance, serving millions of transactions annually. The service integrates via RESTful APIs, delivering sub‑second response times, SOC 2 compliance, and enterprise‑grade security.

Fenris MCP Server is listed with 1 API on the APIs.io network. Tagged areas include Company, Insurance, Data, and Artificial Intelligence.

Fenris MCP Server’s developer surface includes engineering blog, documentation, and 8 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

7.7/100 minimal ▬ flat Agent 0/100 human only Heartbeat 100/100 alive Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

1 API

Company Insurance Data Artificial Intelligence

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 7.7/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 1.0 / 17

Access Clarity 1.8 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.8 / 9

Regulatory Posture 1.2 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Insurance regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the 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):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 6.0 — up to +18.8 composite points
Access Clarity scores 10.5 — up to +17.9 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/fenrisd/apis.yml: open an issue on github.com/api-evangelist/fenrisd to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Fenris MCP API

Real-time insurance data and predictive AI via the Fenris MCP platform. Provides prefill, scoring, and data enrichment APIs.

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Fenrisd Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 3

The organization behind the API

Newsroom

Newsroom

Blog

Blog

Website

Website

Other 2

Properties that don't map to a standard resource type

Hosts

Hosts

Vendors

Vendors

Source (apis.yml)

aid: fenrisd
name: Fenris MCP Server
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  probe_state: unreadable
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://fenrisd.com/wp-content/uploads/2026/08/Embed_Fenris_Logo.png
description: Fenris provides real-time insurance data and predictive AI solutions that power quoting, risk assessment, and
  personalized customer experiences. Their MCP platform offers prefill, predictive scoring, and data enrichment across auto,
  property, and commercial insurance, serving millions of transactions annually. The service integrates via RESTful APIs,
  delivering sub‑second response times, SOC 2 compliance, and enterprise‑grade security.
url: https://raw.githubusercontent.com/api-evangelist/fenrisd/refs/heads/main/apis.yml
x-type: company
x-source: harvest:g2-catalog
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-16 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-16'
modified: '2026-10-16'
tags:
- Company
- Insurance
- Data
- Artificial Intelligence
tags_raw:
- Company
- Insurance
- Data
- AI
- API
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: LLMsTxt
  url: llms/fenrisd-llms.txt
- type: Hosts
  url: hosts/fenrisd-hosts.yml
- type: Vendors
  url: vendors/fenrisd-vendors.yml
- type: TermsOfService
  url: https://fenrisd.com/terms-and-conditions/
- type: PrivacyPolicy
  url: https://fenrisd.com/privacy-policy/
- type: Newsroom
  url: https://fenrisd.com/category/news/
- type: Blog
  url: https://fenrisd.com/category/blog/
- type: Documentation
  url: https://dev.fenrisd.com/
- type: DomainSecurity
  url: security/fenrisd-domain-security.yml
- type: Website
  url: https://fenrisd.com/
apis:
- name: Fenris MCP API
  description: Real-time insurance data and predictive AI via the Fenris MCP platform. Provides prefill, scoring, and data
    enrichment APIs.
  humanURL: https://dev.fenrisd.com/
  aid: fenrisd:fenris-mcp-api
x-coverage:
  state: unreadable
  reason: no-machine-readable-spec
  detail: Documentation is publicly available at the developer portal but no machine‑readable OpenAPI or other contract was
    found.
  evidence:
  - url: https://dev.fenrisd.com/
    status: 200
  checked: '2026-10-16'
x-enrichment:
  date: '2026-10-16'
  status: enriched
  artifacts_added: 7
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

Fenris MCP Server API — frequently asked questions

Does Fenris MCP Server have an API?

Yes. APIs.io lists 1 API from Fenris MCP Server, including Fenris MCP API.

Is the Fenris MCP Server API ready for AI agents?

Fenris MCP Server has a Kin Score of 7.7 out of 100 (minimal) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-11-06.

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Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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