AI Readiness AuditRef. APM-15C0382D · Confidential
Recover for Institutions API
An independent assessment of whether the Recover for Institutions API specification is ready for AI-assisted integration and autonomous agents, prepared for Coincover.
89/100
AI-ready
Ready for AI-assisted builds.
Ready for autonomous agents.
Ready for autonomous agents.
Resolve all findings (through Phase 1) → score reaches 100 · AI-ready
F
D
C
B
A
89
055708085100
F · Not readyD · Significant gapsC · Needs workB · Mostly readyA · AI-ready
0
Critical blockers : no findings stop an operation group outright
150
Total instances: 0 critical, 0 high, 47 medium, 103 low
15
Rule groups across 4 pillars: 0 critical, 0 high sev. instances
Executive summary
Recover for Institutions API scores 89.2/100: AI-ready · Grade A. The weakest pillar is Documentation readiness for AI at 81; the strongest is Standards compliance at 97. We recorded 150 finding instances: 0 critical, 0 high, 47 medium, and 103 low. Ready for AI-assisted builds. Ready for autonomous agents. Every finding lives in the specification, and the roadmap in this report orders the fixes by impact; resolving them projects the score to 100/100.
Readiness by pillar
AI-ready
89 / 100
FoundationThe spec itself
97/100
Standards Compliance
OpenAPI validity, structural correctness, specification conformance, and import linting: can machines trust your spec?
97AI-ready
Build Time ReadinessAI writes the code
86/100
Documentation Readiness for AI
Descriptions that say when to use an operation, examples that pin down every shape: whether an AI can learn the truth of your API from what you publish
81Mostly ready
SDK Readiness for AI
Clean types, predictable naming, correct formats: whether production-grade client code can be generated from your spec, by SDK pipelines and AI coding assistants alike
90AI-ready
Runtime ReadinessAgents make the calls
85/100
MCP Readiness
Whether your operations survive being turned into tools: distinct names, descriptions that carry what an agent must know, safe invocation semantics, auth, error recovery, and context budget
85AI-ready
Score projection · what fixing each phase unlocks
Todaycurrent score
Phase 1fix mediums & lows
About this report. We evaluated the Recover for Institutions API specification against thousands of deterministic rules we have refined over twelve years of building API tooling, supplemented by AI-based analysis. Every finding is reproducible from the spec itself, scored consistently, and paired with a concrete fix.
What's at stake
Your readiness posture is strong
All 4 readiness pillars are managed. These are the failure modes your spec quality protects you from, and the ones to watch as agent adoption grows.
Foundation
The spec itself
97 / 100
Managed · Standards compliance
Minor spec violations affect integrations
We found a few instances where spec compliance could improve, but overall, the API performs well.
22→29
code quality score out of 40: the lift consumers achieve when the underlying spec is clean and standards-compliant
APIMatic Context Plugins research · Series B · June 2026
Build Time Readiness
AI writes the code
86 / 100
Managed · Documentation readiness for AI
AI readers struggle with sparse examples
You maintain an 81/100 documentation readiness score, but improving detail will assist AI readers in understanding the API better.
74%
of developers name missing or unclear documentation as the top reason they abandon an API. AI readers are stricter: what a human would ask about, an AI assumes.
APIMatic Developer Experience research · June 2026
Managed · SDK readiness for AI
SDKs generate incorrect types and formats
You should refine the schema layer to ensure accurate integration code for the payments API.
0
fabricated API constructs when a coding assistant was grounded in an AI-ready spec. Ambiguity in the spec is what triggers hallucinated integration code
APIMatic Context Plugins research · 6 APIs · 3 models · June 2026
Runtime Readiness
Agents make the calls
85 / 100
Managed · MCP readiness
Autonomous Agents Need Minor Adjustments
You have a solid MCP readiness score, but agents struggle with tool names and context budgets. Addressing these issues will improve execution safety.
65%
reduction in token consumption when agent tools are grounded in authoritative API context. Bloated or confusable tools consume up to 3.3× more.
APIMatic Context Plugins research · Series A · June 2026
The evidence numbers come from our controlled research: 6 experiments across 3 models, 6 commercial APIs, and 3 languages. We derive each card's risk status from this API's pillar scores. Full methodology at apimatic.io.
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