How to Publish a Dedicated MCP Integration Reference for Enterprises

If your enterprise deals are stalling in security review because a CISO asked how your AI agent integrations handle token passthrough and you couldn't point to a single public document, this is the playbook to fix it.

When you tell a Chief Information Security Officer (CISO) that your platform is "AI-ready," they do not hear a value proposition. They hear an unquantified security risk. Enterprise buyers will not connect their internal AI agents to your platform based on a marketing page. They require technical proof that your infrastructure can handle autonomous tool calling without exposing their entire database to a prompt injection attack.

The shift happened faster than most product teams realized. A May 2026 CTO survey found that 78% of surveyed enterprises now have the Model Context Protocol (MCP) in production, and 67% of CTOs named MCP their default integration standard for the next 12 months. A separate enterprise deployment study reported that 28% of Fortune 500 companies have implemented MCP for production AI workflows in under 18 months. These companies are building custom LangGraph agents, deploying Claude Desktop across their workforce, and integrating ChatGPT Enterprise into their daily operations. They need these agents to read and write data to your B2B SaaS platform.

The protocol is no longer optional, and neither is the documentation that proves your implementation is safe to plug in. This guide breaks down exactly what enterprise procurement teams are looking for, the architectural security requirements you must document, and how to structure your public-facing MCP reference to bypass security reviews and accelerate your sales cycle.

The Procurement Reality: Why "AI-Ready" Fails Security Reviews

A dedicated MCP integration reference is a public, versioned document that describes exactly how AI agents authenticate to your platform, which tools they can invoke, what data flows through the connection, and how your server behaves under failure conditions. It is not a blog post. It is not a Loom demo. It is structured technical content that a security reviewer can map to their internal checklist.

The reason it has become non-negotiable: the same CTO survey above identified machine identity, gateway security, and token passthrough as the dominant blockers to enterprise MCP adoption. Procurement is not delayed by feature gaps anymore. It is delayed by the absence of credible answers to security questions.

There is also a positioning angle that competitors are exploiting. Kong is loudly positioning itself as the unified AI control plane for enterprise AI traffic, while security vendors like SentinelOne and Sysdig are publishing detailed write-ups on MCP risks. If you do not publish a counter-narrative grounded in your own architecture, the buyer will internalize someone else's framing of what an AI-ready integration looks like.

AI Agent Integration Enterprise Buyer Requirements

Enterprise procurement teams evaluate MCP integrations against a remarkably consistent checklist. Your dedicated MCP integration reference must explicitly address these core requirements without forcing the buyer to ask. Do not bury this information in a general API reference. Create a dedicated "AI Agent Security & Architecture" page that covers the following pillars:

• Tenant isolation model. Is each MCP server scoped to a single connected account, or does a single token grant cross-tenant visibility?
• Credential handling. Where are upstream OAuth refresh tokens stored, who can read them, and how are they rotated?
• Zero Data Retention. Does the MCP server cache responses, log payloads, or persist tool inputs and outputs? The gold standard is a strict pass-through architecture where payloads are proxied directly to the underlying integration and responses return to the client un-persisted.
• Audit trail. Can the customer pull an audit log of every tool invocation, with timestamps, tool names, and request IDs?
• Authentication layers. Is the MCP URL alone sufficient to call tools, or is a second factor (API token, mTLS, SSO session) required?
• Tool scope controls. Autonomous agents hallucinate. Can the customer restrict an MCP server to read-only (get and list), completely removing create, update, or delete tools from the LLM's context window?
• Expiration. Can servers be issued with a fixed Time-To-Live (TTL) or expires_at parameter for contractors, agents, or short-lived workflows?
• Failure modes. What happens when the upstream API returns 429, 5xx, or a malformed payload?

The last point is where most public MCP documentation falls apart. Vendors describe the happy path and stay silent on rate limits, partial failures, and upstream outages. A buyer's risk model lives in the failure cases, so that is where your reference needs to be most precise. See the 2026 MCP buyer's checklist for the full procurement matrix.

Addressing MCP Server Enterprise Security Requirements

Security is the primary reason MCP server platforms face procurement friction. As SentinelOne highlighted, a single breached MCP server without authentication controls can expose an entire organization's integrated databases. Your integration reference must include a dedicated security addendum detailing your architectural safeguards.

Defeating Credential Aggregation and the Blast Radius Problem

Many naive MCP implementations aggregate API keys in a single centralized server configuration. This creates a massive honeypot. If the server is compromised, every connected integration is exposed. Your reference needs to make three things explicit:

1. Scope per server. Document that each MCP server URL is bound to one tenant's one connected account, not a multi-tenant fleet.
2. Token storage at rest. State whether raw tokens are stored or only cryptographic hashes. Detail how your architecture stores tokens securely (e.g., hashed in a fast Key-Value store) and how the MCP server URL itself acts as a cryptographic token mapping to a specific, isolated tenant connection.
3. One-time disclosure. Confirm that the raw URL/token is shown exactly once at creation and never retrievable from logs or admin UIs.

Token Passthrough and Dual Authentication

The most common procurement objection is "the URL is the credential." By default, possessing an MCP server URL grants access to its tools. For enterprise deployments, this is insufficient. A clean answer is to document an optional second authentication layer.

Explain how your platform supports an "API Token Required" flag. When enabled, the MCP client (whether it is Claude Desktop or a custom agent) cannot just connect to the URL - it must also pass a valid API token in the Authorization header.

Show the exact request shape in your reference:

POST /mcp/<server-token> HTTP/1.1
Authorization: Bearer <tenant-api-token>
Content-Type: application/json
 
{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{...}}

With that pattern documented, leaking the URL into a config file or log no longer constitutes a usable credential. You can visualize this dual-auth flow to build immediate technical credibility:

sequenceDiagram
    participant Agent as AI Agent (Claude/Custom)
    participant MCP as MCP Server Router
    participant KV as Token Store
    participant Proxy as Proxy API Layer
    participant SaaS as Target SaaS API

    Agent->>MCP: POST /mcp/:token<br>Header: Authorization Bearer :api_key
    MCP->>KV: Hash URL token & validate expiry
    KV-->>MCP: Validation successful
    MCP->>MCP: Validate API Key (Dual Auth)
    MCP->>Proxy: Execute Tool (e.g., list_contacts)
    Proxy->>SaaS: Proxied Request with OAuth Credentials
    SaaS-->>Proxy: Raw API Response
    Proxy-->>MCP: Normalized Response
    MCP-->>Agent: JSON-RPC Tool Result

Transparent Rate Limit Handling and the 429 Contract

AI agents are notorious for hitting rate limits. They operate at machine speed and will rapidly iterate through pagination cursors until they exhaust a quota. A surprising amount of enterprise security review focuses on rate limits because they are the most common cause of cascading failures during AI agent bursts.

Your documentation must be radically honest about how you handle rate limits. Do not claim to magically absorb or silently retry rate limit errors. If you hide HTTP 429 errors from the LLM, the agent will assume the tool failed for a functional reason and will likely hallucinate a workaround, causing infinite loops.

The honest contract to publish is this: when the upstream provider returns HTTP 429, the MCP server passes that error directly to the calling agent along with normalized rate limit headers, leaving retry decisions to the client. State clearly that you normalize upstream rate limit information into standard IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset).

Document the behavior, and recommend a client-side exponential backoff pattern with jitter to prove to enterprise engineers that they can build predictable workflows:

async function callTool(req: ToolRequest, attempt = 0): Promise<ToolResult> {
  const res = await fetch(mcpUrl, { method: 'POST', body: JSON.stringify(req) });
  if (res.status !== 429) return res.json();
 
  const reset = Number(res.headers.get('ratelimit-reset') ?? 1);
  const backoff = Math.min(reset * 1000, 2 ** attempt * 250) + Math.random() * 100;
  await new Promise(r => setTimeout(r, backoff));
  return callTool(req, attempt + 1);
}

For a deeper treatment, point readers to a dedicated runbook on handling API rate limits and retries across third-party APIs.

Model Context Protocol Documentation Best Practices

A good MCP integration reference reads like a SOC 2 control narrative crossed with an API reference. It is dry, specific, and verifiable. It is an operational manual for AI developers. Here is the structure that has held up across enterprise reviews:

Beyond this structural table, you must include explicit, actionable guides for the engineers actually wiring up the connection:

1. Client Configuration Instructions

Do not assume the buyer knows how to wire up an MCP server. Provide exact, copy-paste instructions for the major clients.

For Claude Desktop: Show the exact JSON block required in the claude_desktop_config.json file.

{
  "mcpServers": {
    "your_saas_integration": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-everything"
      ],
      "url": "https://api.yourdomain.com/mcp/a1b2c3d4e5f6..."
    }
  }
}

For ChatGPT: Provide the step-by-step UI path: Settings -> Apps -> Advanced settings -> Enable Developer mode -> Add Custom Connector.

2. Auto-Generated Tool Schemas

Enterprise developers need to know exactly what tools the LLM will see. Document your naming conventions. Explain that tools are generated with descriptive, snake_case names (e.g., list_all_crm_contacts or update_a_support_ticket_by_id).

Buyers want to see properties, required, and enum values inline, not prose summaries. Highlight how required fields are strictly enforced before the tool executes.

3. Pagination Directives

Document how your tools instruct the LLM to handle pagination. For example, explain that list methods automatically inject a limit and next_cursor property into the query schema.

Show the exact prompt instruction embedded in your tool descriptions: "Always send back exactly the cursor value you received without decoding, modifying, or parsing it." This level of detail proves to engineering buyers that your platform is built specifically for the quirks of LLM behavior.

Standardizing ATS API Responses for LLM Consumption

The documentation structure above gets you past procurement. But the engineers on the buyer's side still have to wire your platform into their LLM pipelines - and that is where most ATS integrations fall apart. Applicant tracking systems like Greenhouse, Lever, Ashby, and Workday all model the same concepts (jobs, candidates, applications, interviews) using wildly different schemas, field names, and nesting depths. An LLM consuming raw responses from three different ATS providers will waste tokens parsing structural noise, hallucinate field names it saw in a different provider's response, and potentially leak PII that was never intended for the model's context.

Standardizing API responses for LLM consumption is not just a developer experience improvement. It is a token cost problem, a compliance problem, and a reliability problem all at once. The rest of this section is the implementation guide.

Field Selection: When to Include vs Redact

Not every field in an ATS response belongs in an LLM's context window. Sending a full candidate record - with home address, phone number, EEO demographic data, and interviewer notes - to an agent whose job is to move a pipeline stage is wasteful and dangerous. The right approach is to define a minimal safe-field list per use case.

The matrix below covers the four primary ATS agent use cases against the unified data model. "Include" means the field is sent to the LLM. "Redact" means it is stripped before the response leaves your normalization layer. "Hash" means the value is replaced with a deterministic, non-reversible token (useful for join operations without exposing the raw value).

Two patterns to notice. First, the EEO fields should never appear in an individual candidate context - they are only safe when aggregated for reporting. Second, free-text fields like job.description, candidate.resume_text, and scorecard.notes should be truncated to a token budget (typically 500-1000 tokens) even when included, because a single verbose job description can consume 20% of a smaller model's context window.

The LLM-Ready Envelope Schema

LLMs perform better when every tool response follows a predictable structure. A standard envelope wrapping every ATS response gives the model a consistent shape to parse, reduces hallucinated field access, and gives your telemetry layer a stable contract to hook into.

Here is the envelope pattern that works across list and get operations:

{
  "resource": "candidates",
  "method": "list",
  "integration": "greenhouse",
  "timestamp": "2026-06-15T08:30:00Z",
  "pagination": {
    "next_cursor": "eyJpZCI6IDQ1Nn0=",
    "has_more": true,
    "limit": 25
  },
  "data": [
    {
      "id": "cand_8a3f",
      "first_name": "Jamie",
      "last_name": "Chen",
      "current_stage": "Onsite",
      "applied_job_title": "Senior Backend Engineer"
    }
  ],
  "_meta": {
    "fields_redacted": ["email", "phone", "resume_text"],
    "fields_truncated": [],
    "token_estimate": 187
  }
}
 

Key design decisions in this envelope:

• resource and method are top-level so the LLM can identify what it is looking at without re-reading the tool call.
• pagination is always present (even for get operations where has_more is false) so the model never has to guess whether more records exist.
• _meta.fields_redacted lists fields that were present in the upstream response but stripped before delivery. This prevents the LLM from assuming the field does not exist in the ATS and hallucinating a workaround to fetch it.
• _meta.token_estimate gives the calling agent a budget signal. An orchestration layer can use this to decide whether to fetch the next page or summarize what it has.

This envelope should be the contract your MCP tools return. When docs and runtime share the same envelope, your reference document becomes a testable spec rather than aspirational prose.

Mapping and JSONata Recipes for ATS Normalization

The gap between "Greenhouse calls it current_stage" and "Lever calls it stage.text" and "Workday nests it three levels deep in staffing_event.disposition.status_name" is where unified API normalization earns its keep. A data-driven mapping architecture - where integration behavior is defined entirely in configuration, not code - solves this with declarative JSONata expressions.

JSONata is a lightweight query and transformation language for JSON. It lets you declare the mapping as a pure expression with no procedural code, and because expressions have no side effects, they are safe to evaluate in sandboxed or edge environments.

Here are three practical recipes that cover the most common ATS normalization patterns:

Recipe 1: Flatten a nested stage name

Greenhouse returns current_stage.name as a string. Lever nests it in stage.text. This expression normalizes both to a top-level current_stage field:

{
  "current_stage": $exists(current_stage.name)
    ? current_stage.name
    : $exists(stage.text)
      ? stage.text
      : "unknown"
}

Recipe 2: Coalesce email from multiple shapes

Some ATS providers return email as a string, others as an array of {type, value} objects. This normalizes to a single primary email:

{
  "email": $type(emails) = "array"
    ? emails[type = "primary"].value
    : $type(email) = "string"
      ? email
      : null
}

Recipe 3: Truncate free-text fields to a token budget

For fields like resume_text or job.description, cap the output to approximately 800 tokens (~3200 characters) to avoid blowing out context windows:

{
  "description_truncated": $length(description) > 3200
    ? $substring(description, 0, 3200) & "...[truncated]"
    : description
}

These expressions are stored alongside integration configuration, not embedded in handler code. When a new ATS provider is added, the mapping is a new config record - not a pull request. For a deep dive on the three-level override architecture (platform, environment, account) that makes this scale to hundreds of tenants, see the JSONata mapping guide.

PII Redaction and Hashing Patterns

Field selection handles the known fields. PII redaction handles the unknown ones - the recruiter who pasted a Social Security Number into a candidate note, the phone number buried in a free-text description field, the email address hiding inside a JSON blob your schema didn't anticipate.

A layered strategy works best:

Layer 1: Schema-driven field stripping. Before any response reaches the LLM, strip fields that are categorically unsafe based on the use-case matrix above. This is the fastest and most reliable layer - if the field is not in the allow-list, it does not get serialized. No regex, no ML models, no false positive risk.

Layer 2: Pattern-based scanning on free-text fields. For fields that pass the allow-list but contain free-text content (notes, descriptions, scorecard comments), apply regex-based detection for structured PII patterns: emails, phone numbers, SSNs, credit card numbers. Replace matches with typed placeholders like [EMAIL_REDACTED] or [PHONE_REDACTED] so the LLM knows data was there but cannot access the raw value.

Common regex patterns worth implementing:

const PII_PATTERNS: Record<string, RegExp> = {
  email:    /[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}/g,
  usPhone:  /(?:\+?1[-\s.]?)?\(?\d{3}\)?[-\s.]?\d{3}[-\s.]?\d{4}/g,
  ssn:      /\b\d{3}-\d{2}-\d{4}\b/g,
  ipv4:     /\b(?:\d{1,3}\.){3}\d{1,3}\b/g,
};
 
function redactFreeText(text: string): string {
  let cleaned = text;
  for (const [type, pattern] of Object.entries(PII_PATTERNS)) {
    cleaned = cleaned.replace(pattern, `[${type.toUpperCase()}_REDACTED]`);
  }
  return cleaned;
}

Layer 3: NLP-based entity recognition for contextual PII. Pattern matching misses names, addresses, and context-dependent identifiers that do not follow fixed formats. For these, Microsoft Presidio is the standard open-source option. It combines NLP models, regex patterns, and rule-based logic to detect entities like names, locations, and medical IDs, then anonymizes them through redaction, hashing, or masking. Presidio runs as a Python service or HTTP sidecar. Wire it into your normalization pipeline as a post-processing step on any free-text field that passes Layers 1 and 2.

MCP Tool Generation and Flattened Input Patterns

LLMs are bad at constructing deeply nested JSON. Every level of nesting increases the probability of a malformed tool call. When generating MCP tools from ATS resource definitions, flatten the input schema as much as possible.

Consider a create_application tool. The underlying ATS API might expect:

{
  "candidate": { "id": "cand_8a3f" },
  "job": { "id": "job_12ab" },
  "source": { "type": "referral", "referrer": { "name": "Alex" } }
}

The MCP tool schema should flatten this to top-level properties:

{
  "name": "create_an_ats_application",
  "description": "Create a new application linking a candidate to a job.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "candidate_id": { "type": "string", "description": "The candidate ID" },
      "job_id": { "type": "string", "description": "The job ID" },
      "source_type": { "type": "string", "enum": ["referral", "agency", "direct", "internal"] },
      "source_referrer_name": { "type": "string", "description": "Name of the referrer, if source_type is referral" }
    },
    "required": ["candidate_id", "job_id"]
  }
}

The mapping layer re-nests these flat inputs into the shape the upstream API expects before proxying the request. The LLM never sees the nesting. This pattern applies to every write operation across the ATS data model - create_candidate, update_application_stage, submit_scorecard - and is especially important for tools that map to JobFormFields, where the custom fields per role can number in the dozens.

Two additional patterns that improve LLM tool-call accuracy:

• Descriptive names over short names. list_all_ats_candidates beats list_candidates because the LLM has less ambiguity when multiple integrations are loaded in the same context.
• Enum values in the schema, not the description. Put allowed values in "enum": ["phone_screen", "onsite", "offer"] rather than describing them in prose. The LLM's structured output mode will enforce the constraint.

Testing Checklist and Telemetry Signals

A normalized ATS response is only trustworthy if you can prove it stays normalized. Ship CI tests alongside your mapping configuration, and instrument telemetry events that catch drift before it reaches production agents.

CI tests for field normalization:

Every ATS integration mapping should have a snapshot test that runs against a fixture of the upstream provider's raw response and asserts the normalized output matches the expected envelope shape.

import { describe, it, expect } from 'vitest';
import { normalize } from './ats-normalizer';
import greenhouseFixture from './fixtures/greenhouse-candidates.json';
import leverFixture from './fixtures/lever-candidates.json';
 
describe('candidate normalization', () => {
  it('greenhouse: produces envelope with required fields', () => {
    const result = normalize('greenhouse', 'candidates', 'list', greenhouseFixture);
    expect(result).toHaveProperty('resource', 'candidates');
    expect(result).toHaveProperty('pagination.has_more');
    expect(result.data[0]).toHaveProperty('id');
    expect(result.data[0]).toHaveProperty('first_name');
    expect(result.data[0]).not.toHaveProperty('email'); // redacted
  });
 
  it('lever: same envelope shape as greenhouse', () => {
    const result = normalize('lever', 'candidates', 'list', leverFixture);
    expect(Object.keys(result.data[0]).sort())
      .toEqual(Object.keys(
        normalize('greenhouse', 'candidates', 'list', greenhouseFixture).data[0]
      ).sort());
  });
 
  it('rejects unknown fields in output', () => {
    const result = normalize('greenhouse', 'candidates', 'list', greenhouseFixture);
    const allowedFields = ['id', 'first_name', 'last_name', 'current_stage', 'applied_job_title'];
    for (const record of result.data) {
      for (const key of Object.keys(record)) {
        expect(allowedFields).toContain(key);
      }
    }
  });
});

The key assertion is the second test: normalized output from different ATS providers should produce the same field set for the same resource and use case. If Greenhouse returns current_stage but Lever returns stage_name, the mapping is broken and the LLM will hallucinate.

Telemetry events to instrument:

These events feed into a freshness and correctness dashboard. When the pii_detected counter spikes, you know a recruiter started pasting sensitive data into a field you were passing through. When fallback_used fires, you know an ATS provider changed their API response shape.

Operational Playbook for Long-Running ATS Endpoints

Not every ATS API call returns in 200ms. Bulk candidate exports, full pipeline snapshots, and compliance data pulls can take 30 seconds to several minutes. LLM tool calls have timeout expectations - Claude Desktop, for example, assumes a tool responds within a few seconds. If your MCP server blocks for 45 seconds waiting on a Workday bulk export, the agent will assume the tool failed and retry, potentially triggering duplicate operations.

Handle these with an async polling pattern:

1. The MCP tool returns immediately with a status: "accepted" response and a poll_id.
2. A second MCP tool - check_ats_export_status - accepts the poll_id and returns either status: "running" with an estimated completion time, or status: "complete" with the result payload.
3. The tool description instructs the LLM to call check_ats_export_status after a delay instead of retrying the original export.

{
  "resource": "candidates",
  "method": "bulk_export",
  "status": "accepted",
  "poll_id": "exp_7f2a9c",
  "retry_after_seconds": 15,
  "data": null,
  "_meta": {
    "instruction": "Do not retry this tool. Call check_ats_export_status with poll_id exp_7f2a9c after 15 seconds."
  }
}

The _meta.instruction field is read by the LLM as part of the tool response and steers it away from retrying. This pattern also applies to interview scheduling endpoints (which often depend on calendar availability lookups) and scorecard submission endpoints that trigger downstream webhooks.

Document these async tools explicitly in your MCP reference, including the expected polling interval and the maximum time before the operation is considered failed. Enterprise buyers running compliance audits or diversity reporting workflows will hit these long-running endpoints first, and their agents need a clear contract for how to wait.

How to Generate and Publish Your MCP Reference Instantly

The hardest part of maintaining an enterprise-grade MCP reference is not writing it once. It is keeping it accurate across hundreds of resources and dozens of upstream API changes per quarter. If your engineering team has to update a markdown file every time they add a new field to an integration, the documentation will drift. When documentation drifts in an AI context, agents hallucinate missing parameters and break production workflows.

Two patterns prevent drift and work reliably in production.

Pattern 1: Documentation-Driven Tool Generation

Derive every tool definition from a single configuration source. Truto approaches this by treating integration behavior entirely as data. The platform utilizes a "zero integration-specific code" architecture. There are no hardcoded handler functions for individual integrations. Instead, integration capabilities are defined in a standardized JSON configuration (config.resources) and mapped using JSONata expressions.

Because the architecture is purely data-driven, Truto automatically generates MCP tools directly from two existing data sources: the integration's resource definitions (which API endpoints exist) and structured documentation records (descriptions and JSON Schema for each method).

When a customer generates an MCP server URL, the system does not load a static list of tools. On every tools/list request, the server dynamically intersects the integration's available resources with its documentation records. If an endpoint does not have a documentation record, it is silently dropped. The same generator that produces the runtime tool list produces the public reference, so they cannot drift.

flowchart LR
  A[Integration config<br/>+ documentation records] --> B[Tool definitions<br/>name, description, schemas]
  B --> C[Public MCP reference<br/>auto-rendered catalog]
  B --> D[Live MCP server<br/>/mcp/:token endpoint]
  C -. always in sync .- D

When the docs and the runtime share one source, your reference document becomes a contract instead of a screenshot.

Pattern 2: Scoped Server Creation with Predictable URLs

Expose a single API that creates an MCP server scoped to a tenant, with method and tag filters and an optional TTL:

POST /integrated-account/:id/mcp
Content-Type: application/json
 
{
  "name": "Acme Corp - Support agent",
  "config": {
    "methods": ["read"],
    "tags": ["support"],
    "require_api_token_auth": true
  },
  "expires_at": "2026-06-01T00:00:00Z"
}

The response returns a URL like https://api.example.com/mcp/<hashed-token> that the customer pastes into Claude or ChatGPT. This is the single primitive that powers contractor access, time-boxed agent runs, and per-environment isolation. Document the endpoint, document the lifecycle, and your reference covers 80% of the procurement checklist on one page.

Turn AI Compliance into a Competitive Advantage

Enterprise procurement is a game of risk mitigation. The enterprises buying AI-ready SaaS in 2026 are not rewarding the loudest demos. They are rewarding the vendors whose documentation answers their CISO's questions before the call.

A dedicated, highly technical MCP integration reference transforms your AI capabilities from a perceived security risk into a validated architectural advantage. By transparently documenting your pass-through architecture, your token isolation strategies, and your strict schema enforcement, you eliminate the ambiguity that causes deals to stall.

The build path is clear: lock down the security model (per-tenant scoping, hashed token storage, optional second-factor auth, 429 passthrough), generate the tool catalog from the same config that runs the server, and publish a single URL that maps cleanly to procurement checklists. Stop letting legacy competitors dictate the narrative around AI readiness, and give your enterprise buyers the exact artifact they need to approve the purchase.