Install Jentic One Beta

Connect the MRC Data — Apparel Supply Chain Infrastructure API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the MRC Data — Apparel Supply Chain Infrastructure API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmeacheal.ai%2Fmeacheal" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmeacheal.ai%2Fmeacheal" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with MRC Data — Apparel Supply Chain Infrastructure API.

Search apparel suppliers by province, city, type such as factory or trading_company, product_type, min_capacity, min_quality_score, and compliance_status

Search fabrics by category such as woven or knit, min_weight_gsm and max_weight_gsm, composition, suitable_for, and max_price_rmb

Read a full fabric record with its lab-test specifications, and list every supplier offering that fabric with a price comparison

List all fabrics a specific supplier offers with pricing

GET STARTED

Start building with MRC Data — Apparel Supply Chain Infrastructure API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use MRC Data — Apparel Supply Chain Infrastructure API for, with concrete tasks.

★ Agent-led apparel sourcing through Jentic

A sourcing agent can shortlist Chinese manufacturers for a brief through Jentic: it searches suppliers by province, product type, minimum capacity, and compliance status, reads each candidate's full profile, and lists the fabrics each one supplies with pricing. Every call is a read, so the agent builds a shortlist without changing any data.

Search suppliers with province Guangdong, type factory, product_type dresses, and min_quality_score 4, read the full profile for the top three, then list each one's fabrics and return a shortlist with prices

Fabric selection with price comparison

Designers and buyers looking for a fabric can filter by category, weight range in gsm, composition, and maximum price in RMB, open the full record with its lab-test specifications, and see every supplier offering that fabric with a price comparison. That turns a fabric brief into a priced supplier list in a few calls.

Search fabrics with category knit, min_weight_gsm 180, max_weight_gsm 220, and composition cotton, open the first result, then list its suppliers and report the lowest price offered

Specification due diligence

Declared specifications do not always match reality. The discrepancy route returns records where a declared fabric weight, fabric composition, supplier capacity, or worker count deviates from lab test results by at least a chosen percentage, helping a buyer flag suppliers for closer audit before placing an order.

Call the discrepancy operation with field fabric_weight and min_discrepancy_pct 10, then report which suppliers appear most often in the results

Key Endpoints

10 endpoints — the mrc data apparel supply chain api searches a database of chinese apparel supply chain entities, including manufacturers, fabrics, and industrial clusters, with verified records covering capacity, compliance, certifications, export qualifications, and aatcc, iso, and gb lab test reports.

METHOD

PATH

DESCRIPTION

GET

/v1/suppliers

Search apparel suppliers

GET

/v1/suppliers/{supplier_id}

Get a full supplier profile

GET

/v1/fabrics

Search fabrics

GET

/v1/fabrics/{fabric_id}/suppliers

List suppliers offering a fabric with prices

GET

/v1/clusters/compare

Compare industrial clusters

GET

/v1/discrepancy

Surface specifications that deviate from lab tests

GET

/v1/suppliers

Search apparel suppliers

GET

/v1/suppliers/{supplier_id}

Get a full supplier profile

GET

/v1/fabrics

Search fabrics

GET

/v1/fabrics/{fabric_id}/suppliers

List suppliers offering a fabric with prices

GET

/v1/clusters/compare

Compare industrial clusters

GET

/v1/discrepancy

Surface specifications that deviate from lab tests

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring MRC Data by hand means handling its bearer key, learning its filter vocabulary such as supplier types and fabric categories, and paging with limit and offset. Through Jentic you install once, import the MRC Data API from the API Directory, store the key once, and your agent calls it.

Permission scoping

MRC Data puts supplier and fabric ids in the URL path (/v1/suppliers/{supplier_id}, /v1/fabrics/{fabric_id}), so a rule can bound your agent to the operations you pick, down to specific records. You choose the operations it may call, so tier-sensitive ones like full supplier profiles are not included unless you add them.

Credential isolation

Your MRC Data key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'find apparel factories' or 'compare fabric prices', and Jentic returns the matching MRC Data operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using MRC Data — Apparel Supply Chain Infrastructure API through Jentic.

What authentication does the MRC Data Apparel Supply Chain API use?

The API authenticates with a bearer token per its OpenAPI spec: send your API key from meacheal.ai as Authorization: Bearer on every request. Supplier detail fields vary by your account tier. Through Jentic the key is stored encrypted on your own instance and injected at call time.

Is there an MRC Data MCP server?

Yes - the vendor describes MRC Data as available as an MCP server. You can also connect the API directly through Jentic: your agent gets the full 10-endpoint surface from the spec without loading another server's tool definitions into its context, your key is injected at call time by your self-hosted instance instead of living in an MCP config, and your rules decide which operations the agent may call.

Can I limit what my agent is allowed to do with the MRC Data API?

Yes. Every operation is a read, so the useful limit is which data the agent may pull. For example, write a rule that allows only GET /v1/fabrics and GET /v1/fabrics/{fabric_id}/suppliers, so a fabric-pricing agent can search fabrics and compare supplier prices but cannot read full supplier profiles. Every call it makes is logged.

What are the rate limits for the MRC Data API?

The OpenAPI spec does not specify numeric rate limits, though every operation can return a RateLimited error. Check https://meacheal.ai for current limits by tier.

Can I check whether a supplier's declared specifications are accurate?

Yes. GET /v1/discrepancy returns records where a declared value deviates from lab test results. Choose the field, one of fabric_weight, fabric_composition, supplier_capacity, or worker_count, and set min_discrepancy_pct to the smallest deviation worth flagging.

How do I find apparel suppliers through Jentic?

Search Jentic for an intent like 'find apparel manufacturers in China', import the MRC Data API, and store your key once. Your agent then calls GET /v1/suppliers with filters such as province, product_type, and min_capacity, and reads each match with GET /v1/suppliers/{supplier_id}. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Meacheal MRC Data — Apparel Supply Chain Infrastructure API

AI ReadinessFoundational (C+)58/100

See full scorecard

Official vendor OpenAPI document · agent-readyData EnrichmentCompany Databearer10 EndpointsREST

For Agents

Find Chinese apparel factories and fabrics by location, capacity, compliance, weight, and price, compare industrial clusters, and flag specifications that differ from lab test results. Read-only, with fields that vary by tier.

Use for: Find knitwear factories in Zhejiang with a minimum monthly capacity, Search for woven cotton fabrics under 150 gsm, List all suppliers that offer this fabric and compare prices, Compare three denim industrial clusters side by side

Not supported: Does not handle purchase orders, shipping, payments, or supplier messaging. Use for read-only apparel supplier, fabric, and cluster research only.

The MRC Data Apparel Supply Chain API searches a database of Chinese apparel supply chain entities, including manufacturers, fabrics, and industrial clusters, with verified records covering capacity, compliance, certifications, export qualifications, and AATCC, ISO, and GB lab test reports. You can filter suppliers by province, city, type, product type, minimum capacity, quality score, and compliance status, and filter fabrics by category, weight in gsm, composition, suitability, and maximum price in RMB. Supplier and fabric records link both ways with pricing, clusters can be compared side by side, and a discrepancy route surfaces declared specifications that deviate from lab test results.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: CSignals: 4

83%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

30%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: A-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

75%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: DSignals: 4

28%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

48%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: D+Signals: 1

49%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the MRC Data — Apparel Supply Chain Infrastructure API to your agent

Capabilities

Start building with MRC Data — Apparel Supply Chain Infrastructure API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Meacheal MRC Data — Apparel Supply Chain Infrastructure API

Jentic AI Readiness Score

MRC Data — Apparel Supply Chain Infrastructure API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness