The Paylo MCP Server connects AI agents to the Paylo storefront network, enabling product discovery, merchant browsing, and end-to-end order and payment management.
Discover Merchants – List and browse available Paylo storefronts, optionally filtering by category or limiting results.
Search Products – Search for products across all stores or within a specific merchant, using a keyword, category, or merchant ID.
Get Product Details – Retrieve detailed information about a specific product (price, description, availability) by its unique ID.
Create Orders – Place a new order by specifying product IDs and quantities, with an optional customer email for receipts.
Generate Payment Links – Produce a Paystack-powered payment URL for an existing order to share with the customer for checkout.
Check Payment Status – Query the current payment status of an order, returning whether it is success, pending, or failed.
All responses include UTM-tagged URLs for attribution tracking.
Queries the Paylo product catalog database to retrieve merchant and product information for commerce operations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Paylo MCP Server search for wireless headphones under 10000 naira"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The Paylo MCP Server connects AI agents to the Paylo storefront network via the Model Context Protocol. Agents can search merchants, browse products and services, and retrieve catalog data — all through a live hosted SSE endpoint.
The server is hosted at:
https://mcp.usepaylo.com/sseNo local setup required. Point your MCP client at this URL.
Related MCP server: mcp-server-paystack
Tool | Description |
| Search active Paylo storefronts by name, keyword, or category |
| Get full catalog summary and stats for a storefront by slug |
| Search products across all stores or within a specific storefront |
| Get details for a single product by storefront slug and product slug |
| Search services across all stores or within a specific storefront |
| List categories available within a storefront |
All responses include UTM-tagged URLs for attribution tracking.
AI Agent → MCP (SSE) → Paylo MCP Server → usepaylo.com API → Storefront CatalogThe MCP server is a thin proxy. It does not access the database directly — all data is fetched from the Paylo backend API and returned as structured JSON.
If you prefer to run your own instance:
git clone https://github.com/scephiro/paylo-mcp-server.git
cd paylo-mcp-server
npm install
npm run buildSet environment variables:
GPT_API_SECRET=your_backend_secret
PAYLO_API_BASE_URL=https://usepaylo.com # default
MCP_PORT=3030 # defaultStart the server:
node build/index.jsThe server listens on http://0.0.0.0:3030. Connect via GET /sse and send messages to POST /messages?sessionId=<id>.
docker run -p 3030:3030 \
-e GPT_API_SECRET=your_secret \
ghcr.io/scephiro/paylo-mcp-server:latestMIT — see LICENSE.
Check the payment status of an order. Returns 'success', 'pending', or 'failed'.
| Name | Required | Description | Default |
|---|---|---|---|
| orderId | Yes | The ID of the order to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the return values ('success', 'pending', or 'failed'), which is helpful, but it doesn't cover other important aspects such as error handling, authentication requirements, rate limits, or whether it's a read-only operation (implied by 'check' but not explicit). For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of two sentences that directly state the tool's purpose and return values. Every sentence earns its place by providing essential information without any waste, making it easy for an AI agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return values, but it lacks details on usage context, error handling, and behavioral traits. Without annotations or an output schema, the description should do more to compensate, but it only meets the minimum viable threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'orderId' fully documented in the input schema. The description doesn't add any additional meaning beyond what the schema provides (e.g., it doesn't explain format or constraints for 'orderId'). According to the rules, with high schema coverage, the baseline score is 3, which is appropriate here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check the payment status of an order.' It uses a specific verb ('check') and resource ('payment status of an order'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'create_order' or 'generate_payment_link', which is why it doesn't reach a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an order ID from 'create_order'), exclusions, or how it relates to sibling tools like 'generate_payment_link' for payment initiation. This lack of contextual usage information limits its effectiveness for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new order for products. Returns an order ID that can be used to generate a payment link.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | List of items to purchase | |
| customerEmail | No | Customer email for receipt (defaults to guest@paylo.ai) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool creates a new order and returns an order ID, which implies a write operation, but it doesn't cover critical aspects like authentication needs, rate limits, error handling, or whether the order is immediately processed. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's action and outcome. Every sentence earns its place by providing essential information without unnecessary details, making it efficient and easy to understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error conditions, or the structure of the returned order ID. While it mentions the order ID's use for payment links, it doesn't fully compensate for the missing structured data, leaving gaps in understanding the tool's full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for 'items' and 'customerEmail'. The description adds no additional meaning beyond what the schema provides, as it doesn't explain parameter usage, constraints, or examples. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create a new order for products.' It specifies the verb ('create') and resource ('order for products'), making the action explicit. However, it doesn't distinguish this from sibling tools like 'generate_payment_link' or 'check_payment_status' beyond mentioning the returned order ID's use for payment links, which is helpful but not a full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions that the returned order ID can be used to generate a payment link, which implies a workflow but doesn't explicitly state when to choose this over other tools like 'generate_payment_link' directly or what prerequisites might be needed. No exclusions or clear context for usage are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a Paystack payment link for an existing order. Returns the URL to share with the user.
| Name | Required | Description | Default |
|---|---|---|---|
| orderId | Yes | The ID of the order to pay for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'Returns the URL to share with the user', which adds some context about the output. However, it lacks details on permissions, rate limits, error handling, or whether this action is idempotent or has side effects, leaving significant gaps for a tool that generates payment links.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded and efficiently structured in two sentences: one stating the action and resource, and another explaining the return value. There is no wasted language, and every sentence earns its place by providing essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (generating payment links), lack of annotations, and no output schema, the description is minimally adequate. It covers the purpose and return value but misses behavioral details like authentication needs or error cases. The schema handles parameters well, but overall completeness is limited by the absence of richer context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'orderId' parameter clearly documented. The description does not add any additional meaning or syntax details beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate a Paystack payment link') and the target resource ('for an existing order'), distinguishing it from siblings like 'create_order' or 'check_payment_status'. It precisely communicates what the tool does without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'for an existing order', suggesting it should be used after an order is created. However, it does not explicitly state when to use this tool versus alternatives like 'create_order' or 'check_payment_status', nor does it provide exclusions or prerequisites beyond the implied order existence.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific product including price, description, and availability.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique identifier of the product |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only), but doesn't mention permissions, rate limits, error handling, or response format. This is inadequate for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It could be slightly more structured by separating usage context, but it avoids redundancy and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., JSON structure), error cases, or behavioral traits like authentication needs. For a read operation with minimal structured data, this leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the 'id' parameter. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('product'), specifying what information is retrieved (price, description, availability). It distinguishes from siblings like 'search_products' by focusing on a single product, but doesn't explicitly contrast with all alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'search_products' or 'list_merchants'. The description implies usage for detailed info on a specific product, but lacks explicit context, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available Paylo merchants and storefronts. Use this to discover stores before searching for products.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter merchants by category | |
| limit | No | Limit number of results (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it indicates this is a listing/discovery operation (implying read-only behavior), it doesn't explicitly state whether this requires authentication, what the response format looks like, whether results are paginated, or any rate limits. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second provides valuable usage guidance. There's zero wasted language, and the information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (listing operation with 2 optional parameters), no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose and basic usage but lacks details about response format, authentication requirements, and behavioral characteristics that would be important for an AI agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('category' and 'limit') with their types and descriptions. The description doesn't add any parameter-specific information beyond what's in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('List available Paylo merchants and storefronts') and identifies the resource type. It distinguishes this from sibling tools like 'search_products' by focusing on merchant discovery rather than product search. However, it doesn't explicitly differentiate from other merchant-related tools (none exist in the sibling list), so it falls just short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('Use this to discover stores before searching for products'), establishing it as a preliminary step to product searches. It implicitly distinguishes from 'search_products' by focusing on merchants rather than products. However, it doesn't provide explicit exclusions or mention alternatives like 'get_product_details' that might also involve merchant information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for products across Paylo stores. Can filter by merchant or category.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for products | |
| merchantId | No | Filter by specific merchant ID | |
| category | No | Filter by product category | |
| limit | No | Limit number of results (default: 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions filtering capabilities but fails to describe critical behaviors like pagination (only implies limit parameter), response format, error handling, authentication requirements, or rate limits. For a search tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that directly state the tool's purpose and main capabilities. Every word earns its place with zero redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple filters), lack of annotations, and absence of an output schema, the description is insufficient. It doesn't explain what the search returns, how results are structured, or important behavioral aspects. The description should provide more context to compensate for missing structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds minimal value by mentioning merchant and category filtering, but doesn't provide additional context beyond what's in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('search') and resource ('products across Paylo stores'), and mentions filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'get_product_details' or 'list_merchants', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance by mentioning filtering options, but offers no explicit advice on when to use this tool versus alternatives like 'get_product_details' for specific products or 'list_merchants' for merchant information. There's no mention of prerequisites, limitations, or optimal use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0check_payment_statuscreate_ordergenerate_payment_linkget_product_detailslist_merchantssearch_productsEach tool has a clearly distinct purpose with no overlap: check_payment_status handles payment status, create_order creates orders, generate_payment_link generates payment links, get_product_details retrieves product info, list_merchants lists merchants, and search_products searches products. An agent can easily differentiate between these operations.
All tools follow a consistent verb_noun naming pattern (e.g., check_payment_status, create_order, generate_payment_link, get_product_details, list_merchants, search_products). This uniformity makes the tool set predictable and easy to understand.
With 6 tools, the server is well-scoped for handling payments and product discovery in a Paylo context. Each tool serves a clear and necessary function, avoiding bloat while covering core workflows like order creation, payment processing, and product browsing.
The tool set covers key operations for payment processing and product discovery, including order creation, payment link generation, status checking, and product/merchant listing. A minor gap is the lack of tools for updating or deleting orders, but agents can likely work around this given the focused scope on payment flows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Directory of APIs, merchants, and tools AI agents can actually use.
Agentic commerce gateway: discovery, search, checkout across Shopify/Woo/Odoo/PrestaShop.
AI shopping gateway for product search, inventory, carts, and merchant-hosted checkout.
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/scephiro/paylo-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
