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., "@agentstorefront-mcp List top 5 data-enrichment services on AgentStorefront"
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.
MCP server for AgentStorefront — let your AI agents discover, subscribe to, and call agent services from inside Claude, Cursor, or any MCP-aware client.
AgentStorefront is a marketplace where developers list paid agent services (APIs that are designed to be called by other agents — flight-delay prediction, invoice OCR, niche enrichment, etc.).
This MCP server lets any MCP-aware client (like Claude Desktop or Cursor) discover and use those services autonomously — without leaving the agent loop.
Tool | Purpose |
| List available services, filtered by category |
| Semantic search: "find me a flight-delay API" |
| Full schema + pricing + SLA for a specific service |
| Get expected monthly cost for an estimated call volume |
Related MCP server: AgentPay
Add to your claude_desktop_config.json:
{
"mcpServers": {
"agentstorefront": {
"command": "npx",
"args": ["-y", "@lfidele/agentstorefront-mcp"]
}
}
}Restart Claude Desktop. The 4 tools above will appear in the tools panel.
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"agentstorefront": {
"command": "npx",
"args": ["-y", "@lfidele/agentstorefront-mcp"]
}
}
}npm install -g @lfidele/agentstorefront-mcp
agentstorefront-mcp # runs on stdioOnce installed, you can ask your MCP client things like:
"Find me a flight-delay prediction API on AgentStorefront that costs less than 5 cents per call."
"What's the pricing if I call the invoice-OCR service 10,000 times a month?"
"List the top 5 data-enrichment services on AgentStorefront."
The agent will use these tools transparently.
Env var | Default | Purpose |
|
| Override API endpoint (self-hosting / staging) |
GitHub: https://github.com/lmaniraruta/agentstorefront-mcp Issues: https://github.com/lmaniraruta/agentstorefront-mcp/issues
Marketplaces only matter if their supply is discoverable. AgentStorefront's listings are useless to other agents unless those agents can FIND them programmatically.
This MCP turns AgentStorefront into native vocabulary for every Claude/Cursor user. A developer asks Claude "is there an API for X?" → Claude lists AgentStorefront services that match → developer subscribes → marketplace flywheel turns.
Built for the two-sided market:
Sellers win because their listings are now findable from inside the dominant AI assistants
Buyers win because they don't have to context-switch to a separate marketplace tab
MIT © 2026 Fidele Maniraruta
Built in public. Real numbers posted on X @lmaniraruta.
Get full details on a single AgentStorefront service: schema, pricing, rate limits, SLA, sample inputs/outputs.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes | The service ID (UUID or slug). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains what the tool returns (schema, pricing, etc.) and implies a read-only operation by using 'Get'. More detail on auth or rate limits would raise score, but current disclosure is adequate for a simple retrieval tool.
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?
Single sentence, front-loaded with action and object, includes specific data details. No wasted 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?
Tool has one simple parameter, no output schema, but description enumerates return fields (schema, pricing, etc.) sufficiently. All necessary context for an agent to invoke correctly.
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?
Only one parameter 'service_id' with schema description already clear ('UUID or slug'). Description adds no extra meaning beyond what the schema provides, so baseline 3 applies.
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?
Description specifies 'Get full details on a single AgentStorefront service' and lists specific data fields (schema, pricing, rate limits, etc.). It clearly distinguishes from siblings list_services and search_services, which are for listing and searching, and request_quote, which is for quoting.
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?
Usage is implied by contrasting with siblings (list_services and search_services for broader retrieval, request_quote for quoting). However, no explicit when-to-use or when-not-to-use statements are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List AgentStorefront services that an agent can subscribe to and call. Returns names, prices, and short descriptions. Optionally filter by category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20, max 100). | |
| category | No | Optional category filter (e.g., 'data', 'communication', 'finance'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description implies a safe read operation ('list') and suggests no side effects. However, it does not mention pagination, rate limits, or any potential restrictions. Basic behavioral context is present but not rich.
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?
Two sentences, no superfluous words. Front-loaded with purpose and return information. Every sentence adds value.
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 output schema, the description briefly states return fields. For a simple list tool, this is mostly sufficient. Minor gap: no mention of sorting or order, but acceptable for the complexity level.
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 coverage is 100%, so descriptions already document both parameters. The tool description adds 'Optionally filter by category' which mirrors the schema. No additional semantic value beyond what the schema provides.
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?
Description clearly states 'List AgentStorefront services' with specific verb+resource and mentions return fields (names, prices, descriptions). This differentiates it from siblings like get_service (single item) and search_services (search).
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 indicates optional filter by category but does not explicitly state when to use this tool versus alternatives like search_services. The context is clear enough for a simple list, but lacks when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Request a quote for using a service at a specific call volume. Returns expected monthly cost, throughput, and subscription URL.
| Name | Required | Description | Default |
|---|---|---|---|
| No | Optional contact email for follow-up. | ||
| service_id | Yes | ||
| expected_calls_per_month | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions return values (cost, throughput, subscription URL) but fails to disclose whether the action is read-only or has side effects (e.g., creating a quote record), authentication needs, or error handling.
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, well-structured sentence that front-loads the core action and outcome. Every word is meaningful, with no redundancy or wasted space.
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 has three parameters, no output schema, and no annotations, the description is too brief. It omits important details such as parameter formats, validation, side effects, and what happens on failure. It is not complete enough for an agent to use confidently.
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 33% (only email has a description). The description adds context for expected_calls_per_month by mentioning 'specific call volume', but it does not clarify service_id (likely a service identifier) or email's role beyond 'optional contact'. It provides some added value but does not fully compensate for the low 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 action ('request a quote') and resource ('for using a service at a specific call volume'), and it distinguishes itself from sibling tools (get_service, list_services, search_services) which focus on retrieving service information rather than generating quotes.
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 the tool is for obtaining cost estimates based on call volume, but it does not provide explicit guidance on when to use it versus alternatives, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Semantic search across AgentStorefront listings. Use a natural-language query like 'flight delay prediction' or 'invoice extraction'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language search query. | |
| max_price_cents | No | Optional max per-call price in cents. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as whether the tool is read-only, pagination behavior, authorization requirements, or rate limits. The description only covers the basic operation.
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 concise with two sentences, no extraneous content, and front-loads key information. However, it could be slightly more structured with separate guidelines.
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 two parameters and no output schema, the description provides basic purpose but lacks details on result format, sorting, pagination, or how to combine with sibling tools. It is insufficient for a search tool needing completeness.
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 coverage is 100% with descriptions for both parameters. The description adds value for the 'query' parameter by showing example usage, but does not add any additional meaning for 'max_price_cents'. Overall, baseline 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 that the tool performs semantic search across AgentStorefront listings, using a natural-language query. It provides concrete examples, and the purpose is distinct from sibling tools like list_services (browsing) and get_service (specific service).
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 advises using natural-language queries, but does not explicitly state when to use this tool over siblings or provide exclusion criteria. It implies usage for flexible search but lacks clear when-to-use guidance.
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.
v0.1.1get_servicelist_servicesrequest_quotesearch_servicesEach tool has a distinct purpose: listing services, searching, getting details, and requesting quotes. No overlap or confusion.
All tool names follow a consistent verb_noun pattern (get_service, request_quote, list_services, search_services).
4 tools is well-scoped for an agent storefront, covering discovery, details, and quoting without being excessive or sparse.
Covers all key user flows: listing, searching, getting detailed info, and requesting a quote (which includes subscription URL). No obvious gaps.
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