Discover and call 10,000+ production APIs from one MCP server. Pay-per-call billing for AI agents.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.4/5 across 8 of 8 tools scored.
Each tool has a clearly distinct purpose covering account management, catalog browsing, searching, API details, payment, and execution. No overlap in functionality.
All tool names follow a consistent snake_case verb_noun pattern (e.g., browse_catalog, get_api_info, list_categories), with register being the only single verb but still clear.
8 tools is well-scoped for an API hub, covering registration, account status, catalog exploration, payment setup, and API calling without unnecessary bloat.
Core workflows (register, browse, search, get info, call) are covered. Minor gap: no tool for usage history or API key regeneration, but not essential for primary use.
account_statusAInspectCheck the current user's account status, billing, and usage. Returns plan info, API call count, the amount consumed this billing cycle in USD (amount_consumed_this_cycle_usd), whether a credit card is on file, and any failed/pending payments. Use this to diagnose why API calls are blocked (missing card or failed payment). Requires the user's API key in the Authorization header.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes return fields (plan info, API call count, amount consumed, credit card on file, failed payments) and requires user's API key in Authorization header. No annotations provided, so description carries full burden and handles it well.
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 succinct sentences: first states main purpose, second provides usage guidance. Every sentence adds value with 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?
Despite no output schema, description lists all key return values. For a tool with no parameters, this provides complete context for an agent to decide and 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?
No parameters exist; schema coverage is 100%. Description does not need to add parameter info, meeting the baseline for zero-param tools.
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 checks account status, billing, and usage with specific verb and resource. It distinguishes from siblings like get_api_info and get_payment_url by focusing on user account billing details.
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?
Explicitly states when to use: to diagnose blocked API calls due to missing card or failed payment. Does not mention when not to use or provide alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
browse_catalogAInspectBrowse the full Zyla API Hub catalog with pagination. Use this to explore ALL public APIs beyond the top 500. Supports filtering by category and search keyword. Returns paginated results with total count and page info.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (default: 1) | |
| sort | No | Sort order: "popularity", "newest", or "name" (default: popularity) | |
| search | No | Search keyword to filter APIs by name or description (server-side search across ALL public APIs) | |
| category | No | Filter by category name (use list_categories to see available categories) | |
| per_page | No | Results per page (default: 20, max: 100) | |
| include_params | No | If true, include full endpoint parameters in the response (default: false, to keep responses compact) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool returns paginated results with total count and page info, but does not disclose authentication requirements, rate limits, or that it is read-only. The description adds some behavioral context but is not comprehensive.
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 three concise sentences that front-load the core purpose and key features. Every sentence provides valuable information with no 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 has 6 parameters and no output schema or annotations, the description covers pagination, filtering, and response contents. It is mostly complete but could mention authentication or error cases for 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?
Schema description coverage is 100%, so the baseline is 3. The description reinforces that search is server-side across all APIs and mentions filtering, but does not add significant new meaning beyond what the schema already 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?
The description clearly states it's for browsing the full Zyla API Hub catalog with pagination. It distinguishes from sibling tools like 'search_catalog' by emphasizing exploration of all public APIs beyond the top 500, making its purpose unambiguous.
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 this to explore all public APIs beyond the top 500, which sets context. However, it does not explicitly contrast with the sibling 'search_catalog' tool or specify when not to use it, lacking explicit alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
call_apiAInspectCall an API endpoint on Zyla API Hub. Requires the user's API key and a credit card on file. Billing is pay-per-successful-request: you only pay for 2xx responses. Each API has its own price per call (shown in get_api_info), and every successful response includes cost_usd — the exact amount billed for that call. There are no monthly subscriptions or minimums — usage is charged to your credit card automatically. If a 402 error is returned with a payment_url, the user MUST open that URL in their browser to add a credit card before retrying. Pass the api_id, endpoint_id, HTTP method, and parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| api_id | Yes | The numeric API ID | |
| method | No | HTTP method: GET or POST (default: GET) | |
| params | No | Key-value parameters to send with the request | |
| endpoint_id | Yes | The numeric endpoint ID within the API |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses billing model (pay-per-success), cost_usd in response, 402 error handling with payment_url, and automatic credit card charging. This is comprehensive behavioral transparency.
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 moderately dense but well-structured: purpose first, then billing details, error handling, and parameter hints. Every sentence adds value, though some details like billing could be slightly more succinct.
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 4 parameters, no output schema, and no annotations, the description covers purpose, usage context, billing, error handling, and parameter usage thoroughly. It is nearly complete, though clarification on obtaining the API key is left to other tools.
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%, and the description adds value by specifying that method defaults to GET, params are key-value, and grouping api_id/endpoint_id as main identifiers. It enhances the schema 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 'Call an API endpoint on Zyla API Hub' with a specific verb and resource. It distinguishes from sibling tools like browse_catalog or search_catalog by focusing on API endpoint execution.
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 explains when to use (to call an API endpoint) and provides context about required user API key and credit card. It implicitly excludes other operations but does not explicitly name alternatives or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_api_infoAInspectGet full details of a specific Zyla API by its numeric ID. Returns the API name, description, category, price per successful call (USD), average latency (ms), and all endpoints with their parameters, example values, and a formal JSON Schema (input_schema) for argument validation.
| Name | Required | Description | Default |
|---|---|---|---|
| api_id | Yes | The numeric API ID (e.g. 781) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Lists exactly what is returned (name, description, category, price, latency, endpoints, schema). No annotation coverage, but tool is read-only with no side effects; disclosure is adequate.
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 with clear structure: action, input, output. No redundant words. Every clause 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 one parameter, no output schema, and no annotations, the description fully explains what the tool does and what it returns. No 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?
Only one parameter 'api_id' with example and type already in schema. Description adds context that ID is numeric and ties to the information retrieval. Schema coverage is 100%.
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?
Clearly states the verb 'Get full details' and the resource 'specific Zyla API by its numeric ID'. Distinguishes from siblings like browse_catalog (listing) and call_api (execution).
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?
Implied use when needing full API details given an ID, but no explicit guidance on when to use versus search_catalog or browse_catalog, or prerequisites like obtaining the ID first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_payment_urlAInspectGet a Stripe Checkout URL so the user can add or update their credit card. REQUIRED after registration and whenever a 402 error occurs. The user must open the returned URL in their browser to add/update their card. Billing is pay-per-successful-request: no subscriptions, no monthly fees, no minimums. You are only charged for successful API responses (2xx). Prices vary per API (see get_api_info). API calls are blocked until a valid credit card is on file.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, but description discloses that user must open URL, billing is pay-per-success, and API calls are blocked without valid card. Could mention what happens if user doesn't complete, but overall good.
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?
Concise, front-loaded with main purpose, no unnecessary words. Every sentence adds relevant information.
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 params or output schema, description covers use case, requirements, user action, and billing implications. Complete for agent decision-making.
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?
No parameters, so description does not need to add param meaning. Schema coverage is 100%. Description still adds value by explaining tool purpose.
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 gets a Stripe Checkout URL for adding/updating credit cards, with specific context of when to use (after registration, on 402 errors). It distinguishes itself from siblings like register and call_api.
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?
Explicitly states it is required after registration and on 402 errors. It also describes user action needed. Lacks explicit 'when not to use' but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspectList all available API categories on Zyla API Hub with the real count of public APIs in each. Use the category names returned here as the "category" parameter in browse_catalog to filter APIs by category. Example categories: Sports, Finance, Data, Science, etc.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states that the tool lists categories with counts, which is a safe read operation. It does not disclose any additional behavioral traits like authentication or rate limits, but for a simple list tool this is adequate.
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: first describes the action and result, second provides usage guidance and an example. No wasted words, front-loaded with key information.
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 zero parameters, no output schema, and a simple purpose, the description is complete. It covers what the tool does and how its output is used, which is sufficient for an agent.
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?
There are no parameters, so baseline is 4. The description adds value by explaining how the output (category names) is used in browse_catalog, though it does not need to describe parameters.
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 verb 'List all available API categories' with specific resource 'categories on Zyla API Hub' and includes the added detail of 'real count of public APIs in each', which distinguishes it from siblings like browse_catalog.
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?
It explicitly instructs to use the returned category names as the 'category' parameter in browse_catalog, providing clear context for when to use this tool. It does not mention when not to use it, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
registerAInspectRegister a new Zyla API Hub account directly from the AI agent. Returns an API key AND a payment_url. The user MUST open the payment_url in their browser to add a credit card before making any API calls. Billing model: pay-per-successful-request with no monthly fees or minimums. Each API has its own per-call price. The credit card is only charged at the end of the billing cycle for actual usage. No authentication required to call this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name of the user | |
| Yes | Email address (must be unique) | ||
| password | Yes | Password (min 8 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: returns API key and payment_url, requires user action, explains billing model (pay-per-request, no monthly fees), and states no authentication needed. This is comprehensive for a registration 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?
The description is five sentences, each adding value: purpose, return values, required user action, billing model, and authentication. It is front-loaded with the core purpose and is succinct without waste.
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?
The description covers all essential aspects: purpose, output, user steps, billing, and prerequisites. For a registration tool with no output schema or annotations, it provides complete context for an AI agent to use 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?
Schema coverage is 100%, so the description does not need to add parameter details. The description adds no extra meaning beyond the schema, which adequately describes name, email, and password.
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 registers a new Zyla API Hub account, using a specific verb and resource. It distinguishes from sibling tools like account_status or get_payment_url, providing a unique purpose.
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 explicit instructions: user must open payment_url to add a credit card before making calls. It explains billing and that no authentication is needed, giving clear context for use. It lacks explicit when-not-to-use, but implies it for existing accounts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_catalogAInspectSemantic search across ALL 10,000+ public APIs on Zyla API Hub. Natural-language queries work best (e.g. "validate an email address", "get stock prices"). Returns matching APIs ranked by relevance with their ID, name, description, category, price per successful call (USD), average latency (ms), and endpoints summary.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default: 10) | |
| query | Yes | What you need, in natural language (e.g. "validate an email address", "currency conversion", "weather forecast by city") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a read operation via 'search' but does not explicitly disclose safety, auth requirements, or rate limits. Adds return field details but lacks explicit behavioral traits.
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 with no wasted words: first sentence states core purpose, second gives query guidance and result fields. Front-loaded and efficient.
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 details return fields (ID, name, description, category, price, latency, endpoints). Lacks pagination or error handling, but sufficient for a search tool with well-documented parameters.
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%, baseline 3. Description adds value by emphasizing natural-language queries and providing examples for the query parameter, and explains what the results contain, which is not in schema.
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 performs semantic search across all 10,000+ public APIs on Zyla API Hub, distinguishes from siblings like browse_catalog and get_api_info by specifying scope and query type.
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?
Provides context that natural-language queries work best and gives examples, but does not explicitly state when not to use this tool or mention alternatives beyond implied sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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