Marketplace of pay-per-call APIs for AI agents. Top up once with USDC, then call any listed API - gasless, walletless, metered per request. Includes onboarded providers and hundreds of x402-native APIs discovered on-chain.
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 4.3/5 across 9 of 9 tools scored. Lowest: 3.7/5.
Each tool has a clearly distinct purpose: balance checking, service discovery (list/search/get), paid calls (internal/external), content reading, and credit top-up. Overlaps like list_services vs. search are clearly differentiated by how they are used (browse vs. filter).
All tools follow a consistent `apihub_verb_noun` pattern in snake_case. Verbs like get, list, search, call, topup are paired with appropriate nouns, and modifiers like 'external' are used uniformly.
With 9 tools, the server is well-scoped for its purpose as an API marketplace. Each tool covers a necessary part of the workflow—discovery, payment, invocation—without unnecessary duplication or missing essentials.
The tool set provides a complete lifecycle: service discovery (list, search, get), credit management (balance, topup), and API invocation (call, call_external, read_content). No obvious gaps for the intended use case.
apihub_balanceAInspectRead-only. Returns your current APIHub credit balance (in microdollars and USD), total lifetime spending (microdollars and USD), and total completed request count. Requires a valid API key. Use before apihub_call or apihub_call_external to confirm sufficient funds for a paid request, or periodically to audit usage. Does not modify state, send payments, or call upstream APIs; for top-ups use apihub_topup.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: read-only, no state changes, no payments, no upstream calls. 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?
Two sentences, front-loaded with 'Read-only.' No wasted words, efficiently conveys all necessary 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?
Despite no output schema, description fully explains what the tool returns and mentions required API key. Complete for a zero-param tool.
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; description adds value by detailing the response contents (balance in microdollars and USD, lifetime spending, request count). Exceeds 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?
Clearly states it returns credit balance, lifetime spending, and request count. Distinguishes from sibling tools like apihub_call and apihub_topup by specifying when to use each.
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 tells when to use (before paid requests), what it does not do (no modifications, no payments), and names alternatives (apihub_topup for top-ups).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_callAInspectSends payment. Calls a paid endpoint on an onboarded APIHub service. Debits the endpoint's price from your credit balance and forwards the request to the upstream provider. Returns an object with the upstream response body, HTTP status, and credits_charged_microdollars. Requires a valid API key and sufficient credit balance; if balance is insufficient the call returns a 402 with payment requirements (use apihub_topup to add credits, apihub_balance to check). Use this for services already onboarded to APIHub (find slugs via apihub_search or apihub_list_services); use apihub_call_external for arbitrary x402 URLs not onboarded here, or apihub_read_content for content gateways.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Optional. Request body as a JSON string for POST/PUT. Ignored for GET/DELETE. The proxy forwards this verbatim with Content-Type: application/json. | |
| method | No | Optional HTTP method, default GET. Must match the method declared on the endpoint or the request will fail. | |
| service_slug | Yes | Required. The service slug as returned by apihub_search or apihub_list_services, e.g. 'exchange-rates' or 'weather'. | |
| endpoint_path | Yes | Required. The endpoint path including any leading slash, e.g. '/latest/USD' or '/v1/forecast'. Get valid paths from apihub_get_service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses debit of credits, forwarding request, return object with upstream response, HTTP status, credits_charged_microdollars, and error condition (402). No contradictions since no annotations provided.
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?
Description is front-loaded with purpose, then behavior, return, error handling, and alternatives. Each sentence adds value, though slightly long; justified by completeness.
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?
Covers all aspects: purpose, usage, behavior, parameters, return, error handling, and alternatives. No output schema needed given detailed description.
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?
Adds meaning beyond schema by explaining body ignored for GET/DELETE, method must match endpoint, and provides examples for service_slug and endpoint_path. 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?
The description clearly states the tool sends a payment and calls a paid endpoint on an onboarded APIHub service. It distinguishes from siblings like apihub_call_external and apihub_read_content.
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 (onboarded services) and when not (use apihub_call_external for arbitrary x402 URLs, apihub_read_content for content gateways). Also covers prerequisites (API key, credit balance) and error handling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_call_externalAInspectCall an external x402-protected URL (any provider in the marketplace or any x402 API). APIHub pays the provider on your behalf using the platform wallet and debits your credit balance for the exact amount. No wallet or gas required.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The full URL to call (e.g. https://hub.atxp.ai/...) | |
| body | No | Request body (object or string). Omit for GET. | |
| method | No | HTTP method (default POST) | |
| headers | No | Additional request headers. Do not set Authorization or X-PAYMENT - handled automatically. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description provides key behavioral details: payment handled via platform wallet, credit balance debited, no wallet/gas needed, and automatic header handling (do not set Authorization/X-PAYMENT). This fully discloses important 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 that are front-loaded with the core purpose. No unnecessary words. 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?
The description covers the payment model and parameter constraints adequately for the tool's complexity. It could optionally mention error behavior or usage limits, but overall it is sufficient for an agent to understand the tool's function.
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. The description adds some value by reiterating header restrictions and payment context, but it largely restates schema fields. It does not add new meaning 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?
The description clearly states the tool calls an external x402-protected URL and handles payment on behalf. However, it doesn't differentiate from the sibling tool 'apihub_call' which might have similar functionality, so more explicit distinction would improve clarity.
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 says to use for external x402-protected URLs and any provider/marketplace API. It does not specify when not to use it (e.g., for internal calls) or mention alternatives, leaving some ambiguity about context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_get_serviceAInspectGet full details for a specific API service including all endpoints, schemas, and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| service_slug | Yes | The service slug (e.g., 'exchange-rates') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes a read operation ('Get full details') but does not discuss side effects, authorization, rate limits, or return format. The mention of endpoints, schemas, and pricing gives some insight, but more context on behavior would be beneficial.
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, front-loaded sentence that conveys the tool's purpose without wasted words. Every part of the sentence adds value, and it is easy 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?
For a simple retrieval tool with one parameter and no output schema, the description is fairly complete. It specifies the type of details returned (endpoints, schemas, pricing). However, additional specifics about the return structure or potential pagination could improve completeness, though not strictly necessary.
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% (the parameter 'service_slug' has a description with an example). The tool description adds context about what the tool returns (endpoints, schemas, pricing) but does not provide additional meaning about the parameter itself. With high coverage, baseline 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 verb 'Get', the resource 'specific API service', and the scope 'full details including all endpoints, schemas, and pricing'. It distinguishes from sibling tools like apihub_list_services, which lists services rather than getting 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?
The description implies that this tool is for retrieving full details of a specific service, but it does not explicitly state when to use it versus alternatives like apihub_list_services or apihub_call. No when-not-to-use guidance or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_list_servicesAInspectRead-only. Lists onboarded APIHub services alphabetically, returning each service's slug, name, description, category, provider, endpoint count, and lowest per-endpoint price in microdollars. No authentication required. Use this to browse the full onboarded catalog when you don't have a specific capability in mind; prefer apihub_search when filtering by query, category, or price. Does not include external x402 APIs (use apihub_search_external for those) and does not return endpoint-level details (use apihub_get_service for that).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional max number of services to return. Default 20, minimum 1, hard cap 100. Values above 100 are clamped. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses read-only nature and no authentication required. Describes clamping behavior for limit parameter. States what is not included (external x402 APIs, endpoint details).
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?
Three sentences, front-loaded with action and key details. No redundant information. Efficient use of 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?
For a simple list tool with one optional parameter and no output schema, the description is comprehensive, covering purpose, return fields, usage guidance, and behavioral constraints.
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 covers limit parameter fully (100% coverage). Description adds clamping behavior beyond schema, which is valuable for correct usage.
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 it lists onboarded APIHub services alphabetically with specific return fields. Distinguished from siblings (apihub_search, apihub_search_external, apihub_get_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?
Explicitly provides when to use (browsing full catalog without specific capability) and when not to use (filtering, external APIs, endpoint details). Names alternatives: apihub_search, apihub_search_external, apihub_get_service.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_read_contentAInspectRead web content through a paid content gateway. Returns clean, structured text extracted from the URL. Use this for content services (service_type = 'content').
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The full URL to read (must match a verified domain on the service) | |
| service_slug | Yes | The content service slug |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits. It mentions the tool is 'paid', hinting at cost implications, but does not specify how charges work, whether authentication is needed, or rate limits. The description also lacks details on what happens if the URL is not verified or if the request fails. Overall, adequate but incomplete.
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 at two sentences. It front-loads the core purpose and usage condition, with no redundant or extraneous 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 the tool's simplicity (2 params, no output schema), the description covers essential aspects: what it does, what it returns, and when to use it. It could be improved by specifying the return format (e.g., plain text, markdown) and error handling, but is largely complete for an agent's 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?
Schema description coverage is 100%, so the input schema already documents both parameters well. The description adds context ('Returns clean, structured text') but does not provide additional semantics for the parameters beyond what is in the schema. The phrase 'service_type = 'content'' indirectly relates to service_slug, but this is already clear from the 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 action ('Read web content'), the resource ('through a paid content gateway'), and the result ('clean, structured text'). It distinguishes from sibling tools by specifying it is for 'content services (service_type = 'content')', which differentiates it from search, balance, and other API tools.
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 a direct usage condition: 'Use this for content services (service_type = 'content')'. This helps the agent decide when to invoke this tool. However, it does not explicitly state when not to use it or mention alternative tools for other service types, which would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_searchAInspectRead-only. Searches onboarded APIHub services by free-text query, with optional category, price, and type filters. Returns up to 10 matches ranked by uptime and endpoint count, each with slug, description, endpoints array, min price in microdollars, provider name, and quality score. No authentication required. Use this when you need to find an API by capability; use apihub_list_services to browse without a query, apihub_search_external to include the external x402 catalog, or apihub_get_service when you already know a slug. Does not call any upstream API or debit credits.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Optional filter. 'api' = standard REST APIs, 'content' = content gateways that proxy a fixed upstream URL. | |
| query | Yes | Required free-text query matched against service name and description (case-insensitive substring match). Use 1-3 keywords describing the capability you want, e.g. 'weather' or 'stock price'. | |
| category | No | Optional exact-match filter. Valid values: ai, data, search, finance, media, infra, communication, content, travel. | |
| max_price_microdollars | No | Optional upper bound on price per request in microdollars (1 USD = 1,000,000 microdollars, so 10000 = $0.01). Services whose cheapest endpoint exceeds this are excluded. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description covers safety (read-only), authentication (none), and side effects (no upstream calls, no credits). It also explains ranking criteria and return fields. Missing details on rate limits or pagination beyond 'up to 10 matches'.
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 paragraph but logically flows from purpose to inputs, output, auth, and usage context. It is informative without redundancy, though could be slightly more structured with bullet points.
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 adequately lists return fields and ranking. It covers essential context like no auth and no credit debit. Missing details on error handling or empty results, but overall sufficient for a search tool.
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 covers 100% of parameters with descriptions, and the description adds practical context: query is case-insensitive substring, max_price example conversion, and type enumeration explanation. This enhances understanding beyond the 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 verb 'searches' and the resource 'onboarded APIHub services', and specifies the free-text query with optional filters. It distinguishes from sibling tools by naming alternatives, making the purpose unmistakable.
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 explicitly advises when to use this tool ('when you need to find an API by capability') and when to use alternatives (browse without query, include external catalog, or get by slug). This provides clear decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_search_externalAInspectSearch external x402-protected APIs (not operated by APIHub, but callable via credits). Returns listings with endpoint counts, prices, and on-chain activity.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10, max 50) | |
| query | No | Search text matched against name/description | |
| category | No | Filter by category (ai, search, finance, media, other) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds context about x402 protection and credit usage. However, it doesn't disclose rate limits, error behavior, or whether it's read-only. Additional behavioral details would be beneficial.
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, front-loaded with key information. No unnecessary words, 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?
Adequately covers purpose and return fields for a search tool with 3 optional parameters and no output schema. Could mention pagination or sorting, but overall sufficient.
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 parameter descriptions. The description adds minimal extra meaning beyond the schema (e.g., 'matched against name/description' for query). Baseline 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?
Clearly states the tool searches external x402-protected APIs, distinguishes from internal APIs, and lists return fields (endpoint counts, prices, on-chain activity). Differentiates from sibling apihub_search likely for internal APIs.
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 on when to use (external APIs callable via credits), implicitly contrasting with internal APIs. Lacks explicit when-not or alternative suggestions but sibling names imply differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apihub_topupAInspectPurchase APIHub credits via x402 (USDC on Base). Returns payment instructions including a web URL for browser-based payment, a CLI command, and raw x402 requirements for agents with wallet support. Credits are added to your account instantly once payment confirms on-chain.
| Name | Required | Description | Default |
|---|---|---|---|
| amount_dollars | Yes | Amount to top up in USD. Minimum $5.00, maximum $10,000 per call. Example: 10 for $10. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description discloses return of payment instructions and instant credit addition, but lacks details on failure behavior, idempotency, or rate limits.
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, front-loaded with purpose, no waste. However, could be slightly more structured to separate payment methods.
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?
Describes return value comprehensively (web URL, CLI command, raw x402 requirements) despite no output schema. Adequate for a payment tool.
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%. Description repeats minimum/maximum amounts from schema and adds an example, providing marginal extra clarity beyond the 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 action ('Purchase APIHub credits via x402') and the resource (APIHub credits). It distinguishes from sibling tools like apihub_balance or apihub_call by focusing on credit top-up.
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 (when you need credits), but no explicit guidance on when to use vs alternatives, or prerequisites like needing a wallet or USDC balance.
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
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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
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