Remote MCP for 1,500+ APIs. Vault-managed credentials; OAuth or API key. Search, load, and execute.
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 4.2/5 across 4 of 4 tools scored.
The four tools have distinct purposes: search_apis discovers operations, load_execution_info fetches operation contracts, execute runs them, and list_credentials handles authentication info. There is mild overlap between search_apis and list_credentials (both reveal available APIs), but they serve different needs—discovery vs. credential access—so the boundaries are mostly clear.
All tool names follow a consistent lowercase_with_underscores convention. Three use verb_noun patterns (list_credentials, load_execution_info, search_apis) and one is a simple verb (execute), but the style is uniform and predictable. No mixing of conventions or vague names.
With 4 tools, the server is tightly scoped to the API execution workflow (search, inspect, execute, credentials). This is a reasonable count for the domain—not too thin to feel incomplete, and each tool earns its place without redundancy.
The core lifecycle is covered: search_apis discovers operations, load_execution_info provides full contracts, execute runs them, and list_credentials covers authentication. There are no major gaps for the stated purpose of remote API execution, though a tool to manage or create credentials might be a minor addition.
executeExecute OperationAUse this tool to execute the chosen action for the user using the provided details.
Always include inputs even if there are none.
To reduce context, use include_paths to return only the fields you need from the output.
DEPRECATION NOTICE: This tool is being deprecated on 20 September 2026. Execution is moving to self-hosted Jentic One. It still works for now but will stop working on that date. To keep running operations, install Jentic One (https://jentic.com/jentic-one) and execute through its local broker. For a step-by-step install and onboarding guide, fetch the 'skill://onboard-jentic-one' MCP resource.
Example operation UUIDs (from search_apis / load_execution_info): NewsAPI.org GET /everything: op_ba86fdce1bade1b7 NewsAPI.org GET /top-headlines: op_8ae297966b7f6da3 Finnhub.io GET /quote: op_f97a5db5e23f7b5a Coincap.io GET /v3/assets/"slug": op_e4a7f50d57fee805
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes | Dictionary containing: - uuid (str, required): The UUID of the operation to execute (e.g., "op_..." or "wf_...") - inputs (dict, required): Input parameters for the execution - include_paths (list[str], optional): Dot-notation paths to include from output (e.g. ["articles"] or ["results.*.title"] or ["parts.0.body.data"]). Defaults to full output. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and readOnlyHint=false, so the description does not need to repeat that this is a mutating operation. The description adds useful behavioral details like the requirement to always include 'inputs' even when empty, and the ability to 'include_paths' to reduce output size. However, it doesn't explain potential side effects, error handling, or rate limits, and the deprecation notice is more about lifecycle than behavior. Given the annotations cover the main safety profile, this is adequate but not exceptional.
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 longer than average due to the deprecation notice and example UUIDs, but each section serves a purpose. It is front-loaded with the main purpose, then usage tips, then deprecation, then examples. The structure is logical and not padded. It could arguably be condensed by moving deprecation to annotations, but it's not excessively verbose.
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 tool is complex with nested objects and an output schema, so the description doesn't need to explain return values. It covers how to obtain UUIDs (via search_apis/load_execution_info), the need for inputs, the use of include_paths, and the deprecation plan with migration instructions. It is fairly complete for a tool of this complexity, though it could benefit from mentioning that credentials are needed (since list_credentials is a sibling) or what happens on error.
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 covers 100% of the parameters (uuid, inputs, include_paths) with detailed explanations. The description adds a useful tip: 'Always include inputs even if there are none.' That goes beyond the schema and helps avoid errors. Since the schema already does most of the heavy lifting, the description's contribution is marginal but positive, so the 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 the tool's purpose: 'Use this tool to execute the chosen action for the user using the provided details.' It distinguishes itself from sibling tools (list_credentials, load_execution_info, search_apis) by being the execution step, and gives concrete example operation UUIDs sourced from those tools, making the tool's role 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 provides clear context for when to use the tool (when you have an operation UUID and inputs) and even includes deprecation guidance with an alternative (Jentic One). It indirectly suggests using search_apis/load_execution_info to obtain UUIDs, but does not explicitly state 'use this only after you have a UUID' or include explicit exclusions. Still, the guidance is useful and mostly clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_credentialsList credentialsAUse this tool to list all API credentials the authenticated agent has access to. Use this if a user asks "What APIs can I use?", "What credentials do I have access to?", "What apps are available to me?", "What APIs are available to me?" This is useful for debugging or to understand what API credentials are available to the agent.
DEPRECATION NOTICE: This tool is being deprecated on 20 September 2026. Execution is moving to self-hosted Jentic One. It still works for now but will stop working on that date. See https://jentic.com/jentic-one, or fetch the 'skill://onboard-jentic-one' MCP resource for a step-by-step install and onboarding guide.
Returns: dict: Response with success status and list of credentials { "result": { "success": True, "credentials": [c.model_dump(exclude_none=False) for c in credentials], } } On error: { "result": { "success": False, "message": "Failed to load credentials", } }
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: a deprecation notice, the scoping to credentials the agent has access to, and the return/error format. This goes beyond the annotations without contradicting them.
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 with the main purpose, includes example triggers, and provides a surprising amount of necessary deprecation context. It is slightly longer than minimal but each section earns its place; well-structured using a bulleted example list.
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 parameterless read-only tool, the description is complete: it covers purpose, appropriate usage, return format for both success and error, and deprecation. The annotations and output schema handle safety and response shape, leaving the description to add the remaining details.
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 tool has zero parameters, and the schema description coverage is 100%. There is no parameter information to convey, but the description specifies the return structure (success boolean and credentials array), which provides clarity even without 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 explicitly states the tool's purpose: 'list all API credentials the authenticated agent has access to.' It is specific (verb 'list', resource 'credentials') and distinguishes from siblings by focusing on credentials rather than API lookup or 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 provides clear when-to-use scenarios with example user queries ('What APIs can I use?' etc.) and states it is useful for debugging. It lacks explicit when-not-to-use or alternative tool references, but the examples and context are sufficient for a zero-parameter list tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_execution_infoLoad Execution InfoAUse this tool to get the full execution contract for a specific operation (its inputs, outputs, and authentication requirements). This tool must use the operation UUIDs returned from the search_apis tool.
DEPRECATION NOTICE: This tool is being deprecated on 20 September 2026.
Execution is moving to self-hosted Jentic One. It still works for now but
will stop working on that date. To load an operation's contract and run
it, install Jentic One (https://jentic.com/jentic-one) and use its
jentic inspect / jentic execute flow. For a step-by-step install and
onboarding guide, fetch the 'skill://onboard-jentic-one' MCP resource.
Example operation UUIDs (returned from search_apis tool): NewsAPI.org GET /everything: op_ba86fdce1bade1b7 NewsAPI.org GET /top-headlines: op_8ae297966b7f6da3 Finnhub.io GET /quote: op_f97a5db5e23f7b5a Coincap.io GET /v3/assets/"slug": op_e4a7f50d57fee805
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes | Dictionary containing: - operation_uuids (list[str] | str): UUID(s) of operation(s) to load (e.g., "op_...") |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds the deprecation timeline and the fact that it will stop working on a specific date, which is critical behavioral context beyond annotations. It also mentions it must use UUIDs from search_apis, which is a dependency. No contradiction with annotations.
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 well-structured with a clear purpose, deprecation notice, and examples. It is slightly longer than necessary but every part earns its place: the deprecation notice is critical, and the examples are helpful. The front-loading is good, with the main purpose stated first.
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 (one parameter, output schema present, annotations provided), the description is quite complete. It covers the purpose, usage, deprecation, and provides examples. The only minor gap is that it doesn't describe the output format, but the output schema exists, so that's not required. The deprecation notice adds important context for the 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?
The schema description coverage is 100% (the 'request' parameter is described as containing operation_uuids). The description adds example UUIDs and clarifies the format, but the schema already provides the key semantics. The description doesn't add much beyond the schema, so a 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 tool's purpose: to get the full execution contract for a specific operation, including inputs, outputs, and authentication requirements. It specifies the resource (operation contract) and the action (load), and distinguishes it from siblings by noting it must use operation UUIDs from search_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?
The description explicitly states when to use this tool (to get execution contract for an operation) and provides a deprecation notice with clear alternatives (Jentic One's jentic inspect/execute flow). It also gives example operation UUIDs to illustrate usage, which is excellent guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_apisSearch APIsAUse this tool to explore for available actions or information based on what the user wants to do (e.g., 'find Discord servers', 'send a message'). Use this to discover what APIs and operations exist in the Jentic catalogue.
Execution is moving to self-hosted Jentic One: to run a discovered operation, install Jentic One (https://jentic.com/jentic-one) and use its search -> inspect -> execute flow through the local broker.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes | Dictionary containing search parameters: - query (str, required): Search query string - max_results (int, optional): Maximum number of results to return (default: 5) - api_names (list[str], optional): Filter by specific API names |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds context about the tool's role (discovery only) and the transition to Jentic One, which informs the agent about execution flow. It does not contradict annotations and provides extra behavioral context beyond the structured fields.
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: three sentences that clearly state purpose, scope, and execution context. It is front-loaded with the main verb and resource, and every sentence serves a purpose without extraneous detail.
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, an output schema exists (covering return values), and the description covers purpose, usage, and a note about Jentic One. It does not explicitly describe what results look like, but the output schema handles that. The description is sufficiently complete for an agent to understand when and how to use the 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?
The schema covers all parameters with 100% coverage (query, max_results, api_names within the request object). The description only says 'Dictionary containing search parameters,' which adds minimal semantic value beyond the schema. Since schema coverage is high, the baseline is 3, and the description does not provide additional formatting, examples, or constraints for the 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 that the tool explores available actions/information based on user intent and discovers APIs/operations in the Jentic catalogue. It uses specific verbs like 'explore' and 'discover' with a clear resource scope, and it differentiates itself from sibling tools like execute by focusing on discovery rather than 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 provides clear context: use this tool to discover what APIs and operations exist, and if you want to run a discovered operation, use the Jentic One flow. It implies an alternative (execute is a sibling, but not named explicitly) and gives a workflow, though it could be more explicit about when not to use it (e.g., for execution).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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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