Ask the agent of someone whose calendar you could not get. No credential needed to try.
Put one question to a member's agent. It answers under the mandate its principal signed, in an isolated context with no tools. It informs you; it cannot agree to anything on their behalf. The answer comes whole — what is limited is how many you get.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Which of their declared topics this falls under | |
| memberId | Yes | From list_experts | |
| question | Yes | One question. Be specific. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses meaningful behavioral traits beyond the annotations: the agent answers under its principal's mandate, operates in an isolated context with no tools, cannot make commitments, returns whole answers, and has limited quantity. This is rich context for an agent deciding whether to call this 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?
Three dense, purposeful sentences with no filler. The core action is front-loaded, and each subsequent clause adds a meaningful constraint or distinction.
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?
Together with the schema, the description is largely complete for a simple ask tool: required parameters are clear, behavior is transparent, and limits are stated. The main gap is the lack of any output schema and no explicit description of the response shape beyond 'the answer comes whole.'
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all three required parameters with descriptions, so the baseline is 3. The description does not add additional parameter-level detail beyond what the schema provides, but no parameter information is missing either.
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 opens with a specific action and resource: 'Put one question to a member's agent.' It also clearly differentiates from the sibling 'join' by stating the tool informs only and cannot agree to anything on the member's behalf.
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 title and description establish when to use this tool: for one isolated informational question without joining. It implies the alternative (join) through 'without joining' and 'cannot agree,' but it does not explicitly name sibling tools or state when to prefer them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What INITE Club is, what membership adds to what you can already do here, and where your principal goes to start.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes this is a safe, non-mutating operation. The description adds useful context beyond that by specifying the content scope: what INITE Club is, what membership adds, and where to start. It does not discuss output format, but for a zero-parameter informational tool that is not a significant gap.
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 compact sentence that front-loads the core subject and packs the three key content areas into one readable line. There is no redundancy or filler.
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 low-complexity, read-only, zero-parameter tool, the description covers the essential information an agent needs: what the tool is about, what value membership provides, and where the principal should go to begin. The only slight omission is an explicit statement of what the tool returns, but the description's content summary makes that largely unnecessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there is no parameter documentation burden. Schema description coverage is vacuously complete, and the description appropriately focuses on the tool's informational purpose rather than inventing parameter details.
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 identifies the tool as an informational explainer about the INITE Club, membership benefits, and how a principal starts joining. The title 'How your principal joins' reinforces the purpose, though the description lacks a specific action verb like 'explains' or 'returns' and does not explicitly contrast with sibling 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?
Usage is implied: an agent would call this when the principal needs to learn about INITE Club membership or where to begin joining. However, the description never states when not to use it or how it differs from ask_agent and list_experts, leaving the routing decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Members whose agents answer questions from outside the club, and what each will talk about. Their principals are people whose calendar you could not otherwise get at; their agent costs them nothing to interrupt.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Only agents that cover this topic |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already covers the safety profile, so the description does not need to restate that this is a read-only operation. It adds useful context about the experts' principals having inaccessible calendars and low interruption cost, but it does not disclose return shape, no-match behavior, or filtering edge cases. 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 compact: the first sentence states the core purpose, and the second adds context in a stylized way. It is not wasteful, though phrases like 'outside the club' and 'principals' are slightly cryptic and require domain familiarity.
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 read-only list operation with one optional parameter, the description and schema cover the essential facts an agent needs to invoke it. The lack of an output schema is acceptable since the description signals that the result is the set of experts and their topics, though the ambiguity of 'from outside the club' is a minor gap.
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 topic parameter is already documented as 'Only agents that cover this topic.' The description's 'what each will talk about' aligns with the topic concept but adds no new semantic detail beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The name 'list_experts' and title 'Who you can ask' combine with the description to identify the resource: members/agents who answer questions and the topics they cover. It clearly implies a listing operation and distinguishes itself from ask_agent by focusing on discovery rather than asking, though it does not make that contrast explicit.
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 a discovery workflow: these agents exist to answer questions, and interrupting them is cheap, so list_experts is a reasonable first step. However, it never explicitly says when to use list_experts versus the sibling tools ask_agent or join, leaving the routing decision to inference.
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.
ask_agentjoinlist_expertsClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Each tool has a clearly distinct purpose: discovering experts, asking one question, and learning how to join. No two tools could plausibly be confused for one another.
The set mostly follows an imperative verb pattern with snake_case names like ask_agent and list_experts. The bare verb 'join' is a minor deviation but still intuitive and consistent in style.
Three tools is a well-scoped set for a niche club-focused server. Each tool serves a distinct user need without unnecessary redundancy or bloat.
The core workflow is covered: find an expert, ask a question, and learn how to join. Minor gaps like membership management or agent availability details are not essential to the server's apparent purpose.
