Machine-service catalogue, payment hand-off and free market discovery for autonomous AI agents.
Get the machine-readable definition of one live Agent Shop service by catalogue slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It clearly signals a read operation and a 'machine-readable' return, but it does not disclose error behavior, authentication needs, or whether the slug must exactly match a live catalogue entry. This is adequate for a simple getter but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with no filler. The core action and resource are front-loaded, and every word adds meaning.
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?
With a single parameter and an output schema present, the description covers the essential task. It is slightly incomplete around alternatives and error handling, but for a low-complexity read operation the provided context is nearly 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?
The schema has a single required parameter with no description (0% coverage), so the description must compensate. The phrase 'by catalogue slug' gives the parameter its selection semantics, though it does not provide slug format or lookup examples.
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 names a specific verb ('Get'), a specific resource ('machine-readable definition of one live Agent Shop service'), and the selection key ('by catalogue slug'). This clearly distinguishes the tool from siblings like invoke_service and list_services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the main use case: when you have a catalogue slug and need the service definition. It does not explicitly name the alternatives or state when not to use it, so it stops short of a 5, but the context is clear enough for routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prepare a paid invocation of one live Agent Shop service. Returns the exact x402 HTTP request and price; it does not execute the service or bypass payment.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| inputs | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the tool returns 'the exact x402 HTTP request and price' and explicitly states it does not execute or bypass payment, which clarifies side effects. It does not address authentication, payment timing, or request expiration, leaving some gaps.
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 brief, front-loaded sentences deliver the core purpose and key clarifications with no filler. Every sentence earns its place.
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 relatively simple and has an output schema, and the description covers the core purpose and non-execution behavior. However, with 0% parameter coverage and no annotations, practical details such as how to obtain the slug, how the returned request should be used, and whether payment is captured remain unstated.
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 0%, and the description does not explain that 'slug' identifies the service or that 'inputs' contains the service arguments. The description only weakly implies a service context, so it does not compensate for the schema gap.
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 uses a specific verb ('Prepare') and resource ('paid invocation of one live Agent Shop service'), and clarifies that it does not execute the service. This makes the tool's purpose distinct from execution-oriented or read-only siblings.
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 provides context that the tool prepares rather than executes and does not bypass payment, but it never names alternatives like get_service, list_services, or market_service. The agent must infer when to use this tool versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List services currently available from the Atinamos Agent Shop.
| 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'List' implies a read-only operation and 'currently available' adds temporal context, but there is no mention of output format, pagination, filtering, freshness guarantees, or whether results reflect live availability.
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 with no filler. It immediately conveys the action and scope, making it optimally concise for a zero-parameter list tool.
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, parameterless listing operation with an output schema, the description is largely sufficient. The main gap is lack of differentiation from sibling tools, but an agent can safely invoke this tool without additional parameter or return-value guidance.
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, so parameter-level documentation is unnecessary. The empty schema and schema description coverage of 100% make this a non-issue, and the baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('List') and resource ('services currently available from the Atinamos Agent Shop'). It is distinguishable from get_service and invoke_service by the explicit listing scope, though it does not explicitly contrast with those 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?
The description implies this is for discovering available services, but provides no guidance on when to choose this over get_service, invoke_service, market_search, or market_service. No exclusions or routing hints are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search machine services observed by Atinamos harvesters across external discovery sources. Results are observational evidence, not an endorsement, trust score or guarantee of current availability.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It usefully warns that results are 'observational evidence, not an endorsement, trust score or guarantee of current availability,' which is valuable context about data reliability. However, it omits other behavioral details such as whether the operation is read-only, how results are ordered, or potential failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-constructed sentence that front-loads the core purpose and then adds an important caveat. There is no filler or redundant information; every clause contributes meaning.
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 tool with an output schema, the description covers the core purpose and result semantics adequately. It is incomplete, however, because it lacks usage guidance relative to sibling tools and provides no parameter clarification, leaving the agent to infer query semantics and selection criteria.
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 0%, so the description must compensate for undocumented parameters. It does not explain the meaning of 'query' beyond the general search verb, nor does it clarify how 'limit' behaves. Since the schema provides no descriptions and the tool description adds no parameter-level detail, this is a notable gap.
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 specifies a clear action ('Search'), a specific resource ('machine services observed by Atinamos harvesters'), and a scope ('across external discovery sources'). It clearly conveys what the tool does and the external nature of the data source, though it does not explicitly contrast itself with sibling tools like list_services.
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 gives no guidance on when to use market_search instead of get_service, invoke_service, list_services, or market_service. It implies this is for discovery-style lookup, but it never states exclusions or alternatives, leaving the agent to infer proper selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get one observed machine service from the live Atinamos market registry by its canonical Atinamos service ID.
| Name | Required | Description | Default |
|---|---|---|---|
| service_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It indicates a read operation ('Get') but does not explicitly state that it is non-destructive, nor does it mention potential failure modes (e.g., ID not found), rate limits, or whether the data is cached. The phrase 'live registry' hints at real-time data but lacks detail. The absence of any side-effect disclosure makes this insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action and resource, then specifies the lookup key. Every word contributes meaning, with no fluff or redundancy. It is appropriately concise for a simple retrieval tool.
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 get-by-ID tool with an output schema already defined, the description covers the essential information: what it retrieves and by what identifier. It does not explain the output (handled by schema) or list edge cases, but given the simplicity and the presence of output schema, it is largely complete. A minor gap is the lack of usage context, but that falls under usage guidelines.
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 only defines 'service_id' as an integer with no description. The description compensates by clarifying that this is the 'canonical Atinamos service ID', which provides essential context about the parameter's origin and format. This goes beyond the bare schema and helps the agent understand what value to supply, though it could add more detail (e.g., whether the ID is global or scoped).
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 ('Get'), the resource ('one observed machine service'), the source ('live Atinamas market registry'), and the lookup key ('canonical Atinamos service ID'). It is specific and unambiguous. However, it does not explicitly differentiate from the sibling tool 'get_service', which may have a similar purpose, so it loses the top score.
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 no guidance on when to use this tool versus alternatives like 'list_services', 'market_search', or 'invoke_service'. It does not mention typical scenarios (e.g., when you already have an ID) or when to prefer a different tool. This leaves the agent to infer usage, which is a notable gap.
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.
get_serviceinvoke_servicelist_servicesmarket_searchmarket_serviceClaiming 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 addresses a distinct action: shop services, market observations, and paid invocation. get_service and market_service could be confused by name alone, but their descriptions clearly separate the live shop catalogue from the observed market registry.
Most tools follow a clear get_/invoke_/list_ pattern, and market_* is used consistently for registry tools. Minor inconsistency: list_services uses plural while the getter tools use singular, and market_search is verb-first rather than object-first.
Five tools is a well-scoped set for an agent shop: listing, retrieving, invoking shop services, plus two market observation tools. Each tool earns its place without redundancy or bloat.
The core shop workflow is covered—discover, inspect, and prepare paid invocation—alongside market discovery and retrieval. Actual payment settlement and execution are intentionally out of scope, so there are no major dead ends for the stated purpose.
