MCP server for the RPG-Schema.org definition and helping the usage of RPG-Schemas in TTRPG manuals
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 3.9/5 across 6 of 6 tools scored.
Each tool has a clearly distinct responsibility: adding, listing, inspecting, searching by class or property, and composing. There is no overlap between search_classes, search_properties, or inspect_ontology, as they target different aspects of ontology data.
All tool names follow a consistent verb_noun pattern (add, inspect, list, search), with the exception of 'compose' as a standalone verb, which is still clear and fits the domain. There is no mixing of case styles or vague verbing.
Six tools is within the ideal range for a domain-specific server, providing enough functionality to cover the core workflow without redundancy. Each tool contributes a distinct capability, making the set well-scoped.
The server covers the full expected lifecycle for ontology management and composition: adding ontologies, listing, inspecting, searching both classes and properties, and composing them into schema cards. There are no obvious dead ends or missing operations that would hinder an agent.
add_ontologyAInspectRegister a new baseline ontology by providing its TTL content. The ontology is saved to the catalog and becomes available for inspection, search, and composition.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| tags | No | ||
| label | No | ||
| description | No | ||
| ttl_content | 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 full burden. It states that the ontology is 'saved to the catalog' which indicates a persistent, write operation. But it does not disclose what happens on duplicate slugs, validation requirements for TTL, or potential side effects. The outcome is described, but edge-case behaviors are absent.
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 two succinct sentences that front-load the primary action and then state the outcome. Every word contributes to the core message with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has five parameters and no annotations, but the description only covers one parameter's semantics. While the output schema covers return values, the description lacks critical context for working with slug and metadata fields. It is adequate for an agent familiar with ontology cataloging but leaves gaps for a first-time caller.
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 only mentions 'TTL content' while ignoring required 'slug' and optional 'tags', 'label', and 'description'. The description does not explain that slug is likely a unique identifier or provide any constraints, so parameters beyond ttl_content are left undefined.
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 'Register' and resource 'baseline ontology' with the action of providing TTL content. It clearly distinguishes from sibling tools like compose, inspect_ontology, and search_* by indicating this is the tool for adding new ontologies to the catalog.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It establishes that the ontology becomes available for inspection, search, and composition, implying this tool should be used to initially add ontologies before those operations. However, it does not explicitly mention alternatives or exclusions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
composeAInspectCompose multiple baseline ontologies into a single schema card. Pass a list of ontology slugs. Returns a merged schema card ready to use as the starting point for extraction.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes | ||
| target_namespace | No |
| 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 must carry the full burden of behavioral disclosure. It only states that it 'returns a merged schema card,' but does not mention whether the operation is read-only, requires specific permissions, or has any side effects. This is a significant gap for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences, front-loaded with the action verb, and contains no fluff. Every sentence contributes essential information: what it does, what input to provide, and what to expect as output.
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 2-parameter tool with an output schema, the description covers the core function and the required parameter. However, it omits any mention of the optional 'target_namespace' and lacks behavioral context. Given the presence of an output schema and the tool's low complexity, the description is mostly complete but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the 'slugs' parameter by stating 'Pass a list of ontology slugs,' but it does not explain 'target_namespace' which appears in the schema with a default. With 0% schema description coverage, the description only partially compensates, leaving one parameter undocumented in prose.
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 and resource: 'Compose multiple baseline ontologies into a single schema card.' This distinguishes it from sibling tools like 'add_ontology' (which adds a single ontology) and 'inspect_ontology' (which inspects). The purpose is 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 use—'ready to use as the starting point for extraction'—implying a common workflow, but it does not explicitly mention when not to use it or name alternative tools. This is a clear context with no exclusions, meriting a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_ontologyBInspectInspect a baseline ontology: returns all classes, datatype properties, and object properties in schema-card format.
| 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?
With no annotations, the description carries the burden of behavioral disclosure. It clearly specifies the complete output (all classes, datatype properties, object properties in schema-card format), which is the primary behavior. The term 'Inspect' implies read-only, but the description does not explicitly confirm non-destructive behavior or mention any side effects, so it isn't a 5.
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 immediately states the action and outcome. It is front-loaded with the verb 'Inspect' and avoids any unnecessary words or repetition.
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 presence of an output schema reduces the need to explain return values, but the description still lacks essential context: the meaning of the required 'slug' parameter and any guidance on when to use this tool instead of siblings. For a tool with only one parameter and a clearly specified output, the description is decent but not fully complete.
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 defines a required 'slug' parameter with no description (0% schema coverage). The description does not mention 'slug' at all, so the agent has no information about what value to provide. The description fails to compensate for the complete lack of parameter documentation.
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 with a specific verb ('Inspect') and resource ('baseline ontology'), and explicitly lists what it returns (all classes, datatype properties, object properties). This distinguishes it from siblings like list_ontologies (which likely lists available ontologies) and search tools (which filter).
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 such as search_classes or search_properties. It does not mention any prerequisites or contextual cues, leaving the agent to infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_ontologiesAInspectList all registered baseline ontologies in the catalog.
| 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, the description must convey behavioral traits. It clearly implies a read-only listing operation, but it does not define what 'baseline' means, whether results are sorted, or what the output structure looks like. The output schema is available, which mitigates some ambiguity, but the description itself adds little beyond the basic action.
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 states the action and scope without unnecessary words or repetition. It is appropriately sized for a simple list-all 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?
Given the zero-parameter schema and presence of an output schema, the description is largely complete for a simple listing operation. It could be slightly enriched by noting when to use this vs. inspect_ontology, but that gap is minor and the core purpose is clear.
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 covers 100% of the (non-existent) parameters. The description correctly avoids inventing parameter details. Baseline of 4 is appropriate for a parameterless tool.
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 ('List') and identifies the exact resource ('all registered baseline ontologies in the catalog'). It clearly distinguishes itself from sibling tools like inspect_ontology (for inspecting a specific ontology) and search_classes/search_properties (for searching inside ontologies).
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 tool is for enumerating all baseline ontologies, but it does not explicitly state when to prefer this over alternatives, nor does it mention any exclusions or prerequisites. It is minimally viable but lacks explicit guidance on choosing between this and inspect_ontology or search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_classesAInspectSearch for classes across all registered ontologies. Matches class name or description (case-insensitive substring).
| Name | Required | Description | Default |
|---|---|---|---|
| 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 full burden. It discloses the matching logic (case-insensitive substring on name or description) and the global scope, which are key behavioral traits. However, it does not mention any limitations like pagination or sorting, though the output schema likely covers return structure.
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 two sentences, front-loaded with the main action, and every word adds value. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and the simplicity of the tool (one parameter), the description covers purpose, scope, and matching behavior. It does not discuss how to choose between this and search_properties, but that is more of a usage guideline concern. Overall, it is sufficiently complete for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only a 'query' string with no description, and schema coverage is 0%. The description implicitly explains the parameter via 'Matches class name or description', making it clear that 'query' is the search term. This compensates well for the schema gap, though it could be more explicit.
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 action (Search), the resource (classes), and the scope (across all registered ontologies). It also specifies the matching behavior (case-insensitive substring), which differentiates it from sibling search_properties.
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 on the search scope (all registered ontologies) and matching criteria, but does not explicitly mention when to use this tool over alternatives like search_properties. An explicit exclusion or pointer to the sibling would elevate it to a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_propertiesAInspectSearch for properties (datatype + object) across all ontologies. Matches property name, domain, range, or description.
| Name | Required | Description | Default |
|---|---|---|---|
| query | 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 carries the full burden. It discloses that the search matches property name, domain, range, or description, and that it operates across all ontologies. However, it does not disclose aspects like case sensitivity, wildcard support, result ordering, or any limitations, leaving room for ambiguity in behavior.
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 two sentences, front-loaded with the verb and resource, and each sentence adds relevant detail. There is no fluff, and the structure makes key information immediately accessible.
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 one-parameter search tool, the description covers the essential aspects: what is searched, scope, and match fields. The output schema exists, so return values are already defined. Minor details like search behavior nuances are missing but not critical given the low complexity, making the description fairly complete.
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. It indirectly clarifies the 'query' parameter by stating that matches occur on name, domain, range, or description, but it does not explicitly state that 'query' is the search string used for these matches. This partial compensation is adequate but not fully explicit.
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 'Search' with a specific resource ('properties') and scope ('across all ontologies'). It distinguishes itself from sibling tool search_classes by specifying property types (datatype + object) and listing match fields (name, domain, range, description), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one needs to search for properties, but does not explicitly state when to use it over alternatives like search_classes, nor does it mention exclusions. The context is clear enough for basic use, but it lacks explicit guidance on choosing between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
