MCP Server Cards

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: discover fetches a card from a URL, generate_card creates a card JSON, register adds to index, schema retrieves schema, search queries index, and validate_card validates a card. No overlap.
Naming Consistency3/5
Naming mixes styles: verb-only (discover, register, search), verb_noun (generate_card, validate_card), and noun (schema). While readable, it lacks a consistent pattern, such as all verb_noun.
Tool Count5/5
Six tools cover the core operations for server card management: discovery, generation, registration, validation, schema retrieval, and search. This is well-scoped and neither too few nor too many.
Completeness4/5
The tools cover the main lifecycle: create (generate_card + register), read (discover, search), and validate. Missing update or delete operations, but these are minor gaps for the server's purpose.
Average 3.5/5 across 6 of 6 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states it searches by keyword but provides no information about the return format, pagination, scope, or any limitations. No annotations exist to compensate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, but the bilingual repetition adds redundancy. The structure is simple, but it wastes space with translation that may not be needed for an English-speaking AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not specify the output or the scope of the search. It does not indicate whether it searches across all servers or within a context. The presence of 'discover' as a sibling suggests another search-like tool, but no differentiation is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description merely repeats the parameter name 'query' and provides a redundant translation. It does not specify format, allowed values, or examples. Schema coverage is 0%, but the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the verb 'search', the resource 'indexed server cards', and lists the fields searched (name, description, categories, tool names). This provides a clear purpose, though it does not explicitly contrast with sibling tools like 'discover'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks any guidance on when to use this tool versus alternatives, such as when to use 'discover' instead. It does not specify prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so description must disclose behavior. It states creation of a file but omits side effects like overwriting, permissions, or error conditions. No mention of output location or success/failure signals.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short paragraphs plus a bullet list of parameters. No wasted words, but could be more front-loaded with key purpose. The German sentence is redundant but not harmful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 10 parameters and no annotations, the description lacks behavioral context (side effects, prerequisites). Output schema exists but return values still require understanding. Missing guidelines on when to invoke.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must explain parameters. It lists all 10 parameters with brief comments, but most are translations or minimal definitions. Adds format hint for 'tools' but overall meaning is shallow, not deep semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a .well-known/mcp-server-card.json for MCP servers, with specific verb 'generate' and resource 'server card'. It distinguishes from siblings like discover or validate_card by focusing on creation of metadata file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like discover or validate_card. The description mentions agents use the card for discovery but does not compare or provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It discloses that the tool adds or updates a card if the name exists, which implies idempotency-like behavior. However, it omits details on return value, side effects, or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and includes both English and German, but the dual language adds minimal overhead. The purpose is front-loaded. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-param tool with output schema, the description covers the basic operation. However, it lacks context on when to use this vs other tools and does not explain the output or error conditions. Adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description reads 'Args: card_json: JSON-String der Server Card', explaining the parameter format and purpose. This adds beyond the schema (which only shows type string), but lacks depth on valid JSON structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Register a server card in the local index' and explains it adds or updates with the same name. This distinguishes it from siblings like 'generate_card' (which likely creates a new card from scratch) and 'search' (which retrieves).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs siblings like 'discover', 'search', or 'validate_card'. Lacks context such as prerequisites (e.g., a server must exist) or when updating is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description indicates it's a fetch operation (checking a file) but does not disclose idempotency, auth needs, or whether it is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise with front-loaded English, though mixed language slightly reduces clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one parameter; output schema exists (not shown) so return value coverage is likely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the url parameter by specifying it as the base URL with an example, compensating for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it discovers an MCP server card from a URL via .well-known, distinguishing it from siblings like search and validate_card.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies use when you have a server URL to check for a card, but no explicit when-not-to-use or alternative guidance given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the tool as a read-only schema retrieval with no mention of side effects, auth needs, or rate limits. While functional, it misses opportunity to disclose benign traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded, delivering core information in English and German. The bilingual text adds minor redundancy but does not significantly detract from conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (no params) and presence of an output schema, the description is fully adequate. It clearly states the tool's output and purpose without needing additional detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so schema coverage is 100%. The description adds meaning by explaining the schema's purpose (fields and requirements), exceeding the baseline of 4 for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the JSON schema for MCP server cards, specifying the verb 'get' and the resource. It distinguishes itself from sibling tools (e.g., discover, validate_card) by focusing on schema definition rather than card operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (before creating or validating cards) but lacks explicit guidance on when to use this tool versus alternatives like search or discover. No when-not or alternative references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states that the tool validates against a schema, checks required fields, and yields warnings for missing recommended fields. This adequately discloses the read-only nature of validation, though it does not detail output structure (but output schema exists).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with an English lead sentence followed by German explanation. It is front-loaded and avoids unnecessary details. The Args section is minimal but effective. Slight redundancy due to bilingual text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and the existence of an output schema, the description covers the core behavior well. It explains the validation logic and warnings. It could mention error handling or strictness, but overall is sufficiently complete for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description explicitly defines the parameter 'card_json' as a JSON string of the server card. This fully compensates for the missing schema description and provides sufficient clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates a server card JSON against a schema, checking required fields and giving warnings for recommended fields. The verb 'validate' and resource 'server card JSON' are specific, and it distinguishes itself from sibling tools like 'generate_card' (creation) and 'search' (discovery).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks explicit guidance on when to use this tool versus alternatives. It only describes the functionality without stating prerequisites, exclusions, or when not to use it. However, the purpose is clear enough that an agent can infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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