Search a Ukrainian catalog of 21,000+ AI tools — search tools, get details, list categories.
Get full details for one AI tool by its slug (as returned by search_ai_tools).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Tool slug, e.g. 'notion-ai'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully convey behavioral traits. It states the tool retrieves full details but does not mention any side effects, authentication needs, or limitations. The description is straightforward but lacks depth on expected behavior or return characteristics.
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 sentence of 13 words, immediately stating the purpose and key detail (slug origin). No unnecessary words; every phrase 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?
For a simple get-by-identifier tool with one parameter and no output schema, the description is complete. It specifies the input (slug), how to obtain it (from search_ai_tools), and the output (full details). The sibling tools are listed, providing additional context.
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 100% of the parameter with description 'Tool slug, e.g. 'notion-ai'.' The description adds value by connecting the slug to 'search_ai_tools', providing operational context. This extra information helps the agent understand the parameter's origin, making semantics clearer than schema alone.
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 'Get' and the resource 'full details for one AI tool', specifying identification by slug. It references the sibling tool 'search_ai_tools' to clarify how the slug is obtained, effectively distinguishing itself from the 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?
The description implies when to use this tool: after obtaining a slug from 'search_ai_tools'. It provides context on the slug's origin but does not explicitly state when not to use it or give alternative tools for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all AI-tool categories available in the catalog.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It states that the tool lists all categories, but does not mention any side effects, authentication requirements, or performance considerations. For a simple read-only list with no parameters, this is adequate but lacks depth.
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, clear sentence that immediately states the tool's purpose. It is front-loaded with no unnecessary words, earning 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?
For a simple listing tool with no output schema, the description adequately explains what it does. It might benefit from mentioning the format or structure of the returned categories, but given the simplicity, the description is sufficiently 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?
There are no parameters, and the schema coverage is 100% (empty). The description adds meaning by specifying that it lists categories, which is appropriate for a parameterless tool. The baseline for zero parameters is 4, and the description meets that.
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 lists all AI-tool categories, which is a specific verb-resource combination. It distinguishes from siblings: 'get_tool' likely retrieves a single tool, and 'search_ai_tools' allows filtering, while this tool provides a comprehensive list of categories.
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 implicitly suggests using this tool when you need a complete list of categories. It does not explicitly exclude alternatives or mention when not to use it, but the context is clear given the sibling tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the AICatalog directory of 20,000+ AI tools by keyword. Returns matching tools with name, tagline, catalog URL and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-25). | |
| query | Yes | Search keywords, e.g. 'video editing'. |
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 return fields (name, tagline, catalog URL, pricing) but does not disclose ordering, pagination, or potential limitations like partial matches or rate limits.
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 concise sentences with no wasted words. The purpose and return values are front-loaded.
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 search tool with two parameters and no output schema, the description adequately covers what the tool does and what it returns. Missing details like pagination are not critical for basic understanding.
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% with both parameters having descriptions. The tool description does not add semantics beyond the schema, so baseline of 3 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 the tool searches an AICatalog of 20,000+ AI tools by keyword and returns specific fields. It effectively distinguishes from siblings (get_tool, list_categories) by focusing on keyword-based search.
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 for keyword searching and explicitly states what is returned. However, it does not explicitly contrast with alternatives like get_tool for specific tool details or list_categories for browsing by category.
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
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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Search 2,000+ AI tools: pricing, alternatives, comparisons, and live, dead or acquired status.
Search, compare, and find alternatives across a curated catalog of 1,100+ AI tools.
Public Data Ukraine Mcp connects AI agents to real public APIs via MCP. Tools include
Query the HokAI catalogue of AI tools, agents, models, companies and infrastructure services.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: search returns a list, get details a single item, and list categories provides taxonomy. No overlap.
All tools follow a consistent verb_noun pattern in snake_case: search_ai_tools, get_tool, list_categories.
Three tools are perfectly scoped for a read-only AI tool catalog: search, detail retrieval, and category listing. No excess or deficiency.
The surface covers the essential operations for browsing a catalog: searching, viewing details, and navigating categories. No missing CRUD that would hinder an agent.
