Verified discount & referral codes for crypto exchanges, cards and more — tested first-hand.
Returns the full verified-code record for one brand: the code, the benefit, how to apply it, last-verified date, availability notes, and the guide/review URLs. Match by brand name or slug (e.g. 'kraken', 'Bybit Card').
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Brand name or slug, e.g. 'kraken' or 'Bybit Card' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes what the tool returns in detail (code, benefit, apply, dates, URLs). No annotations provided, so description carries full burden; lacks mention of error handling or authentication, but adequate for the simple read operation.
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?
Single concise sentence that front-loads purpose, lists contents, and specifies matching method without 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?
For a simple tool with one parameter and no output schema, the description covers all essential information: what is returned and how to call it.
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?
Single parameter 'brand' is well-described with examples and matching strategy (name/slug). Schema coverage is 100%, and description adds practical context beyond the schema.
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?
Clearly states it returns the full verified-code record for one brand, listing specific fields (code, benefit, apply method, etc.) and distinguishes from siblings (list_brands, search_codes) by focusing on a single brand.
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?
Indicates matching by brand name or slug with examples. While it doesn't explicitly exclude when to use alternatives, the purpose is clear enough for an AI to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns every brand on find.codes with its category, current offer and code (if any). Optionally filter by category: crypto-exchange, crypto-card, hardware-wallet, trading-tools, travel-esim, creator-tools, dining, ai-tools.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter, e.g. 'crypto-exchange' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It transparently indicates the tool returns brands with category, offer, and code, implying a read operation. However, it does not explicitly state it is read-only or describe potential side effects, which is a minor 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?
Two sentences, no extraneous words. The purpose and filter options are stated upfront, making it easy to scan.
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 one optional parameter and no output schema, the description provides all necessary information: what is returned (brand, category, offer, code) and the available filter values. No 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 schema covers 100% of the single optional parameter with a description and example. The tool description adds value by enumerating all allowed category values, which goes beyond the schema's brief description.
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 returns every brand with its category, current offer, and code, using a specific verb ('returns') and resource ('every brand'). This distinguishes it from siblings like get_code which retrieves a single code, and search_codes which performs broader searches.
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 use for listing all brands with an optional category filter, but does not explicitly state when to use this tool over alternatives (e.g., get_code for a specific code). No when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free-text search across brands, categories and offers, e.g. 'stock perps discount', 'esim', '20% off exchange'. Returns matching verified-code records.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses it returns 'matching verified-code records' but does not mention pagination, limits, or exact response 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?
Two sentences, front-loaded with purpose and examples, no redundant information. Every word contributes.
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 simple schema (one parameter, no output schema), description adequately explains input domain and output type. Sibling tools help contextualize, but missing details like result count or language support.
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 covers the single 'query' parameter with basic description. The tool description adds semantic context with examples and scope ('brands, categories, offers'), which is valuable beyond schema.
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?
Description clearly states 'free-text search across brands, categories and offers', a specific verb+resource, and distinguishes from sibling tools 'get_code' (single record) and 'list_brands' (brands only).
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?
Provides explicit examples like 'stock perps discount', 'esim', '20% off exchange' illustrating use cases, but does not mention when not to use it or alternatives beyond siblings.
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_codelist_brandssearch_codesClaiming 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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Verified referral and discount codes for crypto exchanges, wallets and trading tools. Read-only.
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Compare cashback rates across portals and discounted gift cards for stores in 30+ countries.
Real-time crypto market data, funding rates, arbitrage and trading tools from 60+ exchanges.
Add one secure layer between your agents and this server.
Each tool serves a unique purpose: get_code retrieves a specific brand's code, list_brands enumerates all brands with optional filtering, and search_codes does free-text search. No overlap in functionality.
All tools follow a consistent verb_noun snake_case pattern (get_code, list_brands, search_codes), making them predictable and easy to understand.
With 3 tools, the server is concise and well-scoped for a code lookup service. It covers the essential query operations without excess, though adding a tool for direct code string lookup could be beneficial.
The tool set covers the main use cases: retrieving a specific code, browsing all codes, and searching. It lacks update/delete tools, but for a read-oriented API focused on finding codes, it is reasonably complete.