Curated Amazon/Shopify/TikTok Shop changes (fees, policies, APIs) & upcoming seller deadlines.
List recent seller-facing changes on Amazon, Shopify and/or TikTok Shop, newest first.
Use for "what changed on TikTok Shop recently", "any new Amazon fees",
"Shopify API updates this month". Each change is curated from the
platform's official changelog/announcement and includes a summary, change
type, impact types, effective date and the original source URL.
Args:
platform: Optional platform slug — amazon, shopify or tiktok-shop.
Empty = all platforms.
limit: Max changes to return, 1-100 (default 10).
Returns {generated_at, source, platforms, total, changes:[{id, platform,
publishedAt, title, summary, changeType, topics, impactTypes, effectiveAt,
deadline, originalUrl}]}. Cite each record's originalUrl when presenting;
mention generated_at if freshness matters.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| platform | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it states changes are curated from official changelogs, includes specific fields like summary and impact types, and instructs to cite originalUrl when presenting and mention generated_at for freshness. This goes beyond annotations and provides actionable guidance.
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 well-structured and appropriately sized. It front-loads the core purpose in the first sentence, provides usage examples, then details parameters and return format in a clear, scannable manner. Every sentence adds value; no wasted words.
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 tool's moderate complexity (two optional params), the description is complete. It explains the output format in detail (fields returned), provides usage guidance, parameter semantics, and behavioral notes (citing originalUrl, freshness). The sibling tool's purpose is implicitly clear from the name; no further context needed.
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 provides two parameters (limit, platform) with defaults but no descriptions. The tool description compensates well: it explains platform is an optional slug (amazon, shopify, tiktok-shop) and empty means all platforms; limit is a max limit of 1-100 with default 10. This adds meaning beyond the bare schema, though the absence of enums in schema is mitigated by explicit slug 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 clearly states the tool lists recent seller-facing changes across three named platforms (Amazon, Shopify, TikTok Shop), newest first. It distinguishes itself from the sibling get_upcoming_deadlines by focusing on past changes rather than upcoming deadlines, and explicitly names the platforms, making 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 provides clear usage examples like "what changed on TikTok Shop recently" and "any new Amazon fees", effectively signaling when to use this tool. However, it does not explicitly state when not to use it or contrast with get_upcoming_deadlines, though the purpose naturally differentiates them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List upcoming seller-facing deadlines on Amazon, Shopify and/or TikTok Shop, soonest first.
Use for "any Amazon deadlines coming up", "what do TikTok Shop sellers
need to do before month end". Deadlines come from curated official
announcements (API sunsets, policy compliance dates, fee effective dates);
past deadlines are excluded server-side. Free-form deadlines ("rolling")
sort after dated ones.
Args:
platform: Optional platform slug — amazon, shopify or tiktok-shop.
Empty = all platforms.
Returns {generated_at, as_of, platforms, total, deadlines:[{id, platform,
deadline, title, summary, effectiveAt, originalUrl, ...}]}. `as_of` is the
server-side cutoff date used. Cite each record's originalUrl.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite already having readOnlyHint=true and destructiveHint=false in annotations, the description enriches transparency by detailing server-side exclusion of past deadlines, sorting behavior, and the 'as_of' field representing the server-side cutoff. It also instructs to cite each record's originalUrl. This goes well beyond the bare annotations.
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 well-structured: a one-line summary, practical usage examples, source context, then explicit Args/Returns sections. Every sentence adds value—no fluff. It front-loads the core purpose and packs detailed behavior into a compact format.
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 only one optional parameter and no output schema, the description must explain the return structure, and it does: a dict with generated_at, as_of, platforms, total, and deadlines array with key fields. It also covers edge cases like 'rolling' deadlines. For a read-only listing tool, this is 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 schema provides zero description for the 'platform' parameter (0% schema coverage). The description fully compensates by documenting it as 'Optional platform slug — amazon, shopify or tiktok-shop. Empty = all platforms.' and clearly explains the default behavior. This adds substantial meaning absent from 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?
The description opens with a specific verb+resource statement: 'List upcoming seller-facing deadlines on Amazon, Shopify and/or TikTok Shop, soonest first.' It clearly states the platforms and ordering, and the examples ('any Amazon deadlines coming up') cement the purpose. While it doesn't explicitly contrast with the sibling tool, its scope is precise and 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?
It provides concrete example queries ('what do TikTok Shop sellers need to do before month end') and clarifies that deadlines come from official announcements, past deadlines are excluded, and free-form deadlines sort after dated ones. However, it doesn't explicitly mention alternatives or state when not to use this tool (e.g., for platform changes), so it falls 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_platform_changesget_upcoming_deadlinesClaiming 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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The two tools have clearly distinct purposes: one lists platform changes, the other lists upcoming deadlines. There is no overlap in functionality, and the descriptions make the difference explicit.
Both tool names follow a consistent verb_noun pattern ('get_platform_changes', 'get_upcoming_deadlines'), with 'get' as the verb and clear domain-specific nouns. The naming is predictable and uniform.
With only two tools, the set is lean and precisely scoped to the server's purpose of tracking platform changes and deadlines. No unnecessary tools, and the count is appropriate for the domain.
The two tools cover the core needs of the domain: retrieving platform changes and retrieving upcoming deadlines. The descriptions indicate they handle all relevant platforms and provide necessary details, leaving no obvious gaps.
