Know what every affiliate link actually earns, and fix what's bleeding revenue.
Articles that are earning affiliate commissions AND have broken affiliate links on the page — the revenue currently leaking each month. This is the cross-join between WeCanTrack revenue data and LinkPulse's local link scanner. Returns empty if the site has no broken links on earning articles. Use this to answer "where am I losing money" and prioritize fixes.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Revenue window in days (default 30). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses data source cross-join and empty return behavior. However, lacks details on permissions, latency, or whether the result is cached or real-time.
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?
Three sentences conveying purpose, data source, and use case. Could be slightly more concise but lacks fluff and front-loads key information.
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?
Adequate for a simple tool with one parameter and no output schema, but does not describe output fields (e.g., article URL, revenue lost). This gap reduces completeness.
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?
Only one parameter 'days' with full schema description coverage. Description does not add extra semantics beyond the schema's 'Revenue window in days'. 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?
Clearly defines the tool as returning articles earning affiliate commissions with broken links, specifying the cross-join of two data sources. Differentiates from siblings like broken_links (only broken links) and revenue_summary (aggregate revenue).
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?
Explicitly states when to use: to answer 'where am I losing money' and prioritize fixes. Mentions empty return condition. Could be improved by contrasting with siblings like broken_links for cases without revenue context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
All broken affiliate links currently flagged by the LinkPulse scanner on this site. Status 4xx (dead) and 3xx (redirecting) are both included, grouped by post. Useful for audit-style questions and for piping into suggest_replacement.
| 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 carries the full burden. It reveals data grouping ('grouped by post') but does not disclose whether the tool is read-only, requires authentication, or has rate limits. For a tool listing broken links, this is insufficient.
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: first defines the tool's output precisely, second gives usage guidance. No wasted words, front-loaded with key information.
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?
Without an output schema, the description explains what is returned (broken links with status codes, grouped by post). It could specify the exact structure (e.g., list of posts with links) or mention pagination, but it's sufficient for a simple audit tool.
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 zero parameters, and schema coverage is 100%, so the description doesn't need to add parameter meaning. Baseline 4 applies per rules.
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 explicitly states the tool returns 'all broken affiliate links' with status codes 4xx and 3xx, grouped by post. It clearly distinguishes itself from siblings like 'suggest_replacement' and 'revenue_summary' by focusing on broken links.
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 says 'Useful for audit-style questions and for piping into suggest_replacement,' providing clear usage context. It doesn't explicitly state when NOT to use it, but the sibling tools imply alternatives, so it's nearly excellent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Total affiliate commissions for the current site over a window (default 30 days), with transaction count, average commission, and delta vs. the equivalent prior window. Data comes from WeCanTrack via the LinkPulse sync pipeline.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Window size in days (1-365, default 30). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the data source (WeCanTrack via LinkPulse) and indicates it is a read-only summary returning specific metrics. No annotations are present, so the description carries the burden; it is adequate but could mention limitations like data recency or potential delays.
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 that is front-loaded with the main output and efficiently packs all key information 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?
For a simple tool with one parameter and no output schema, the description provides all necessary information: what is returned, the default window, and data provenance.
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 single parameter 'days' is fully described in the schema. The description adds context by mentioning the default value (30 days) and that the delta compares with an equivalent prior window, enhancing understanding 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?
The description clearly states the tool returns total affiliate commissions with additional metrics like transaction count and delta, and specifies the scope (current site) and time window. It distinguishes well from sibling tools which focus on content analysis.
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 fetching revenue summaries over a given window, and siblings are clearly different domains, so there is no confusion. However, it lacks explicit guidance on when not to use it or alternative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a broken or dead affiliate URL plus the article it sits on, ask Claude (with web search) to find the closest-available alternative product on the same retailer or network. Returns a candidate URL, short reasoning, confidence, and a URL-reachability check. This is the "autonomous fix suggestion" that makes LinkPulse agent-native — the v1.1 apply_fix tool will consume this output directly.
| Name | Required | Description | Default |
|---|---|---|---|
| network | No | Affiliate network (e.g. Awin, Bol.com, Daisycon) if known. | |
| broken_url | Yes | The affiliate URL that is dead or redirecting. | |
| anchor_text | No | The visible anchor text of the broken link, if known. | |
| article_title | No | Title of the article the link appears on (helps with context). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full transparency burden. Discloses that tool uses Claude with web search to find alternatives. States return fields, including a URL-reachability check. Does not mention failure modes (e.g., no alternative found) or rate limits, but key behavioral traits are covered.
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?
Three sentences, no redundancy. Front-loaded with action. Each sentence adds value: purpose, method, outcome, and integration. Ideal conciseness.
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?
Despite no output schema, description lists all return fields. Explains integration with apply_fix. Sibling tools are distinct. All necessary information for an agent to understand and use this tool correctly is present.
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 coverage is 100%, meaning schema already documents all parameters. Description restates parameters without adding new meaning beyond context grouping. Baseline 3 is appropriate as description adds minimal semantic value over 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?
Description uses specific verb 'suggest' with resource 'alternative affiliate product URL'. Clearly states input (broken URL, article) and output (candidate URL, reasoning, confidence, reachability). Distinguishes from siblings like broken_links (detection) and apply_fix (application), making it unique.
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?
Description implies usage when an affiliate URL is broken/dead and article context is available. Mentions that output feeds into apply_fix tool, providing workflow context. Does not explicitly state when not to use or alternatives, but context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Articles on the current site that generated the most commission over a window. URL grouping strips tracking parameters so the same article with different ?gclid= values is counted together. Use this to answer "which posts make money" and "where should I spend editorial time".
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Window size in days (default 30). | |
| limit | No | Max articles to return (default 10). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses important behavior: URL grouping strips tracking parameters like gclid. No annotations provided, so description carries full burden but does not cover query scope (e.g., read-only, no side effects) or rate limits. Adequate but not exhaustive.
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: first states purpose, second provides behavioral detail and usage guidance. No redundancy, front-loaded. Every sentence adds value.
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?
Parameters well-covered. Missing explicit statement about ranking order (descending by commission) and window end point (e.g., last N days including today). However, tool is simple and output likely obvious. Good enough for effective use.
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 both parameters with descriptions. Description adds semantic context: explains how the grouping of URLs (stripping tracking params) affects article counting. This helps agent understand parameter impact beyond schema basics.
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?
Describes exact resource ('articles on current site'), action ('most commission over window'), and unique behavior (URL grouping). Distinguishes clearly from siblings like at_risk_articles or revenue_summary.
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?
Explicitly states use cases ('which posts make money', 'where to spend editorial time'). Does not specify when not to use, but the purpose is narrow enough. Could mention alternatives like revenue_summary for broader earnings view.
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.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: at_risk_articles combines broken links with revenue loss, broken_links lists all broken links, revenue_summary gives revenue data, suggest_replacement finds alternatives, and top_articles shows top earners. There is no overlap.
All tool names follow a consistent snake_case pattern with descriptive terms like adjective_noun (at_risk_articles, broken_links) or verb_noun (suggest_replacement). No mixing of conventions.
5 tools is well-scoped for a server focused on affiliate link management and revenue optimization. Each tool serves a clear purpose without redundancy.
The set covers finding broken links, assessing revenue impact, and suggesting replacements, but lacks an 'apply_fix' tool to actually implement changes, which is mentioned as v1.1. Missing update/delete functionality for links.