AI research reports: market analysis, competitor intel, blog posts. USDC payment.
Order an SEO-optimized blog post (1,200+ words, same-day delivery). Pay $29 USDC on Ethereum mainnet.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to receive the post | ||
| topic | Yes | Blog post topic and target keywords | |
| language | No | English |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behavioral traits: payment amount, currency, network, delivery speed, and word count. However, it omits what happens after payment and return value.
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 sentence includes all critical information with 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 no output schema, the description does not explain what the agent receives as a result. It also lacks prerequisites like wallet payment. Completeness is adequate but not thorough.
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 67%. The description adds value by specifying 'SEO-optimized' and '1,200+ words' but does not explain parameter formats or constraints 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?
The description clearly states it orders an SEO-optimized blog post with specific word count and delivery. It distinguishes from sibling tools like order_competitor_analysis by specifying 'blog post'.
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 use for blog posts but does not explicitly state when to use or not, nor does it compare with sibling tools. Guidance is implicit, not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Order a competitor analysis report (1,500+ words, same-day delivery). Pay $49 USDC on Ethereum mainnet.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to receive the report | ||
| topic | Yes | Your market and competitors to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It reveals that the tool requires payment ($49 USDC on Ethereum mainnet) and delivery time (same-day), which are critical. However, it does not mention what happens after ordering (e.g., confirmation, failure modes).
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, efficient sentence that conveys the essential information without filler. Every word 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?
For a simple ordering tool with two parameters, the description covers the critical operational details (cost, delivery time, word count). It could be more complete by specifying the report format or delivery method, but it is adequate for an agent to understand the core functionality.
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?
Since schema description coverage is 100%, the baseline is 3. The description adds overall context but does not provide additional detail on the parameters beyond what the schema already gives.
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 action ('Order'), the resource ('competitor analysis report'), and key attributes (1,500+ words, same-day delivery, $49 USDC payment). This distinguishes it from sibling tools like order_blog_post or order_market_research.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, scenarios, or when not to use it. The payment information is given but not contextualized as a usage condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Order a professional market research report (1,500+ words, same-day delivery). Pay $49 USDC on Ethereum mainnet.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to receive the report | ||
| topic | Yes | The market or industry to research | |
| language | No | English |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses payment requirement ($49 USDC on Ethereum mainnet), delivery speed (same-day), and output size (1500+ words). This is substantial transparency for a payment-required tool, though it omits confirmation steps or failure handling.
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, efficient sentence that includes all essential information: purpose, output specification, delivery, and payment. Every word contributes meaning, with no redundancy or filler.
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 3 parameters, no output schema, and no annotations, the description covers the core purpose but lacks details on output format, payment flow, error scenarios, or post-order steps. For a simple ordering tool, it is adequate but not fully comprehensive.
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 67% (2 of 3 params have descriptions). The description adds value beyond the schema by specifying report quality (1500+ words, professional) and payment details, which are not in parameter descriptions. This compensates well for the missing language 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 orders a market research report with specifics (1500+ words, same-day delivery, $49 USDC). However, it does not explicitly differentiate from sibling tools like order_blog_post or order_competitor_analysis, though the 'topic' parameter implies market research context.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or when not to use it. The agent would have to infer usage from the tool name and description content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Order a technical summary (600-1,000 words, same-day delivery). Pay $19 USDC on Ethereum mainnet.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to receive the summary | ||
| topic | Yes | Topic or document to summarize |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses that this involves a payment of $19 USDC on Ethereum mainnet and same-day delivery. However, it does not mention prerequisites, side effects (e.g., wallet connection), or what occurs on failure.
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 efficiently conveys the product, specifications, cost, and payment method with zero 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?
While the description covers key aspects (product, length, delivery, price), it does not explain what the agent receives (e.g., confirmation, email delivery) or any steps required from the user. With no output schema, this gap reduces completeness. Sibling context helps but not fully.
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% with clear descriptions for both parameters. The description adds no additional meaning beyond the schema, focusing instead on the service output. Baseline 3 is appropriate.
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 'Order' and resource 'technical summary', specifies word count range (600-1,000) and delivery speed (same-day). It distinguishes from siblings by specifying 'technical' vs. blog post, competitor analysis, or market research.
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 context that this tool is for ordering technical summaries, with specific constraints (word count, delivery, payment). It does not explicitly exclude alternatives, but the noun 'technical' and sibling names imply when to use.
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.
order_blog_postorder_competitor_analysisorder_market_researchorder_technical_summaryClaiming 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.
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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.
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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
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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
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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
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Each tool has a distinctly different deliverable (blog post, competitor analysis, market research, technical summary), making it easy for an agent to select the appropriate one.
All tool names follow a consistent 'order_<noun>' pattern, which is predictable and clear.
With 4 tools, the server is well-scoped for its purpose of ordering research reports; not too few or too many.
Covers common report types (blog, competitor, market, technical) but lacks options for custom requests or status tracking, though these are minor gaps for the intended scope.