Machine-payable prediction-market briefs and probability-change monitoring for AI agents.
Purchases one 0.05 USDC read-only structured prediction-market brief on Base.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Exact Polymarket market slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It does reveal the cost (0.05 USDC) and the blockchain (Base), but it does not explain prerequisites, authorization requirements, or what 'read-only' means in this purchase context. The behavioral disclosure is partial but not absent.
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, front-loaded sentence that wastes no words. It efficiently communicates the core purpose and key specifics without unnecessary elaboration.
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?
Even though the tool is simple (one parameter, no output schema), the description is incomplete for an agent: it does not explain what the resulting 'brief' contains, what the tool returns after purchase, or when it should be used. The lack of an output schema heightens the need for return-value context, which is absent.
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% (slug has a clear description), so the baseline is 3. The tool description adds no additional meaning about the slug parameter beyond what the schema already provides.
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 (purchases) and the resource (market brief), with specific details (0.05 USDC, on Base). It distinguishes this paid purchase tool from the sibling read-oriented tools by indicating a transaction rather than a read operation.
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?
No guidance is provided on when to use this tool versus alternatives like get_market_delta or get_market_pricing. There are no exclusions or explicit context for choosing this tool, leaving the usage decision entirely to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Purchases one 0.05 USDC read-only probability-change report since an ISO 8601 UTC timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Exact Polymarket market slug. | |
| since | Yes | Previous successful check time in ISO 8601 UTC format ending in Z. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose the paid nature (0.05 USDC) and read-only behavior. However, it lacks context on what happens after purchase (e.g., immediate delivery, failure modes, balance requirements).
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 and efficient, but the adjective placement '0.05 USDC read-only' creates a slightly awkward structure. Still, every word 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 paid operation with no output schema, the description is insufficient. It does not explain the report contents, error conditions, cost implications beyond the fee, or what the agent should expect after invoking the 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?
Schema coverage is 100%, so the parameters are already documented. The description adds no extra meaning to the parameters themselves; it only reinforces the 'since' timestamp concept already present in 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 identifies the tool as purchasing a probability-change report since an ISO 8601 UTC timestamp, which distinguishes it from siblings like get_market_brief and get_market_pricing. However, the verb 'purchases' is unusual for a read-only operation and could slightly obscure the core action.
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?
No explicit guidance on when to use this tool versus alternatives. The 'since' parameter implies usage for checking changes since a prior timestamp, but the description does not state use cases, exclusions, or when to prefer other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns public MCP pricing, network, asset, seller, and payment metadata without making a payment.
| 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 discloses that the operation is public and involves no payment, which are key behavioral traits for a read-only tool. It does not detail response format or data freshness, but given the zero-parameter scope, this level of disclosure is adequate.
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, front-loaded sentence with no wasted words. Every word contributes: 'Returns' sets the action, the list of metadata types specifies scope, and 'without making a payment' clarifies behavior.
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 simplicity (no parameters, no output schema, no annotations), the description is quite complete. It covers the primary purpose and the key behavioral aspect (no payment). It could mention the exact return structure, but the listed categories provide sufficient context for an agent to invoke the tool correctly.
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 tool has zero parameters, so the baseline is 4. The description adds value by enumerating the categories of data returned (pricing, network, asset, seller, payment metadata), which effectively explains what the tool does with no input.
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 states the verb 'Returns' and specifies the resource: 'public MCP pricing, network, asset, seller, and payment metadata'. This clearly differentiates it from sibling tools (get_market_brief, get_market_delta, list_trending_markets) by focusing on pricing and metadata rather than briefs, deltas, or trends. The title adds specificity with 'x402nano'.
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 phrase 'without making a payment' provides clear context that this is a free, non-transactional lookup, which implies it should be used when pricing/metadata is needed without a payment. It does not explicitly mention alternatives or exclusions, but the context is sufficiently clear for an agent to select this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists free Polymarket market candidates and slugs before purchasing intelligence.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum markets to return. |
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 indicates the tool is 'free' and returns 'market candidates and slugs,' which adds some transparency. However, it does not mention pagination, rate limits, or what 'purchasing intelligence' entails, leaving gaps for a simple list tool.
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, concise sentence that front-loads the action verb 'Lists' and includes no fluff or repetition. Every word serves a purpose.
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?
Although there is no output schema, the description hints at return content ('market candidates and slugs'), which is useful. For a tool with one well-documented parameter and a simple listing purpose, this is reasonably complete, though it could clarify the exact structure of returned data.
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 only parameter 'limit' is fully described in the schema ('Maximum markets to return'), so schema coverage is 100%. The description adds no additional meaning to the parameter; thus the baseline score 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 identifies the action (Lists) and the resource (free Polymarket market candidates and slugs). It distinguishes from sibling tools that focus on retrieving specific market details (get_market_brief, get_market_delta, get_market_pricing) by emphasizing this is a listing operation.
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 phrase 'before purchasing intelligence' provides a clear context for when to use this tool—as a discovery step prior to acquiring paid data. It does not explicitly name alternatives or state when not to use it, but the usage context is evident.
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_market_briefget_market_deltaget_market_pricinglist_trending_marketsClaiming 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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Calibrated probabilistic foresight for AI agents, powered by live prediction-market signal.
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Add one secure layer between your agents and this server.
Each tool targets a distinct action: listing trending markets, purchasing a brief, purchasing a delta report, and retrieving service pricing metadata. There is no functional overlap, and the descriptions clearly differentiate the outputs.
Most tools use 'get_' for acquiring resources, while 'list_trending_markets' uses 'list_' for enumeration. This is a common and predictable convention, so the naming is mostly consistent with only a minor deviation.
With only 4 tools, the server is tightly scoped to its purpose of providing market intelligence, avoiding unnecessary bloat while covering the essential functions.
The tool set covers the full workflow: discover trending markets, understand pricing, and purchase two types of market analyses. No obvious gaps exist for the stated domain of prediction-market intelligence.
