Live prediction markets: Polymarket + Kalshi prices, odds, order books. Pay-per-call USDC, no key.
Average 4.1/5 across 5 of 5 tools scored. Lowest: 3.5/5.
Most tools occupy distinct roles (install, usage stats, wallet setup), but list_endpoints and pricing_info overlap substantially: both report paid endpoints and prices, even though one emphasizes live status and the other detailed fees/payment networks. Descriptions help separate them, but an agent could still pick the wrong one for a pricing question.
Names are uniformly snake_case and readable, but the pattern is mixed: install_snippets and list_endpoints use verb_noun, while pricing_info, usage_stats, and wallet_helper use noun-based names. This is a readable mixed convention rather than a chaotic one.
Five tools is a reasonable number for a small server, but for a product called 'Prediction Market Data for AI Agents' the set is oddly scoped: everything is onboarding/pricing/usage meta-tools rather than data tools. The count is not excessive, but it feels thin for the apparent market-data purpose.
The server promises prediction market data, yet none of the five tools retrieves markets, odds, prices, positions, or any actual market data. Even if the free helper tools are complete, the core data surface is entirely absent, leaving agents with no way to fulfill the server's stated purpose.
install_snippetsAInspectReturn ready-to-paste configuration snippets for installing this MCP server in Claude Code, Cursor, Cline, Continue.dev, Windsurf, and Zed. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adequately reveals behavior: returns ready-to-paste snippets for multiple IDEs and notes it's free. No hidden side effects are expected for this read-only 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?
Single sentence, no wasted words. All essential information is front-loaded.
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 parameters and no output schema, the description covers the tool's purpose and output sufficiently. Minor room for improvement (e.g., format of snippets) but not necessary.
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?
No parameters exist, so baseline score of 4 applies. Description does not need to add parameter semantics.
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 returns configuration snippets for installing the MCP server in specific IDEs. It is distinct from sibling tools like list_endpoints or pricing_info.
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 when snippets are needed but does not explicitly state when to use or avoid this tool versus siblings. No alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_endpointsAInspectList all paid endpoints exposed by this MCP server with their prices and live status. Free — no wallet required. Use this first to discover what tools are available.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. While the read-only nature is implied for a list operation, it does not explicitly state safety or lack of side effects. Mentions 'Free' but does not cover rate limits or other behavioral traits.
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 action and result. Every sentence adds value: first defines action, second gives usage hint. 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?
Describes return content (prices, live status) but lacks detail on format or potential filtering. Adequate for a simple list tool with no output schema, though could be more precise.
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?
No parameters in schema (100% coverage), so baseline of 4 applies. Description adds no parameter detail beyond schema, which is expected.
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 the tool lists all paid endpoints with prices and live status. Uses specific verb 'list' and resource 'paid endpoints', distinguishing it from siblings like pricing_info which may focus on pricing details.
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 says 'Use this first to discover what tools are available' and 'Free — no wallet required', providing clear ordering and prerequisite guidance. Does not explicitly contrast with alternatives but offers strong usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pricing_infoAInspectReturn pricing details for the GoCreative Agent API — base price per call, premium endpoints, cache TTLs, and supported payment networks. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It indicates the tool is read-only by stating it 'returns' pricing details and adds 'Free' to signal no cost. However, it could more explicitly confirm no side effects or rate limits.
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 two sentences long, starting with the main action and listing key outputs. Every word adds value; no redundancy. It is ideally sized for a simple info tool.
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), the description covers the essential information an agent needs. It could be slightly more complete by hinting at the return format (e.g., JSON), but it is sufficient for most use cases.
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 baseline is 4. The description does not need to add parameter meaning, and the schema coverage is 100% (no parameters to document). The description adds no param info, which 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 tool returns pricing details for the GoCreative Agent API, listing specific components (base price per call, premium endpoints, cache TTLs, supported payment networks). This distinguishes it from sibling tools like list_endpoints or usage_stats, which serve different purposes.
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 retrieving pricing information but does not explicitly state when to use this tool over alternatives or mention any prerequisites. The context (siblings) suggests it's the go-to for pricing, but no direct guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
usage_statsAInspectReturn summary stats of how this MCP server has been used (top tools called, success rate, recent activity). Free. Use to verify your own integration is hitting the right tools.
| 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 must fully disclose behavioral traits. It mentions the tool is free and returns stats, but does not state whether it has side effects, requires authentication, or has rate limits. For a stat retrieval tool, more clarity on read-only nature and output details would be beneficial.
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 concise with two short sentences, each serving a clear purpose: first defines the function, second provides usage context. There is no unnecessary verbiage.
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 parameters, no output schema, and no annotations, the description covers the essential aspects: what the tool returns and a typical usage scenario. It could be slightly more detailed about response structure or limitations, but overall it is sufficient.
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 input schema has no parameters, so schema description coverage is 100%. The description adds value by detailing what the output contains (top tools, success rate, recent activity), which helps the agent understand the semantic meaning beyond the empty 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 summary stats about MCP server usage, including specific items like top tools called, success rate, and recent activity. It effectively conveys the core function, though it does not explicitly contrast with sibling tools to further distinguish itself.
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 a practical use case ('verify your own integration is hitting the right tools') and notes it's free. However, it lacks explicit guidance on when not to use this tool or mention of alternatives among siblings, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
wallet_helperAInspectReturn step-by-step instructions for setting up x402 USDC autopay for this MCP server. Use this if a paid tool returned a 402 error or you're onboarding a new agent that needs to pay for API calls. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It correctly identifies the tool as returning instructions and being free, but lacks details on output format or any potential rate limits. However, given the tool's simplicity (zero parameters), the disclosure is sufficient.
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 two sentences, front-loaded with the core action, and every word adds value. No redundancy or unnecessary detail.
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 zero-parameter, simple instruction-returning tool, the description covers purpose and usage context adequately. It could mention the return format (e.g., text) but is otherwise 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 input schema has zero parameters and 100% coverage, so the description does not need to add parameter details. The baseline of 4 applies, and the description offers no extra param information, which is acceptable.
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 ('return') and the resource ('step-by-step instructions for setting up x402 USDC autopay'), and the context signals show no sibling overlaps, making its purpose distinct.
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 specifies when to use ('if a paid tool returned a 402 error or you're onboarding a new agent') and notes it is free, providing clear guidance against alternatives.
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.
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Order-book depth & history for Polymarket, Kalshi & Limitless. Keyless demo, key for full access.
Live prediction-market odds, volume and movers across 8 platforms. Read-only, no auth.
Live Kalshi + Polymarket prediction-market data and cross-venue arbitrage spreads, one schema.
Kalshi MCP — US-regulated prediction-market data (no auth on public reads).
