One search across every agent registry: 100K+ MCP/A2A/x402 services, signed receipts.
Call a listed remote service THROUGH Clearhouse and get a signed receipt.
Routes the call via the matching protocol adapter (MCP / A2A / HTTP-x402),
enforces spend caps, settles x402 paywalls (dry-run by default), and
returns the result plus a cryptographically signed receipt binding
intent -> execution -> settlement -> outcome.
Args:
listing_id: id from search_agents results
tool: downstream tool name (omit to list the service's tools)
args_json: JSON object of tool arguments
caller_ref: opaque caller identifier for the receipt
payment_json: for paid services — JSON {"authorization": typed_data.message,
"signature": "0x..."} produced by signing quote_call's typed_data with
your own key. Clearhouse relays it; it never sees your private key.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | ||
| args_json | No | {} | |
| caller_ref | No | mcp-client | |
| listing_id | Yes | ||
| payment_json | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 substantial work: it discloses protocol adapter routing (MCP / A2A / HTTP-x402), spend-cap enforcement, dry-run default for x402 settlement, the signed receipt binding, and the fact that Clearhouse relays payment without seeing the private key. This gives the agent a clear model of side effects and safety behavior.
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 leads with a one-sentence summary, followed by a compact behavior block and a structured Args list. No sentence is filler — even the receipt-binding chain ('intent -> execution -> settlement -> outcome') concisely communicates the guarantee. It is long only because the tool is complex, and it front-loads the essential action.
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?
The tool has five parameters, no annotations, and an output schema (so return values do not need to be described). The description covers the full call flow, parameter semantics, optional behaviors, payment handling, and the receipt result, and it links to the sibling tools (search_agents, quote_call) that precede it. No critical information needed to select or invoke this tool correctly is missing.
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 0%, so the parameter explanations in the description are the only guidance available. Every one of the five parameters receives an added semantic: listing_id is tied to search_agents results, tool gains the omit-to-list behavior, args_json is a JSON object of tool arguments, caller_ref is identified as an opaque receipt identifier, and payment_json gets a precise JSON structure and signing source. This fully compensates for the schema gap.
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?
States a specific verb+resource: 'Call a listed remote service THROUGH Clearhouse and get a signed receipt.' The description further clarifies the role by mentioning protocol adapters, spend caps, and a signed receipt, which distinguishes it from siblings like search_agents (listing), quote_call (pricing/typed_data), and service_evidence (post-hoc verification).
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 references the prerequisite flow — 'listing_id: id from search_agents results' and payment_json produced by signing quote_call's typed_data — which tells the agent when this tool fits in the workflow. It also gives an explicit usage note for the optional 'tool' parameter ('omit to list the service's tools'). However, it never states when-not-to-use or points to an alternative for a specific condition, so it falls short of an explicit routing rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How many listings Clearhouse currently indexes, broken down by source registry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description clearly conveys a read-only count operation and discloses the output breakdown, which is the main behavioral trait for a zero-argument stats tool. It does not mention caching, freshness, or access constraints, but with no annotations the core read-only behavior is still clear from 'How many... currently indexes'.
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 filler. It states the metric, the resource, and the grouping dimension efficiently, earning its place entirely.
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 zero parameters and an output schema present, the description plus schema is nearly sufficient to invoke the tool correctly. The main missing piece is explicit guidance on when to prefer this tool over its siblings, though that gap is already reflected in usage_guidelines.
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 is empty and schema coverage is 100%, so there are no parameter semantics for the description to add. A zero-parameter tool gets a baseline of 4, and the description appropriately focuses on output rather than arguments.
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 identifies a specific resource ('listings Clearhouse currently indexes'), the metric ('How many'), and the grouping ('by source registry'). This makes the tool's purpose unmistakable and clearly separates it from operational siblings such as call_service and quote_call.
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 gives no explicit guidance on when to use index_stats versus search_agents or the other siblings. There are no triggers, exclusions, or alternative tool references, so the agent must infer usage from the name and wording alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Step 1 of a paid call (non-custodial). Fetches the service's payment requirements and returns the exact EIP-712 typed data your agent must sign with ITS OWN key. Nothing is charged until you sign and pass the result to call_service. If the service is free, says so.
Args:
listing_id: id from search_agents
payer_address: your agent's wallet address (the key that will sign)
args_json: JSON object of query args for the service
| Name | Required | Description | Default |
|---|---|---|---|
| args_json | No | {} | |
| listing_id | Yes | ||
| payer_address | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and handles it well. It discloses non-custodial behavior, that no charge occurs until signing, that the agent signs with its own key, and that free services are reported as such. It omits potential failure modes or rate limits, but the core behavioral traits are transparent.
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 front-loaded with the most critical fact, 'Step 1 of a paid call', followed by a tight behavioral overview and a clean Args list. Every sentence earns its place with no 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?
An output schema exists, so explaining return values is unnecessary. The description covers the payment workflow, signing requirement, charge behavior, and free-service case, giving an agent everything it needs 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?
Schema description coverage is 0%, so the description must add meaning to parameters. It does: listing_id is tied to search_agents, payer_address is explicitly the agent's signing wallet, and args_json is described as a JSON object of query args. This goes beyond the bare schema, although args_json could be more detailed about expected structure.
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 uses a specific verb, 'fetches', and clearly identifies the resource: the service's payment requirements and the exact EIP-712 typed data to sign. It also distinguishes itself from call_service by explicitly labeling itself 'Step 1 of a paid call' and noting the result must be passed to call_service.
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 strong workflow context: it is Step 1 before call_service, and nothing is charged until the agent signs and passes the result. It does not explicitly mention when not to use the tool or name alternative sibling tools like search_agents, but the ordering and 'pass to call_service' instruction make the intended usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search all indexed agent registries at once.
Args:
query: capability keywords, e.g. "pdf extraction", "solana rpc", "payments"
remote_only: only return services callable over the network right now
limit: max results (1-50)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| remote_only | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states that the search spans all indexed registries and clarifies remote_only and limit behavior, but it does not explicitly confirm read-only semantics, authentication needs, rate limits, or result behavior. 'Search' implies a safe read operation, but the tool does not state this.
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 compact and front-loaded: a one-sentence purpose followed by a tight Args block. Every line earns its place and there is no redundant or filler content.
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 search tool with an output schema available, the description covers purpose and all three parameters well. It lacks explicit guidance on result semantics and sibling-tool differentiation, but given the output schema and low complexity, the remaining gaps are minor.
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 0%, so the description fully compensates. It explains query with capability keywords and concrete examples, remote_only with precise semantics of network-callable services, and limit with a range of 1-50. This adds meaning far beyond the bare schema titles and defaults.
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 first sentence states a specific action and resource: 'Search all indexed agent registries at once.' This clearly distinguishes the tool from siblings like call_service and quote_call, which imply direct invocation rather than registry discovery.
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 given about when to use this tool versus alternatives such as index_stats or service_evidence. There are no explicit conditions, exclusions, or context for selecting search_agents over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Observed facts about a listing: live probe results (handshake, latency, HTTP status) and routed-call history (count, success rate, distinct callers). Includes an arithmetic summary_index with published methodology and a confidence tier. This is EVIDENCE for the caller's own decision — not a safety rating, and Clearhouse never selects or executes on your behalf.
| Name | Required | Description | Default |
|---|---|---|---|
| listing_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It explicitly says the tool provides evidence rather than a safety rating and that Clearhouse never selects or executes on the user's behalf, which is a meaningful behavioral boundary. It also hints at the live-probe nature of the data, though it could be clearer about whether invoking the tool triggers new probes or returns precomputed facts.
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 front-loaded with the core content: observed facts, probe results, call history, and summary index. The closing disclaimer about evidence vs. safety rating is valuable context and not excessive. It is slightly longer than necessary but every sentence adds meaning.
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 tool with one simple parameter and an output schema, the description covers the key context: what data is returned, the confidence tier, and the intended decision-support role. It does not explicitly state side effects or error cases, but the 'never selects or executes' line covers the most important behavioral edge. Overall, it is complete enough for an agent to call it appropriately.
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 only one parameter, listing_id, but schema description coverage is 0%, so the description must compensate. The description says 'about a listing' but never explains the listing_id parameter, its format, or how to obtain it. This leaves the agent to infer the parameter meaning entirely from the property name.
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 what the tool does: it returns observed facts about a listing, including live probe results and routed-call history. It specifies the resource ('listing') and the type of data returned, making the purpose concrete. However, it does not explicitly distinguish itself from sibling tools like index_stats, which could also return index-like statistics.
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 context: this is evidence for the caller's own decision, not a safety rating, and Clearhouse never selects or executes on the caller's behalf. This tells the agent when to use the tool and what not to use it for. It does not name alternatives or explicit conditions for sibling tools, so it stops 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.
call_serviceindex_statsquote_callsearch_agentsservice_evidenceClaiming 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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Search 15K+ MCP services, A2A agents, and x402 APIs from 5 registries. Paid via x402 (USDC on Base).
AI agent registry — search, discover, register, and connect agents via MCP.
Meta-search MCP for x402 agent-payable services -- find paid APIs across every registry.
Search engine for AI agents to find MCP servers, A2A agents, and skills on their own.
Add one secure layer between your agents and this server.
Each tool covers a distinct stage of the workflow: discovery (search_agents), marketplace-level data (index_stats), per-service evidence (service_evidence), pricing/authorization (quote_call), and execution (call_service). There is no meaningful overlap or ambiguity between tool purposes.
Three tools use a clear verb_noun pattern (call_service, quote_call, search_agents), but index_stats and service_evidence are noun phrases. The names are readable and snake_case is consistent, but the convention is mixed rather than uniform.
Five tools is well-scoped for a service gateway: search, evidence, stats, quote, and call. Each tool has a clear role and no tool feels redundant or unnecessary.
The toolset covers the full agent-facing lifecycle: discovering services, inspecting evidence and index totals, obtaining payment quotes, and executing calls with signed receipts. There are no obvious dead ends or missing operations within the stated domain.
