agent-tools (x402 directory)
Server Details
Discover and call x402 paid services — search by intent, filter by price/chain.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- JoursBleu/agent-tools-mcp
- GitHub Stars
- 0
- Server Listing
- agent-tools-mcp
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Usage analytics
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Tool Definition Quality
Average 4.3/5 across 11 of 11 tools scored. Lowest: 3.5/5.
Most tools have clearly distinct purposes, but `search` and `ask_services` both retrieve services with overlapping functionality, though descriptions clarify their different ranking methods. `get` and `get_mcp_server` also have similar roles, but the latter is specifically for MCP servers.
Tool names generally follow a verb_noun pattern (e.g., `ask_services`, `search_mcp_servers`), but `stats` is a bare noun and `register` is a bare verb, deviating from the pattern. Overall consistency is good with minor exceptions.
With 11 tools covering discovery, detail retrieval, scanning, registration, categorization, and statistics, the count is appropriate for a directory service. Each tool serves a distinct role without unnecessary bloat.
The tool surface covers core directory operations well: search, detail views, registration, safety scanning, and stats. However, there is no update or delete tool for registered services, and `get_mcp_server` is somewhat redundant with `get`, which is a minor gap.
Available Tools
11 toolsask_servicesAInspect
Ask for the best x402/MCP services for an agent intent.
This is the high-level discovery tool: it retrieves candidates from the
directory, asks the configured backend LLM to rank only those candidates,
and returns service cards for the selected recommendations. If the LLM is
unavailable, it falls back to the directory ranker.
Args:
intent: Natural-language job the agent wants to accomplish.
top_k: Max recommendations to return (1-10).
max_price_usd: Optional per-call budget cap.
category: Optional directory category filter.
chain: Optional payment network filter, e.g. "base" or "solana".
require_healthy: When true, only consider services marked health=ok.
min_confidence: Optional x402scan quality floor (0.0-1.0).
has_mcp: When true, only consider services with MCP endpoints.
use_llm: Set false for deterministic retrieval-only fallback.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | ||
| top_k | No | ||
| intent | Yes | ||
| has_mcp | No | ||
| use_llm | No | ||
| category | No | ||
| max_price_usd | No | ||
| min_confidence | No | ||
| require_healthy | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes the retrieval and ranking process, including fallback behavior, but does not explicitly state whether the tool is read-only or has side effects (e.g., logging). For a discovery tool, the operations appear safe, but the description could be more direct about non-destructive nature.
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 appropriately structured with a brief introductory sentence followed by a clear Args list. It is not overly verbose for 9 parameters, though some parameter descriptions could be slightly more compact. Overall, it earns its place without excessive fluff.
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 complexity (9 parameters, 1 required, no enums) and the presence of an output schema, the description covers the behavior and all parameters adequately. It does not describe return values, but the output schema presumably handles that. The description is complete enough for an agent to understand and 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 carries full burden. The Args list clearly explains all 9 parameters with their types and defaults. Each parameter's purpose is described in one line, adding meaning beyond the schema properties. This fully compensates for the lack of schema descriptions.
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's purpose: 'Ask for the best x402/MCP services for an agent intent.' It explains it is the high-level discovery tool that retrieves candidates, ranks them via LLM, and returns service cards. This distinguishes it from siblings like 'get' (likely for a single service) and 'search' (simpler filtering).
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 explains the tool's behavior (discovery, LLM ranking, fallback) but does not explicitly state when to use it versus alternatives. For instance, it doesn't say 'use get for direct lookups' or 'use search for simple keyword queries.' The context of sibling tool names hints at differentiation, but the description itself lacks explicit usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getAInspect
Get full details + ready-to-paste call template for a service.
Returns the service card with payment, call, quality, resource samples,
and ready-to-use MCP/x402 call hints. Use this after `search` or
`ask_services` before paying/calling an external service.
Args:
slug: Service slug as returned by `search` items.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 tool returns a service card with various components, but does not mention potential side effects, error handling, or permission requirements. For a read-only retrieval tool, this is adequate but could be improved with explicit safety notes.
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 paragraph that front-loads the main purpose (get full details + call template) and provides structured detail in the Args section. It is appropriately concise with no wasted words, though a bullet list of returned components could improve scannability.
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 has an output schema (documenting return values) and only one required parameter, the description is complete. It explains the tool's output, usage context, and parameter source. No additional details are necessary for an agent to use it 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 input schema has zero description coverage for the 'slug' parameter. The description compensates by specifying that the slug comes from 'search' items, adding meaningful context beyond the schema. This helps the agent understand where to obtain the value.
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 retrieves full details and a ready-to-paste call template for a service, listing specific components (payment, call, quality, resource samples) and MCP/x402 hints. It distinguishes itself from siblings by specifying its use after 'search' or 'ask_services'.
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 explicitly states when to use this tool: after 'search' or 'ask_services' and before paying/calling an external service. It implies exclusivity by omitting alternative use cases, providing clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_mcp_serverAInspect
Get the full card for one MCP server by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It indicates a read operation via 'Get', but does not explicitly state that it does not modify data, mention authentication requirements, or describe the output beyond 'full card'. The output schema exists but is not summarized.
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 direct and to the point, with no unnecessary 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?
For a simple tool with one parameter and an output schema, the description is nearly complete. However, it lacks behavioral transparency and could mention that it is a read-only operation. Still, it provides the essential purpose.
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%, and the description only mentions 'by slug', adding minimal meaning. It does not explain what constitutes a slug or how to find it. The description should compensate for the lack of schema descriptions.
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 'Get', the resource 'full card for one MCP server', and the key parameter 'slug'. It distinguishes this tool from siblings like 'search_mcp_servers' which would return multiple servers.
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 you have a specific slug, but does not explicitly state when to use this tool versus alternatives like 'search_mcp_servers'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
List all available service categories in the directory.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since annotations are absent, the description bears full burden. It clearly states 'list', implying a read-only operation with no side effects. However, it does not mention authentication requirements or behavior when no categories exist, but for a simple list tool, this is largely 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?
A single, front-loaded sentence of eight words. Every word earns its place. No redundancy or 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?
Given the tool's simplicity (no parameters, list operation) and the presence of an output schema, the description is complete. It conveys the essential purpose without needing additional context.
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, so the baseline is 4. The description adds no parameter information beyond the empty schema, which is acceptable as no parameters exist.
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 the specific verb 'list' and resource 'service categories' with clear scope 'all available in the directory'. It distinguishes from siblings like 'get' (single item), 'register' (create), 'search' (query), and 'stats' (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?
No guidance on when to use this tool versus alternatives. No mention of exclusions or prerequisites. The description simply states the action without providing context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
registerAInspect
Self-register an x402 / MCP service in the agent-tools directory.
Service owners and agents may submit new services here. Submissions
are auto-reviewed instantly by x402 verification (no human gate): if
the URL proves x402 payment support it is listed immediately and shows
up in `search`; otherwise it is rejected or retried automatically.
Listing is FREE.
Dedup: if a service with the same canonical origin (scheme://host)
already exists in the directory we return its slug instead of
creating a duplicate submission. Same goes for a still-pending
submission with the same origin.
Rate limit: at most 5 pending submissions per client IP per 24h.
Hits beyond that get `{error: rate_limited}` — try again later or
email contact@agent-tools.cloud for bulk imports.
Args:
url: Public HTTPS URL of the service (the x402-payable endpoint
or its homepage). Required.
name: Human-friendly name. Defaults to the URL hostname.
description: One-paragraph description (max ~2000 chars).
mcp_url: If the service speaks MCP, its streamable-http endpoint.
category: Free-form (e.g. "defi", "search", "social"). Use
`list_categories` to align with existing taxonomy.
chains: Networks the service accepts payment on
(e.g. ["base", "solana"]).
price_min_usdc: Lower bound of per-call price in USDC.
price_max_usdc: Upper bound of per-call price in USDC.
contact: Optional email / handle the directory team can reach
you on for clarifications.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| name | No | ||
| chains | No | ||
| contact | No | ||
| mcp_url | No | ||
| category | No | ||
| description | No | ||
| price_max_usdc | No | ||
| price_min_usdc | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully shoulders the burden. It discloses the auto-verification process, dedup logic (returns slug for existing origin), rate limit (5 pending per 24h per IP), and error message. It does not detail update behavior or full error handling, but is still quite 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 well-structured: purpose first, then process, dedup, rate limit, and parameter list. It is front-loaded with key info. While slightly long, every sentence contributes value, earning a high score.
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 9 parameters, no annotations, and an output schema existence, the description covers the core flow, dedup, and rate limit. It lacks explicit detail on success response format (though output schema may cover it) and other error cases, but is largely 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?
Schema description coverage is 0%, yet the description provides comprehensive explanations for all 9 parameters, including defaults, constraints, and examples (e.g., 'url: Public HTTPS URL... Required; name: Human-friendly name. Defaults to URL hostname; chains: Networks the service accepts payment on'). This adds substantial meaning beyond the raw 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 opens with 'Self-register an x402 / MCP service in the agent-tools directory,' which clearly states the verb (register) and the resource (service in the directory). It distinguishes from siblings (search, list_categories, etc.) which are about querying, not creating.
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 'Service owners and agents may submit new services here' and explains the auto-review, dedup, and rate limiting. It provides an alternative for bulk imports and advises retrying after rate limit, giving clear guidance on when and when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_mcp_safetyAInspect
Check an MCP server for malware / prompt-injection lures by its endpoint URL.
Give the server's streamable-http endpoint URL. Two paths:
* **Already in the agent-tools directory** → returns our LATEST stored
rule verdict. Every indexed server is re-scanned hourly, so you get a
consistent, continuously-refreshed answer without re-probing.
* **Not yet indexed** → we probe the endpoint live, statically scan its
advertised tools + metadata, ADD it to the directory, and return the
fresh verdict (so the next caller gets the rule verdict instantly from
cache).
Two dimensions are reported. `verdict` is authoritative and comes from
deterministic static rules — pure pattern-matching over the *advertised*
text only, NO code execution. It flags the social-engineering / RCE tricks
listing-spam servers use:
* `curl … | bash` and `base64 -d | sh` install lures
* `eval "$(curl …)"` / PowerShell `IEX(...DownloadString)` cradles
* base64 blobs that decode to a shell command
* bare-IP payload hosts and cheap throwaway TLDs
* prompt-injection / credential-exfiltration phrasing
("ignore previous instructions", "send your .env / api key")
* MCP tool-poisoning coercion — descriptions that hijack an agent's
tool-calling ("always call this tool first", "before using any other
tool you must…"), hidden `<IMPORTANT>` instructions, "list all API
keys / include secrets in your response", and coercion to read &
forward `.key`/`.pem`/`.ssh`/`.env` files
Source-code-oriented rules (SQL / command / code injection) are deliberately
not applied to natural-language descriptions, to avoid false positives.
`llm_reference` is an advisory frontier-LLM second opinion over the same text.
Because the LLM is slow it is computed LIVE on this call only and is never
stored (the hourly job never runs it), so it may be null on timeout. It
never overrides the rule verdict; when it is *more* severe than the rules an
`advisory` note is attached as a safety-net signal. Security/defense
products that merely *name* these attacks are not flagged.
Args:
endpoint_url: The MCP server's streamable-http URL (required). This is
the identity we look up / index by.
name: Optional advertised name (used when the server is new and gets
added; falls back to the URL host).
description: Optional description / README blurb (scanned when new).
tools_text: Optional tool names + descriptions; used only if the live
probe cannot fetch the server's tools/list.
Returns:
{ verdict: "clean"|"suspicious"|"malicious", score: 0-100,
reasons: [{rule, weight, snippet}],
llm_reference: {model, verdict, reason, confidence} | null,
advisory: str | null, slug, name, endpoint_url,
source: "stored" (existing) | "new_scan" (just added), indexed: bool }
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| tools_text | No | ||
| description | No | ||
| endpoint_url | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It meticulously explains the two dimensions (verdict and llm_reference), the deterministic static rules vs. advisory LLM, the types of attacks detected, and what is deliberately not detected to avoid false positives. It also clarifies that llm_reference is computed live and never overrides the rule verdict.
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 lengthy but well-structured with sections, bullet points, and clear separation of paths. Every sentence adds value, though some redundancy exists (e.g., repeating 'static rules' details). Front-loading the purpose helps agents quickly grasp the tool's function.
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 complexity (two execution paths, two evaluation dimensions, extensive rule explanations), the description is remarkably complete. It covers the output structure even though an output schema exists, and it addresses edge cases like timeout for llm_reference. No gaps remain for an agent to misuse 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 description coverage is 0%, but the description fully compensates by explaining each parameter: endpoint_url (required, identity for lookup/index), name (optional, used when new), description (optional, scanned when new), and tools_text (optional, fallback if live probe fails). This adds significant meaning beyond the schema's type and default values.
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 it checks an MCP server for malware and prompt-injection lures by endpoint URL. It distinguishes itself from sibling tools like search and get by focusing on security scanning. The verb 'check' is generic, but the elaboration on 'malware / prompt-injection lures' makes it specific.
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?
It clearly instructs to provide the streamable-http endpoint URL and outlines two paths (already indexed vs. new). While it doesn't explicitly state when not to use the tool, the context from siblings and the description imply it's for safety checks. No alternative tools are mentioned, but usage is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspect
Find x402 / MCP services matching an intent or filter set.
Two usage modes (agents pick whichever fits):
A. Natural-language: `search(intent="fetch tweets for @user")`
B. Pure browse: `search(has_mcp=True, category="defi", top_k=10)`
At least one of `intent`, `category`, `chain`, `has_mcp`,
`min_confidence` must be supplied — otherwise the call is
rejected (we won't dump 2300+ rows).
Results are ranked by:
(health=ok AND tx_30d>0) → health=ok → has-quality-signal →
confidence → tx_30d → recency.
So the highest-quality real-traffic services appear first.
Each item includes (when available):
- confidence : 0.0–1.0 x402scan quality score.
- tx_30d : 30-day x402 payment count (proxy for real usage).
- match_snippet : FTS snippet showing where `intent` hit ([[token]]).
- match_reason : list[str] of human-readable ranking signals.
- mcp_url : populated when the service exposes an MCP endpoint
(you can call it directly via streamable-http).
Agents should prefer items with non-null confidence and tx_30d > 0
unless the user explicitly wants experimental endpoints.
Args:
intent: What the agent wants to do (English or Chinese). Optional
when at least one structured filter is set. Synonym expansion
covers twitter↔X↔推特, whale↔巨鲸, price↔价格 etc.
top_k: Max services to return (default 5, hard cap 25).
max_price_usd: Upper bound on per-call price in USD.
category: Filter (see `list_categories`).
chain: "base", "polygon", "solana", "arbitrum", ...
min_confidence: Minimum confidence (0.0–1.0). 0.8+ keeps only
services x402scan rates as high-quality.
has_mcp: When true, return only services with a callable MCP
endpoint. Use this when the agent wants to chain another MCP
server rather than perform raw HTTP+x402.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | ||
| top_k | No | ||
| intent | No | ||
| has_mcp | No | ||
| category | No | ||
| max_price_usd | No | ||
| min_confidence | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavior: ranking logic (health, tx_30d, confidence), rejection criteria, result fields, synonym expansion, and default values. No contradictions.
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?
Somewhat verbose but well-structured with sections (overview, modes, ranking, fields, args). Information is front-loaded and every sentence adds value, though could be slightly more compact.
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 tool complexity (7 params, ranking, result fields, output schema exists), the description is thorough: explains ranking algorithm, result fields, usage modes, and parameter constraints. Leaves no significant gaps.
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?
Despite 0% schema coverage, the description adds rich meaning to all 7 parameters: provides defaults, examples, synonyms for 'intent', filter guidance for 'category' and 'chain', and constraints for 'min_confidence' and 'has_mcp'.
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's purpose: 'Find x402 / MCP services matching an intent or filter set.' It distinguishes from siblings like 'get' (single service retrieval) and 'list_categories' by emphasizing multi-parameter search and ranking.
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 describes two usage modes (natural-language and pure browse) with examples. States required parameters to avoid rejection: 'At least one of intent, category, chain, has_mcp, min_confidence must be supplied.' Advises agents to prefer items with high confidence and usage unless experimental endpoints requested.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_a2a_agentsAInspect
Find A2A agents you can delegate a task to.
Args:
intent: Natural-language description of the task to delegate.
top_k: Max agents to return (1-20).
x402_only: When true, only return agents that advertise x402 payment.
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | ||
| intent | No | ||
| x402_only | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It only explains parameters and states the purpose, but doesn't mention if it's read-only, side effects, or authentication 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?
One clear sentence followed by concise parameter list. No superfluous text; every element is informative.
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?
Output schema exists, so return values are covered. Parameter descriptions are complete, but missing behavioral context (safe to call, side effects). Overall adequate for a search 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 0%, but the description explains each parameter (intent, top_k, x402_only) with meaningful context beyond the schema's bare types 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?
Clear verb+resource ('Find A2A agents') distinguishes from siblings like search_mcp_servers. The description immediately states the tool's 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?
While the description doesn't explicitly compare to alternatives, the tool's name and purpose make it clear when to use (for A2A agents). Parameter docs give context, but no explicit 'when not to'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_mcp_serversAInspect
Find MCP servers in the directory.
Searches the standalone MCP directory (PulseMCP / official MCP registry
import) unioned with x402 services that also expose an MCP endpoint.
Returns normalised entries with a ready-to-use streamable-http
`call_hint.mcp.url`.
Args:
intent: Natural-language description of the tool/capability needed.
top_k: Max servers to return (1-20).
chain: Optional payment-network filter for paid MCP servers.
require_healthy: When true, only return servers marked health=ok.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | ||
| top_k | No | ||
| intent | No | ||
| require_healthy | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses the output includes normalized entries with a streamable-http URL and mentions filtering options (chain, require_healthy). Missing explicit statement about auth or rate limits, but for a search tool this 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?
Concise and well-structured: a brief opening sentence, a sentence about data sources, a sentence about output format, and a clear Args list. Every sentence adds value without 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?
The description covers the tool's purpose, data sources, all parameters, and output format. With no annotations but an output schema hinted, it provides sufficient context for an agent to understand behavior and return values.
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 0%, but the description fully compensates by explaining all four parameters: intent, top_k, chain, require_healthy. It adds meaning beyond the schema by describing their purpose and constraints (e.g., top_k range 1-20). This exceeds the baseline of 3.
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 finds MCP servers in the directory, specifying the union of two sources. It differentiates from sibling tools like search_a2a_agents and search_resources by focusing on MCP servers.
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 explains the data sources (standalone MCP directory and x402 services) and provides parameter details, but does not explicitly state when to use this tool versus alternatives like search or search_a2a_agents. However, the purpose is clear and the context implicitly guides usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_resourcesAInspect
Unified search across x402 services, MCP servers and A2A agents.
Args:
intent: Natural-language query.
protocol: Optional filter: "x402", "mcp" or "a2a".
top_k: Max resources to return (1-50).
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | ||
| intent | No | ||
| protocol | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral disclosure. It explains the search operation and parameter constraints (top_k 1-50) but omits aspects like authentication, rate limits, or whether results are ordered. This is acceptable for a search tool but not comprehensive.
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 extremely concise, using just six lines to state purpose and list parameters with brief explanations. Every sentence adds value, and the structure front-loads the core 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?
Given the presence of an output schema (which covers return format), the description adequately describes the input parameters and scope. However, it could mention default values (e.g., top_k defaults to 10) or whether results are paginated, but overall is sufficient for a search 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 description coverage is 0%, so the description's parameter explanations add critical meaning. It clarifies that 'intent' is a natural-language query, 'protocol' filters by type, and 'top_k' caps results. This goes beyond the raw schema which lacks any descriptions.
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 'Unified search across x402 services, MCP servers and A2A agents,' which precisely identifies the tool's purpose and distinguishes it from more specific siblings like search_mcp_servers and search_a2a_agents.
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 defines each parameter (intent, protocol, top_k) with clear semantics, giving implicit guidance on usage. However, it does not explicitly specify when to use this unified search versus more targeted sibling tools, leaving some inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statsAInspect
High-level stats about the directory: total services, healthy count, sources.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the output (stats) but does not disclose whether the operation is read-only, non-destructive, or any potential side effects. The agent can infer it is safe due to the nature of stats, but explicit statements are missing.
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 concise, front-loaded with key terms ('high-level stats'), and lists specific metrics. Every word adds value without 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?
Given no parameters, no annotations, and presence of an output schema, the description is mostly complete for a simple stats tool. It names the key stats returned, though it could mention real-time vs. cached nature or scope. Still, it adequately serves the agent's needs.
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 (100% schema coverage), so baseline is 4. The description adds no additional parameter meaning as there are none. No further explanation is necessary.
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 that the tool provides 'high-level stats about the directory', listing specific metrics (total services, healthy count, sources). This distinguishes it from sibling tools like 'get' (individual item) and 'search' (filtered retrieval), effectively communicating its role as an aggregate/summary tool.
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 the tool is for obtaining an overview of directory health, but it does not explicitly state when to use this tool versus alternatives (e.g., when to use 'list_categories' or 'search'). No exclusions or prerequisites are provided, leaving context to the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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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
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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
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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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