Provides tools to search for sales tools, APIs, and MCP servers that integrate with HubSpot, enabling users to find enrichment, outreach, or CRM tools compatible with HubSpot.
Search 200+ sales tools, APIs, and MCP servers directly from Claude Code, Cursor, or other AI agents.
Salestools.club - Technical audits, head-to-head comparisons, and starter prompts for AI-native operators.
Related MCP server: Integrations MCP
Search Sales Tools: Find the best enrichment, outreach, or CRM tools via natural language.
API Audits: Get technical details on auth methods, API types, and SDKs.
MCP Discovery: Filter specifically for tools that support the Model Context Protocol.
Starter Prompts: Get copy-paste prompts to immediately integrate tools into your workflow.
Add this to your Claude Code or AI agent config:
{
"mcpServers": {
"salestools": {
"command": "npx",
"args": ["-y", "@salestoolsclub/mcp"]
}
}
}Just tell your AI agent what you need:
"Find me enrichment APIs for my project" "What cold email tools work with HubSpot?" "Search for free CRM tools"
Maintainer: Salestools Club License: MIT
Compare two sales tools side-by-side — API type, auth, pricing, MCP support, AI capabilities, and SDKs.
| Name | Required | Description | Default |
|---|---|---|---|
| tool1 | Yes | Slug of the first tool (e.g., 'hubspot') | |
| tool2 | Yes | Slug of the second tool (e.g., 'pipedrive') |
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 explains what the tool does (compare attributes) but does not disclose any behavioral traits such as whether it fetches live data, if authentication is required, or potential rate limits. The description gives minimal behavioral context beyond the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the action and lists key aspects. Every word adds value, with no redundancy or filler. It front-loads the 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 that there is no output schema and parameters are simple strings, the description adequately explains what the tool compares (specific attributes). It is complete enough for an agent to understand the tool's capabilities, though it could briefly mention the return format or potential errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both 'tool1' and 'tool2' have clear descriptions as tool slugs). The description adds no parameter-specific information beyond the schema, so it meets the baseline without further enrichment.
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 compares two sales tools side-by-side and lists the specific attributes compared (API type, auth, pricing, MCP support, AI capabilities, SDKs). This distinguishes it from sibling tools like get_tool_details (single tool details) and search_sales_tools (listing/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 implies usage when a side-by-side comparison of two specific tools is needed. However, it does not explicitly state when not to use it or mention alternatives (e.g., for single tool details use get_tool_details). The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the copy-paste starter prompt for a tool so you can immediately use it with your AI agent.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool's slug (e.g., 'hubspot', 'apollo', 'lusha') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description only says 'copy-paste starter prompt', failing to disclose any potential side effects, rate limits, or return format. Minimal behavioral disclosure.
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 fluff. Efficiently conveys the tool's purpose and the parameter's role.
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 required parameter and no output schema or annotations, the description sufficiently explains what the tool does and what input is needed. No 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?
Schema coverage is 100% with description for slug. The description adds context by providing example slugs ('hubspot', 'apollo'), enhancing understanding beyond the schema alone.
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 verb 'get', resource 'starter prompt for a tool', and purpose 'immediately use it with your AI agent'. Distinguishes from sibling tools like compare_tools and get_tool_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?
Implied usage when needing a starter prompt, but no explicit when-to-use or when-not-to-use, and no mention of alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full details about a specific sales tool — API specs, auth methods, AI capabilities, starter prompts, MCP config, and more.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool's slug (e.g., 'hubspot', 'apollo', 'lusha', 'pipedrive') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It lists the content provided but does not disclose behavioral traits like idempotency, rate limits, or authentication requirements. The 'get' verb implies read-only, but explicit confirmation is 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?
Single sentence with an enumerated list of content types. No redundant words, and key 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?
Despite lacking an output schema, the description lists specific content categories, making it reasonably complete for a retrieval tool with one parameter. Minor gap: no mention of response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'slug'. The description does not add further meaning beyond the schema's examples. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'Get' and specifies the resource 'full details about a specific sales tool', listing concrete content like 'API specs, auth methods, AI capabilities'. This distinguishes it well from sibling tools like 'compare_tools' and 'get_starter_prompt'.
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 obtaining detailed information about a single tool, which distinguishes it from siblings. However, it does not explicitly state when not to use it or provide alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available sales tool categories with descriptions and tool counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully convey behavior. Only states 'list all', implying a read operation, but no mention of authorization requirements, rate limits, or any side effects. Minimal disclosure.
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, front-loaded with key action and details. 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 zero-parameter list tool, the description adequately covers what is returned (categories, descriptions, counts). Complete given the tool's simplicity.
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?
Input schema has zero parameters (100% coverage). Description adds no parameter info, but none is needed. Baseline for 0-param tools is 4.
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?
Description specifies verb 'list', resource 'sales tool categories', and includes details 'with descriptions and tool counts'. Clearly distinguishes from sibling tools like search_sales_tools and get_tool_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?
No explicit guidance on when to use this tool vs alternatives like search_sales_tools. Usage is implied as a simple overview, but lacks when-not-to-use or alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Salestools Club directory for sales APIs, tools, and MCP servers. Use natural language like 'lead enrichment API' or 'free CRM with webhooks'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query (e.g., 'enrichment API', 'cold email tools', 'CRM for startups') | |
| mcpReady | No | Only return tools with MCP server support | |
| hasFreeTier | No | Only return tools with a free tier | |
| category | No | Filter by category (e.g., 'Sales Intelligence', 'CRM & RevOps', 'Sales Engagement') | |
| limit | No | Max results to return (default: 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, or rate limits. Only implies a search operation.
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?
Very concise: two sentences with no wasted words. Front-loaded with 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?
Despite rich input schema, the description does not mention what the tool returns or any additional context about results. Without output schema, the agent lacks information about the response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds value by showing example queries, going beyond the schema's formal definitions.
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 it searches the Salestools Club directory for sales APIs, tools, and MCP servers, with specific verb and resource. Distinguishes from sibling tools (compare_tools, get_tool_details, etc.) which are not search-focused.
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?
Provides example natural language queries, which hints at usage. However, it does not explicitly state when to use this tool versus alternatives, nor when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: searching, listing categories, getting details, comparing, and extracting starter prompts. No overlap in functionality.
All tool names follow a consistent verb_noun pattern with underscores (e.g., compare_tools, search_sales_tools), making their actions predictable.
Five tools is well-scoped for a sales tool directory, covering browsing, searching, details, comparison, and starter prompts without being excessive.
The tool surface covers core directory operations, but lacks a way to list tools by category directly or filter search results, though search and categories partially address this.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A registry of AI agent tools — MCP servers, APIs, CLIs, SDKs — kept current by automated ingestion.
Find, compare, and audit software for AI agents. Scored registry of tools and MCP servers.
MCP server registry — validated by live handshake, scored on reliability, monitored continuously.
327 tools, 92 providers. Pay per call via x402 + MPP. One MCP endpoint.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/akhilkannur/salestools-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
