Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GrowthPilot Find tech startups in Austin for email automation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GrowthPilot is an AI-powered B2B outbound sales automation pipeline, exposed as an MCP (Model Context Protocol) server coupled with a premium, real-time reactive web dashboard.
By orchestrating multiple external APIs through a robust 7-stage self-correcting workflow, GrowthPilot transforms a simple goal like "Find SaaS companies in Bangalore with 20-100 employees" into qualified sales intelligence and personalized, audit-verified emails ready for outreach.
💻 Interactive Frontend (Vercel): https://ui-sigma-snowy.vercel.app
⚙️ Production MCP Backend (NitroStack Cloud): https://growthpilot-6a5af3-hyd-bros-amrita-university-amritapuri-campus.app.nitrocloud.ai
🔌 Live SSE Handshake Endpoint: https://growthpilot-6a5af3-hyd-bros-amrita-university-amritapuri-campus.app.nitrocloud.ai/sse
Related MCP server: LeadClaw
You do not need to install or run anything locally to test GrowthPilot:
Open the Interactive Frontend URL.
Enter a natural language goal in the input field, for example:
"Find commercial printing businesses in Kerala interested in workflow automation"
Click Run Campaign.
Watch the multi-agent pipeline execute in real time across all 7 stages on the live NitroStack Cloud container.
Once complete (~40 seconds), scroll down to see the leads. Click any card to expand the profile drawer to view the generated email copy and the quality audit score!
Standard LLMs with "Deep Research" capabilities can browse the web and answer questions. GrowthPilot takes this further:
Task Accomplishment, Not Just Chat: Instead of conversational answers, it outputs structured, actionable business deliverables (validated leads + ready-to-send copy).
Self-Correcting Pipeline: A dedicated Critic Agent audits the output of the Drafting Agent, scoring it and automatically sending it back for revisions if it falls below our quality threshold.
Open Standard Composability: Because it's an MCP server, any compatible AI client (Claude Desktop, Cursor, ChatGPT, custom CLI tools) can plug directly into the GrowthPilot server and gain these B2B prospecting tools instantly.
graph TD
A["🎯 User Goal Input"] --> B["📋 1. Planner Agent (Gemini)"]
B --> C["🔍 2. Discovery Agent (Google Places API)"]
C --> D["✅ 3. Validator Agent (Rule-based Filter)"]
D --> E["🔬 4. Research Agent (Tavily Search API)"]
E --> F["📊 5. Qualification Agent (Gemini)"]
F --> G["✉️ 6. Drafting Agent (Gemini)"]
G --> H["🧪 7. Critic Agent (Gemini Audit)"]
H -->|"Score < 0.8 (Up to 3 Revisions)"| G
H -->|"Score >= 0.8"| I["🚀 Ready Outbound Campaign"]Planner: Parses user goals into structured campaign parameters (target industry, locations, filters).
Discovery: Queries Google Places live for matching businesses.
Validator: A zero-cost deterministic gate that filters out invalid industries, categories, or keywords.
Research: Crawls the web and scrapes online presence using Tavily.
Qualification: LLM-based scoring that assigns leads to High, Medium, or Borderline tiers based on fit.
Draft: Generates hyper-personalized cold outreach emails.
Critic: Audits emails for tone, placeholders, and length, revising automatically up to 3 times to hit a 0.8 quality score.
GrowthPilot provides a complete set of Model Context Protocol features:
Tools:
gp_run_pipeline: Orchestrates and runs the entire 7-stage campaign end-to-end.
Resources:
growthpilot://system/status: Exposes real-time server health and active providers.
Prompts:
gp_campaign_brainstorm: Assists users in brainstorming high-yield campaign goals based on their industry.
The repository is structured as a monorepo containing:
/ (Root): The TypeScript + Node.js NitroStack MCP Server.
/ui: The React + Vite + TypeScript Frontend dashboard.
Clone the repository and install dependencies:
npm installCreate a .env file in the root based on .env.example:
PORT=3000
GEMINI_API_KEY=your_gemini_key
TAVILY_API_KEY=your_tavily_key
GOOGLE_MAPS_API_KEY=your_google_maps_key
LIVE_MODE=trueBuild and start the development server:
npm run dev(Optional) Run the test suite:
npm run testNavigate to the /ui directory and install dependencies:
cd ui
npm installStart the Vite development server (configured to proxy /sse and /mcp to the backend on port 3000):
npm run devOpen http://localhost:5173 in your browser.
Credentials for all live APIs (Gemini, Google Maps, Tavily) are securely handled at the environment level inside the NitroStack Cloud container. No raw keys are exposed to the browser or checked into source control.
Production builds compile cleanly with TypeScript ESM imports.
Audits a cold email draft for placeholder flags, styling, and factual accuracy using Gemini API.
| Name | Required | Description | Default |
|---|---|---|---|
| draft | Yes | The Email Draft to analyze | |
| campaignId | No | The Campaign ID reference | |
| researchTime | No | The research verification timestamp |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden. It discloses the use of Gemini API and the scope of the audit. However, it does not specify read-only behavior, idempotency, rate limits, or side effects. Adequate 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?
Single sentence that front-loads the verb and resource, clear and efficient. 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?
No output schema provided, yet the description fails to hint at the return value (e.g., audit report, flags). For a tool with a nested object and three parameters, the description is too brief to cover all necessary context beyond the 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 100%, setting baseline at 3. The description adds context about what the tool does with parameters (e.g., audting draft, using researchTime) but does not explain parameter specifics beyond the schema. Minimal added 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 the verb 'Audits' and the resource 'cold email draft', specifying three audit aspects: placeholder flags, styling, and factual accuracy. This distinguishes it from sibling tools like gp_generate_email_draft and gp_revise_draft.
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 or when not to use. It implies use after drafting but does not compare with siblings or mention prerequisites. Minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches for and discovers target companies matching the campaign parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| campaign | Yes | The Campaign parameters |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose behavioral traits such as side effects, permissions, rate limits, or return characteristics. With no annotation support, the description should cover safety and behavior but falls short.
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 waste. Front-loaded with action and object. Could benefit from a second sentence (e.g., return format) without losing conciseness.
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?
No output schema and description does not mention what is returned (list of companies, IDs, etc.). For a discovery tool, this is a significant gap. The description is too terse for an agent to understand full invocation impact.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema documents all parameters. However, the description adds minimal semantic value beyond 'matching the campaign parameters'. No explanation of how each field (e.g., maxEmployees, targetIndustry) modifies the search.
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 uses verb 'Searches for and discovers' with resource 'target companies', giving a clear purpose. It distinguishes from siblings like gp_research_company (which researches specific companies) but could be more specific (e.g., 'discovers new companies from campaign parameters').
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 vs alternatives (e.g., gp_research_company for existing companies). No explicit when-to or when-not context. Only inferred from tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates a highly personalized cold sales email outreach draft for a lead using Gemini API reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | The user campaign goal | |
| score | Yes | The QualificationScore details | |
| company | Yes | The target Company details | |
| research | Yes | The Company web research profile | |
| campaignId | No | The campaign ID reference |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the use of Gemini API but does not mention side effects, whether the draft is saved, or any state mutation. Minimal behavioral context.
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 sentence that is efficient and front-loaded. Could be slightly more concise, but 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?
The tool has 5 parameters with nested objects and no output schema. The description is too brief, failing to explain how parameters like goal or score are used or what the output format (likely an email string) will be.
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%, so the schema already documents all parameters. The description adds no extra meaning beyond the schema, meeting 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 verb 'generates', the resource 'cold sales email outreach draft for a lead', and the method 'using Gemini API reasoning'. It distinguishes from siblings like gp_plan_campaign and gp_critique_draft which have 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?
No guidance on when to use this tool versus alternatives like gp_critique_draft or gp_revise_draft. The description does not provide any when/when-not or context for choosing this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Processes a user goal (e.g. "Find SaaS companies in Bangalore with 20-100 employees") and plans the campaign parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | The user campaign goal to parse and plan |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the full burden. It only says 'processes and plans' without indicating side effects, state changes, or permissions. For a planning tool, it should clarify if it is read-only or modifies data.
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, clear sentence with no redundancy. Could be slightly improved by front-loading the purpose more directly, but overall efficient.
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?
No output schema or explanation of return value. Does not describe side effects, error conditions, or campaign parameter details. Incomplete for a planning tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the only parameter 'goal'. The tool description adds an example, but does not significantly enhance meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it processes a user goal and plans campaign parameters. It distinguishes from siblings like gp_discover_companies and gp_run_pipeline by specifying planning vs discovery or execution.
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 when or when-not to use this tool. It implies use for campaign planning before execution, but lacks alternatives or context compared to siblings like gp_run_pipeline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculates the sales qualification fit score for a discovered company using Gemini API reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | The target Company details | |
| campaign | Yes | The Campaign parameters | |
| research | Yes | The Company web research profile |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must bear the full burden. It mentions 'using Gemini API reasoning' but does not disclose side effects (is it read-only?), cost implications, rate limits, or whether state is modified. For a tool that likely triggers an external API call, this is a significant gap.
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 conveys the core function without any extraneous words. It efficiently captures what the tool does, which is appropriate for a focused scoring 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?
Despite high schema coverage and no output schema, the description is too minimal. It does not explain what the fit score represents, how it should be interpreted, or what the tool returns. Given the tool computes a qualification score and has no output schema, more contextual details are needed for proper use.
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 basic descriptions for each object (e.g., 'The target Company details'). The description adds the context that these parameters are used to compute a fit score, but does not explain individual fields beyond the schema. This is adequate but adds little extra meaning.
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 calculates a sales qualification fit score for a discovered company, using Gemini API reasoning. The verb 'calculates' and resource 'sales qualification fit score' are specific. In context of siblings like gp_discover_companies and gp_research_company, this tool's purpose as the qualification step is 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?
Usage is implied from the naming and sibling tools (e.g., gp_research_company before, gp_generate_email_draft after), but no explicit when-to-use or when-not-to-use guidance is provided. The description does not state prerequisites or alternatives, relying on the pipeline ordering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Runs deep web research on a specific company using Tavily search API.
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | The target Company details |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the full burden. It only mentions using Tavily API but doesn't disclose whether the tool is read-only, if it has rate limits, or what behavioral side effects exist. The term 'deep web research' is vague.
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?
Extremely concise single sentence with no redundant information. Every word is necessary and 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?
The tool has a nested object parameter and no output schema, but the description does not explain what the tool returns or how to interpret results. Missing behavioral context like whether the research is synchronous or asynchronous.
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% for the outer parameter, but the nested object fields lack descriptions. The description adds no additional meaning beyond the schema, which already documents the company object with its required fields. Baseline 3 applies due to high coverage.
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 runs deep web research on a specific company using Tavily. It specifies the verb and resource, but doesn't explicitly differentiate from siblings like gp_discover_companies which also involve company research.
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 vs alternatives like gp_discover_companies or gp_run_pipeline. No context on prerequisites or 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.
Revises a cold email draft using the feedback and suggestions from a critique.
| Name | Required | Description | Default |
|---|---|---|---|
| draft | Yes | The original Draft email | |
| critique | Yes | The Critique feedback to apply |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must fully disclose behaviors. It only says 'revises' without explaining if the draft is mutated or a new version created, nor any permissions, rate limits, or side effects. Significant gaps.
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 is concise and efficient, with no wasted words. However, it could benefit from structure (e.g., bullet points) to improve readability and scanning.
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?
Tool has nested objects and no output schema. Description does not explain what the revised result looks like, how it relates to the pipeline (generate → critique → revise), or any workflow expectations. Incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. Description adds brief context: 'original Draft email' and 'Critique feedback to apply', which is helpful but does not deeply explain parameter semantics beyond field names.
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 states verb 'revises' and resource 'cold email draft', clearly distinguishing from sibling tools gp_generate_email_draft and gp_critique_draft. Purpose is specific and unambiguous.
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 after critique input, but does not explicitly state when to use or when to avoid. No alternatives or context provided, making it merely implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Executes the entire GrowthPilot lead generation campaign pipeline end-to-end for a given user goal.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | The campaign goal, e.g. "Find SaaS companies in Bangalore with 20-100 employees" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility for behavioral disclosure. It does not mention possible side effects, duration, idempotency, or return value. 'Executes the entire pipeline' is vague and omits crucial traits like whether it triggers a long-running process or modifies state.
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, efficient sentence with no redundant words. However, it could be slightly expanded to improve completeness without harming conciseness.
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 complexity of a pipeline tool with no annotations or output schema, the description is insufficient. It does not explain that the pipeline may involve multiple steps or how the output relates to individual tool results. More context is needed for agents to use it reliably.
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 sole parameter 'goal' is well-described in the schema with an example. The description adds no extra parameter information. With 100% schema coverage, the baseline score of 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 clearly states the verb ('executes'), the object ('entire GrowthPilot lead generation campaign pipeline'), and the scope ('end-to-end for a given user goal'). It distinguishes this tool from sibling tools which handle individual steps like planning or 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 provided on when to use this tool versus alternatives. For example, it does not clarify that this should be used after setting a goal, or that individual steps might be preferred for inspection. The agent receives no hints about its role relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A simple test tool that returns a pong message to verify the NitroStack server is operational.
| Name | Required | Description | Default |
|---|---|---|---|
| message | No | An optional test message to send | ping |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It indicates a read-only, non-destructive test returning a pong message. Adequate for this simple 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?
One concise sentence that fully describes the tool with 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?
For a simple test tool with no output schema, the description is sufficient. It does not detail the pong response format but that is acceptable 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?
Parameter has full schema coverage. The description adds 'optional test message' but schema already provides similar detail. 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 clearly states it is a simple test tool that returns a pong message to verify server operability. It is distinct from sibling tools which are data processing and campaign tools.
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 verifying server status. While it doesn't explicitly state when not to use it, the context is clear for a simple health check tool.
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.
v1.0.0gp_critique_draftgp_discover_companiesgp_generate_email_draftgp_plan_campaigngp_qualify_leadgp_research_companygp_revise_draftgp_run_pipelinepingEach tool has a clear and distinct purpose covering different steps in the lead generation pipeline, from planning to revision. No two tools have overlapping functionality.
All tool names follow a consistent pattern: 'gp_' prefix plus verb_noun in snake_case, e.g., gp_plan_campaign, gp_discover_companies. This makes the tool set predictable and easy to navigate.
With 9 tools, the server is well-scoped for its purpose. Each tool covers a necessary step in the pipeline without redundancy or bloat.
The tool set covers the main lifecycle of a lead generation campaign: planning, discovery, research, qualification, email generation, critique, and revision. A minor gap is the lack of a tool for sending emails or managing outcomes, but the core flow is well-covered.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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