Skip to main content
Glama

callout_help

Get started with Callout by learning its capabilities and usage instructions for AI-powered code review, tech guidance, and project management.

Instructions

Show what Callout can do and how to use it. Call this when a user first connects or asks about Callout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'callout_help' tool implementation - registered with server.tool() and contains the async handler function that returns comprehensive help documentation about all available Callout tools and their usage examples. The tool takes no parameters (empty schema {}) and returns formatted markdown text with tool descriptions and example commands.
      server.tool(
        'callout_help',
        'Show what Callout can do and how to use it. Call this when a user first connects or asks about Callout.',
        {},
        async () => {
          return {
            content: [{
              type: 'text' as const,
              text: `# Callout — Your AI Co-founder for 0→1 Builds
    
    ## Core Tools
    
    ### review — "Get expert opinions on my project"
    9 perspectives in 3 groups: Technical (CTO, Security, DevOps), Business (Product, Customer, Strategy), Founder (Investor, Unicorn Founder, Solo Entrepreneur). Use \`focus\` to zoom in, \`perspective_group\` to select a group.
    
    **Try:**
    - "Review this project" — full 9-perspective review
    - "Review with founder group" — investor + unicorn founder + solo entrepreneur
    - "Review focus: should I use Supabase or Firebase?" — technology decision
    - "Review focus: user login page" — focused feature review
    - "Review security + CTO only" — selected perspectives
    
    ### coach — "Am I working with AI the right way?"
    Analyzes your project setup, development habits, and knowledge blind spots. Reveals what you don't know you're doing wrong when working with AI coding tools.
    
    **Try:** "Coach me" / "How am I doing with AI?" / "Check my collaboration habits"
    
    ### test_translate — "What do my tests actually cover?"
    Translates test results into plain language. Shows what's tested, what failed, and produces a manual test script.
    
    **Try:** "Translate my test results" / "What do these tests mean?"
    
    ### idea_score — "Is this idea worth building?"
    Scores your idea across 10 dimensions (market size, feasibility, moat, revenue, etc.) with a skeptical default stance. Returns a verdict: CONTINUE, SIMPLIFY, PAUSE, or DELETE.
    
    **Try:** "Score this idea" / "Is this worth building?" / "Rate my project"
    
    ## Supporting Tools
    
    ### recommend — "What tools should I use for this?"
    Detects what your project needs (auth, database, payments, etc.) and recommends the best tool.
    
    **Try:** "What should I use for auth?" / "I need to add payments"
    
    ### Todo List — Your central command
    All findings from review and coach flow into your todo list.
    Tools: todo_add, todo_update, todo_list, todo_summary
    
    **Try:** "Show my todos" / "What's my top priority?"
    
    ### portfolio — "What should I work on across all my projects?"
    Scans all your projects, shows health status, and gives resource allocation advice.
    
    **Try:** "Show my portfolio"
    
    ### Other: set_target_user, save_review_findings, recommend_dismiss, recommend_reset, init
    
    ---
    
    **Start here:** Say "Coach me" to check your AI collaboration setup, or "Review this project" for a full expert review.`,
            }],
          };
        },
      );
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool's purpose and usage context, it lacks details about what the tool actually does behaviorally—such as whether it returns a list of features, provides interactive guidance, or displays documentation. For a tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and well-structured, consisting of two sentences that directly address purpose and usage guidelines. Every word earns its place, with no redundant information, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (low, with no parameters) and the lack of annotations and output schema, the description is adequate but incomplete. It covers purpose and usage well, but fails to describe what the tool outputs or how it behaves, which is crucial for an AI agent to understand the result of invocation. This leaves gaps in contextual understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately focuses on purpose and usage. A baseline of 4 is justified as the description compensates well for the lack of parameters by providing clear context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Show what Callout can do and how to use it.' This specifies the verb ('show') and resource ('Callout capabilities and usage'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'init' or 'coach', which might have overlapping introductory functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Call this when a user first connects or asks about Callout.' This clearly defines the triggering conditions (initial connection or user inquiries about Callout), making it easy for an AI agent to decide when to invoke it versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/fantasieleven-code/callout'

If you have feedback or need assistance with the MCP directory API, please join our Discord server