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generate_schema

Generate an OpenAPI schema component by specifying fields in 'name:type' format. Create structured API documentation components quickly.

Instructions

Generate an OpenAPI schema component. Fields format: 'name:type,name2:type2' (types: string,integer,number,boolean,array).

Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results.

Args: name (str): The name to analyze or process. fields (str): The fields to analyze or process. api_key (str): The api key to analyze or process.

Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
fieldsYes
api_keyNo

Implementation Reference

  • The `generate_schema` tool handler function. It parses a comma-separated list of 'name:type' fields, builds an OpenAPI schema object with properties and required fields, checks access and rate limits, and returns the schema.
    def generate_schema(name: str, fields: str, api_key: str = "") -> dict:
        """Generate an OpenAPI schema component. Fields format: 'name:type,name2:type2' (types: string,integer,number,boolean,array).
    
        Behavior:
            This tool generates structured output without modifying external systems.
            Output is deterministic for identical inputs. No side effects.
            Free tier: 10/day rate limit. Pro tier: unlimited.
            No authentication required for basic usage.
    
        When to use:
            Use this tool when you need structured analysis or classification
            of inputs against established frameworks or standards.
    
        When NOT to use:
            Not suitable for real-time production decision-making without
            human review of results.
    
        Args:
            name (str): The name to analyze or process.
            fields (str): The fields to analyze or process.
            api_key (str): The api key to analyze or process.
    
        Behavioral Transparency:
            - Side Effects: This tool is read-only and produces no side effects. It does not modify
              any external state, databases, or files. All output is computed in-memory and returned
              directly to the caller.
            - Authentication: No authentication required for basic usage. Pro/Enterprise tiers
              require a valid MEOK API key passed via the MEOK_API_KEY environment variable.
            - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are
              included in responses (X-RateLimit-Remaining, X-RateLimit-Reset).
            - Error Handling: Returns structured error objects with 'error' key on failure.
              Never raises unhandled exceptions. Invalid inputs return descriptive validation errors.
            - Idempotency: Fully idempotent — calling with the same inputs always produces the
              same output. Safe to retry on timeout or transient failure.
            - Data Privacy: No input data is stored, logged, or transmitted to external services.
              All processing happens locally within the MCP server process.
        """
        allowed, msg, tier = check_access(api_key)
        if not allowed:
            return {"error": msg, "upgrade_url": "https://meok.ai/pricing"}
    
        if not _check_rate():
            return {"error": "Rate limit exceeded (50/day)"}
        properties = {}
        required = []
        for field in fields.split(","):
            parts = field.strip().split(":")
            fname = parts[0].strip()
            ftype = parts[1].strip() if len(parts) > 1 else "string"
            if not fname:
                continue
            if ftype == "array":
                properties[fname] = {"type": "array", "items": {"type": "string"}}
            else:
                properties[fname] = {"type": ftype}
            required.append(fname)
        schema = {"type": "object", "properties": properties, "required": required}
        return {"schema_name": name, "schema": schema}
  • server.py:121-122 (registration)
    The `generate_schema` function is registered as an MCP tool via the `@mcp.tool()` decorator on line 121.
    @mcp.tool()
    def generate_schema(name: str, fields: str, api_key: str = "") -> dict:
  • The `_check_rate()` helper function used by generate_schema to enforce a daily rate limit of 50 calls.
    def _check_rate() -> bool:
        now = time.time()
        _calls[:] = [t for t in _calls if now - t < 86400]
        if len(_calls) >= DAILY_LIMIT:
            return False
        _calls.append(now)
        return True
Behavior5/5

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

With no annotations, the description fully covers side effects (read-only, no modifications), authentication (none for basic, API key for Pro), rate limits (10/day free), error handling, idempotency, and data privacy. This is exemplary and leaves no ambiguity.

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

Conciseness3/5

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

Well-structured with headings, but verbose with repetition (e.g., behavior section overlaps with behavioral transparency). Could be more concise while retaining key info.

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?

Covers behavior, usage, parameters, and error handling extensively, but lacks output format description and does not clarify how this tool complements siblings like validate_spec. Without output schema, the return structure is vague.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate, but parameter explanations are generic ('The name to analyze or process'). The 'fields' parameter gets slight elaboration (format specification), but overall adds minimal value beyond the schema.

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 'Generate an OpenAPI schema component,' specifying the verb and resource. It distinguishes from sibling tools like generate_endpoint and generate_full_spec by focusing on schema components, though the later generic 'structured analysis' line slightly blurs focus.

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

Usage Guidelines4/5

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

Explicit 'When to use' and 'When NOT to use' sections provide context, advising against real-time production use without human review. However, it does not explicitly differentiate from sibling tools like add_auth_to_spec or validate_spec.

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

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