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postExamsidSessionssessionIdGenerate_overall_feedback

Generate comprehensive feedback for an entire exam session to assess overall performance and identify areas for improvement.

Instructions

Generate feedback for the test as a whole in an exam session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
sessionIdYes

Implementation Reference

  • src/server.py:96-101 (registration)
    The tool 'postExamsidSessionssessionIdGenerate_overall_feedback' is registered here as part of all tools generated from the OpenAPI specification of the Examplary API using FastMCP.from_openapi. The spec is fetched from https://api.examplary.ai/openapi and patched to remove certain endpoints.
    print("Creating MCP server from OpenAPI spec...", file=sys.stderr)
    mcp = FastMCP.from_openapi(
        openapi_spec=openapi_spec,
        client=client,
        name="Examplary"
    )
  • HTTP client used by all generated tool handlers to make authenticated requests to the Examplary API endpoints, including the one for generating overall feedback in exam sessions.
    client = httpx.AsyncClient(
        base_url="https://api.examplary.ai",
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        },
        timeout=30.0
    )
  • Patches the OpenAPI spec before tool generation, excluding certain endpoints like those related to API keys and OAuth, ensuring the generate_overall_feedback endpoint is available unless excluded.
    def patch_openapi_spec(spec: dict) -> dict:
        """Patch OpenAPI spec to fix validation issues and filter endpoints"""
        # Remove unwanted endpoints
        excluded_patterns = [
            "api-key",
            "api_key",
            "library",
            "libraries",
            "oauth"
        ]
    
        if "paths" in spec:
            paths_to_remove = [
                path for path in spec["paths"].keys()
                if any(pattern in path.lower() for pattern in excluded_patterns)
            ]
    
            for path in paths_to_remove:
                print(f"Excluding endpoint: {path}", file=sys.stderr)
                del spec["paths"][path]
    
            # Add missing description fields to response objects
            for path, path_item in spec["paths"].items():
                for method, operation in path_item.items():
                    if method in ["get", "post", "put", "patch", "delete"] and "responses" in operation:
                        for status_code, response in operation["responses"].items():
                            # Add description if missing
                            if isinstance(response, dict) and "description" not in response:
                                response["description"] = f"Response for {method.upper()} {path}"
    
        return spec
  • src/server.py:63-104 (registration)
    Full function that fetches OpenAPI spec, patches it, creates HTTP client, and registers all tools via FastMCP.from_openapi.
    def create_server() -> FastMCP:
        """Create and configure the Examplary MCP server"""
        # Get API key
        api_key = get_api_key()
    
        # Fetch the OpenAPI specification
        print("Fetching OpenAPI specification...", file=sys.stderr)
        try:
            response = httpx.get("https://api.examplary.ai/openapi", timeout=30.0)
            response.raise_for_status()
            openapi_spec = response.json()
            print(f"Successfully loaded OpenAPI spec (version {openapi_spec.get('openapi', 'unknown')})",
                  file=sys.stderr)
    
            # Patch the spec to fix validation issues
            print("Patching OpenAPI spec to fix validation issues...", file=sys.stderr)
            openapi_spec = patch_openapi_spec(openapi_spec)
    
        except Exception as e:
            print(f"ERROR: Failed to fetch OpenAPI spec: {e}", file=sys.stderr)
            sys.exit(1)
    
        # Create authenticated HTTP client
        client = httpx.AsyncClient(
            base_url="https://api.examplary.ai",
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json"
            },
            timeout=30.0
        )
    
        # Create MCP server from OpenAPI specification
        print("Creating MCP server from OpenAPI spec...", file=sys.stderr)
        mcp = FastMCP.from_openapi(
            openapi_spec=openapi_spec,
            client=client,
            name="Examplary"
        )
    
        print("MCP server created successfully!", file=sys.stderr)
        return mcp
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. It states 'Generate feedback' but doesn't clarify if this is a read-only operation, a mutation, or has side effects (e.g., creating data, triggering notifications). It also omits details like authentication needs, rate limits, or response format. 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.

Conciseness4/5

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

The description is a single sentence that is front-loaded and efficient, with no unnecessary words. It directly states the tool's action and target. However, it's arguably too concise given the lack of detail in other dimensions, but as a standalone sentence, it earns a high score for brevity and clarity.

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

Completeness2/5

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

Given the complexity (a POST operation likely involving data generation), no annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't explain what the feedback includes, how it's delivered, or any behavioral traits. For a tool with 2 required parameters and no structured support, the description should provide more context to be useful.

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?

The input schema has 2 parameters (id, sessionId) with 0% description coverage, meaning the schema provides no semantic information. The description adds no parameter details—it doesn't explain what 'id' and 'sessionId' refer to (e.g., exam ID and session ID), their formats, or examples. This fails to compensate for the low schema coverage, leaving parameters undocumented.

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

Purpose3/5

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

The description states the action ('Generate feedback') and target ('for the test as a whole in an exam session'), which provides a basic purpose. However, it's vague about what 'feedback' entails and doesn't distinguish this tool from sibling tools like 'postExamsidSessionssessionIdFeedback' or 'postExamsidSessionssessionIdOverall_feedback', which appear related. The description lacks specificity about the type of feedback generated.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., requiring an existing exam session), exclusions, or comparisons to sibling tools like 'postExamsidSessionssessionIdFeedback' or 'postExamsidSessionssessionIdOverall_feedback'. Without this context, an agent must infer usage from tool names alone.

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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