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СДАМ ГИА MCP Server

by art22017

Batch Retrieve Multiple Problems

sdamgia_batch_get_problems
Read-onlyIdempotent

Retrieve multiple complete exam problems from СДАМ ГИА database in a single request with detailed solutions and answers.

Instructions

Efficiently retrieves multiple complete problems from the СДАМ ГИА database in a single request.

When to use:

  • You have multiple problem IDs and need all their details

  • You want to compare several problems side-by-side

  • You're building a problem set or practice collection

  • You need to fetch related problems after a search

  • You want to reduce API calls compared to individual get_problem requests

Parameters:

  • subject (required): Subject code for all problems (all IDs must belong to this subject)

  • problem_ids (required): Array of problem IDs to fetch. Must include 1-10 problem IDs as numeric strings (e.g., ["12345", "67890", "54321"])

  • response_format (optional): 'markdown' (default) or 'json'

Returns:

  • problems: Array of complete problem objects, each containing:

    • condition: Full problem statement with text and optional HTML/images

    • solution: Detailed step-by-step solution

    • answer: The correct answer

    • similar_problems: Related problem IDs

    • metadata: Problem ID, subject, difficulty level

  • total: Number of problems successfully fetched

Response format:

  • Markdown: Formatted text with each problem in a separate section, clearly delineated with problem IDs

  • JSON: Structured object with problems array and metadata

Example usage:

{
  "subject": "math",
  "problem_ids": ["12345", "67890", "54321", "11111", "22222"],
  "response_format": "markdown"
}

Typical workflow:

  1. Use sdamgia_search_problems to find relevant problem IDs

  2. Pass the IDs to this tool for batch retrieval

  3. Review all problems together for comparison or practice

Performance benefits:

  • Single API call instead of multiple individual calls

  • Faster than sequential sdamgia_get_problem requests

  • Ideal for fetching 2-10 problems at once

  • Reduces network overhead and latency

Constraints:

  • Maximum 10 problems per batch request

  • All problem IDs must be valid numeric strings

  • All problems must be from the same subject

  • Invalid IDs will cause the entire batch to fail

  • Fetching many problems may return large responses

Error handling:

  • If any problem ID is invalid or not found, the entire batch fails

  • Make sure all IDs exist in the subject before batching

  • Consider splitting into smaller batches if you encounter errors

Notes:

  • Batch size is limited to 10 to prevent excessive response sizes

  • Use when you need full problem details, not just IDs

  • For searching, use sdamgia_search_problems first

  • Each problem includes similar problems for extended practice

  • All problems in batch are fetched in parallel for speed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subjectYesSubject code
problem_idsYesArray of problem IDs to fetch
response_formatNomarkdown
Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint. Description adds that entire batch fails on invalid ID, parallel fetching, and return format details, providing good context beyond annotations.

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?

Well-structured with sections, bullet points, example usage, typical workflow, performance benefits, constraints, error handling. Content is front-loaded and every sentence adds value for a batch tool.

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

Completeness5/5

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

No output schema, but description thoroughly explains return structure, usage, constraints, error handling, performance, and workflow with sibling tools. Complete for the tool's complexity.

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?

Schema has 67% description coverage. Description enriches parameters with usage details (numeric strings, max 10, must be same subject) and explains return structure (problems with condition, solution, etc.) not in schema.

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

Purpose5/5

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

The description clearly states it retrieves multiple complete problems in a single request, which distinguishes it from sibling tools like sdamgia_get_problem (individual) and search tools.

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?

Provides explicit 'When to use' bullet points, mentions alternatives (individual get_problem for fewer, search for finding IDs), and includes constraints (max 10, same subject, batch fails on invalid IDs).

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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