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yuplin2333

MCP Long Context Reader

by yuplin2333

summarize_with_sequential_notes

Reads a document or text sequentially to synthesize notes focused on a specific question, enabling query-aware summarization of long content without loading it entirely into context.

Instructions

Reads a document or string sequentially to synthesize query-aware notes.
Exactly one of context_path or context_text must be provided. Do NOT provide both.
Note: This operation is resource-intensive and can be time-consuming.

Args:
    context_path (str): The path to the context file.
    context_text (str): The text content to synthesize notes from.
    question (str): The goal of the note-taking.

Returns:
    str: A string containing the synthesized notes, focusing on the question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionNo
context_pathNo
context_textNo
Behavior4/5

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

With no annotations, the description carries the disclosure burden. It openly states the operation is 'resource-intensive' and 'time-consuming', and that it reads 'sequentially', which are useful behavioral traits beyond the simple action. It does not list side effects, but none are apparent for a note-taking tool.

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 compact and well-structured: a one-sentence summary, a constraint note, a performance warning, an Args list, and a Returns line. Every sentence provides necessary information with no redundancy.

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

Completeness4/5

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

Given no annotations and no output schema, the description provides adequate context: purpose, parameters, return type, and performance characteristics. It could better address relative usage against siblings, but overall it is sufficiently complete for an agent to invoke the tool correctly.

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 schema has 0% description coverage, so the description must explain parameters. It lists all three arguments with brief meanings (context_path as file path, context_text as text content, question as goal) and clarifies the critical mutual exclusivity constraint. This goes well beyond the bare 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 the tool reads a document or string sequentially to synthesize query-aware notes. The use of 'sequentially' distinguishes it from the sibling 'summarize_with_map_reduce', making its specific purpose clear.

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?

The description gives explicit constraints on parameter usage ('Exactly one of context_path or context_text must be provided. Do NOT provide both.') and a warning about resource intensity. However, it does not explicitly compare against sibling tools or state when to prefer this over them, so it misses the highest level of guidance.

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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