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yuplin2333

MCP Long Context Reader

by yuplin2333

summarize_with_map_reduce

Summarize long documents via map-reduce: break text into chunks, summarize each, then combine. Provide file path or text content to get an overall summary.

Instructions

Summarizes a document or string using a map-reduce approach.
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 summarize.
    question (str): The question for each chunk to answer.

Returns:
    str: A string containing the overall summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionNo
context_pathNo
context_textNo
Behavior3/5

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

The description discloses that the operation is resource-intensive and time-consuming, and sets a constraint on inputs, but lacks details on error behavior, side effects, or process specifics. Since no annotations are provided, this partial disclosure earns a 3.

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 well-organized with clear sections (purpose, constraint, args, returns) and is not unnecessarily verbose, though the parameter list duplicates schema names but adds necessary detail.

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?

The description covers the essential invocation details: purpose, parameters, return type, and a behavioral warning. However, it lacks explicit guidance on when to choose this tool over the sibling summarization tool, and it doesn't explain the map-reduce process or potential limitations, leaving some context gaps given the absence of annotations and output schema.

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

Parameters5/5

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

The schema provides no descriptions, but the description compensates fully by explaining each parameter (context_path, context_text, question) and adding the mutual exclusivity rule. This adds significant meaning beyond the 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's function with a specific verb ('Summarizes') and resource ('document or string'), and specifies the 'map-reduce approach' which distinguishes it from the sibling tool 'summarize_with_sequential_notes'. This meets the criteria for a 5.

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

Usage Guidelines3/5

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

The description provides usage context by stating exactly one of context_path or context_text must be provided and warns about resource intensiveness, but it does not explicitly compare to alternatives like 'summarize_with_sequential_notes' or state when this approach is preferred. This is implied usage, not explicit 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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