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Extend Context From Files

extend_context_from_files
Read-onlyIdempotent

Load multiple files, compress them into temporary libraries, and retrieve context for any query to understand large codebases without permanent storage.

Instructions

Loads multiple files, compresses them into temporary libraries, and retrieves relevant context for a given query. Perfect for quickly understanding large codebases or document sets without creating permanent libraries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathsYesArray of file paths to load and compress
queryYesThe question to answer using extended context
top_kNoNumber of chunks to retrieve per file (default: 8)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
files_processedYes
total_chunks_retrievedYes
extended_contextYes
total_wordsYes
compression_statsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context by explaining that files are compressed into temporary libraries, which clarifies the ephemeral nature and the internal processing that occurs. It does not contradict the annotations and enriches the agent's understanding of side effects.

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 concise and front-loaded, with the core behavior stated in the first sentence. The second sentence provides a use case but partially repeats 'permanent libraries' from the first sentence, which is a minor redundancy. Overall, it is efficient with negligible waste.

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?

The tool has full schema coverage, annotations, and an output schema, so the description does not need to explain return values. It clearly conveys the tool's purpose, usage context, and behavioral characteristics (temporary libraries). Nothing critical is missing for an agent to select and invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema completely documents all parameters (query, top_k, file_paths). The description does not add significant meaning beyond the schema, aside from implying that files are processed and a query is answered. This meets the baseline for fully covered schemas but does not exceed it.

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 specifies the tool's function: loading multiple files, compressing them into temporary libraries, and retrieving context for a query. It distinguishes itself from permanent library tools by emphasizing 'temporary libraries' and 'without creating permanent libraries.' The purpose is unambiguous and differentiates from siblings like create_knowledge_library.

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 provides clear situational context: use for quickly understanding large codebases or document sets without permanent libraries. It implies a contrast with permanent library creation tools, but does not explicitly name alternatives or state when not to use. This is clear context without formal exclusion criteria.

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