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content-core

by lfnovo
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Python
Hybrid

Summarize Content

summarize_content

Generate concise summaries of text with an LLM, and pass optional context to control format, focus, or length.

Instructions

Summarize content using LLM with optional context. Requires OPENAI_API_KEY (or another LLM provider key) to be configured.

Input Schema

NameRequiredDescriptionDefault
contentYesThe text content to summarize
contextNoOptional context to guide summarization (e.g., "summarize as bullet points")

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.1.1
    • addedInput schema / properties / content / description
      Added value: +"The text content to summarize"
    • addedInput schema / properties / context / description
      Added value: +"Optional context to guide summarization (e.g., \"summarize as bullet points\")"
  2. Addedv2.0.4

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses a real prerequisite — an LLM provider key such as OPENAI_API_KEY must be configured — which is not in the schema. However it says nothing about cost, latency, or truncation behavior on long input, leaving gaps for a tool that calls an external LLM.

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?

Two compact sentences with no filler, and the core purpose is front-loaded ahead of the setup requirement. Slightly terse, but every sentence earns its place.

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?

Output schema exists so return values need no explanation, and both parameters are schema-documented, making the definition largely adequate for a simple tool. The gaps are the absence of any routing guidance versus extract_content and the lack of cost/latency context for an external LLM call.

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 both parameters are already fully documented, and the description adds nothing beyond restating that context is optional. Baseline 3 applies when the schema does the heavy lifting.

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

Purpose4/5

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

States a specific verb and resource ('summarize content') plus the mechanism ('using LLM'), which distinguishes it from the sibling extract_content at a high level. It does not explicitly name or contrast with that sibling, so it falls short of 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 Guidelines2/5

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

There is no guidance on when to choose this tool over extract_content, nor any exclusions or preconditions for use beyond the API-key note. The reader must infer that summarizing is the use case from the name 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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