blackmount-nlp-mcp

get_extractive_summary

Extract key sentences from text to generate a concise summary. Scoring considers sentence position, keyword frequency, length, and title overlap for relevance.

Instructions

Extract the best N sentences as a summary. Scores by position, keyword frequency, length, and title overlap.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "get_extractive_summary"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes
`n_sentences`	No
`title`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Apr 14, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description must disclose behavior. It reveals the scoring dimensions (position, keyword frequency, length, title overlap), which gives insight into how sentences are ranked. It does not mention edge cases, limits, or output format, but the core behavior is well-explained.

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 two sentences with no extraneous words. The first sentence states the core action; the second adds detail. It is front-loaded and efficient.

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?

Given the tool's complexity (3 parameters, output schema exists), the description covers the purpose and scoring but lacks parameter details and usage context. It is adequate but not comprehensive, especially without annotations.

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 coverage is 0%, so description must compensate. It explains that 'title' is used for title overlap scoring, which adds meaning beyond the schema. However, 'text' and 'n_sentences' are not further described; the schema already has names and defaults, so the description adds only partial value.

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 verb 'Extract' and the resource 'summary', specifying it selects the best N sentences. It lists scoring factors (position, keyword frequency, length, title overlap) which distinguishes it from sibling tools like keyword extraction or readability indices.

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?

No explicit usage guidance is provided. The description implies when to use it (for extractive summarization), but does not exclude cases or name alternatives. Among siblings, there are no other summarization tools, so the need is limited, but the description could clarify it is for extracting key sentences from a text.

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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