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Stelar Signals

by io.github.StelarDigital
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URL / text summarizer

summarize
Read-only

Tight 3-5 sentence summary of a URL or a raw text block, via an LLM. Give it EXACTLY ONE of url (a page to fetch and summarize) or text (raw text to summarize directly). For agents/pipelines that need a fast, information-dense digest without running their own LLM call or web fetch.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "summarize"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlNoPage URL to fetch and summarize
textNoRaw text to summarize directly

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the tool is read-only and non-destructive. The description adds valuable behavioral context beyond annotations: it fetches a page when given a URL, summarizes raw text directly, produces a 3-5 sentence result, and requires exactly one input. This is strong disclosure for a simple read-only 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?

Two sentences with no filler. The output behavior is front-loaded, the parameter rule is explicit, and the use case earns its place. Every sentence conveys actionable information.

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?

For a simple two-parameter, read-only tool with full schema coverage and no output schema, the description covers the input selection rule, the fetch-vs-direct behavior, the output format and length, and the intended use case. Nothing essential is missing for correct invocation.

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 already documents both parameters at 100% coverage, so the baseline is 3. The description adds the critical constraint that exactly one of `url` or `text` must be provided, which the schema does not enforce (zero required params, no oneOf). That meaningful addition pushes it above baseline.

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 states a specific verb ('summarize') with an explicit resource ('a URL or a raw text block') and adds concrete output detail ('tight 3-5 sentence summary, via an LLM'). It also clearly differentiates the two input modes, and the sibling tools are unrelated crypto/finance tools, so no confusion is possible.

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?

It gives clear context: use this when you need a fast, information-dense digest without making your own LLM call or web fetch. It does not explicitly name alternatives or when-not-to-use cases, but the sibling set is unrelated, so the guidance is sufficient for routing.

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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