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sugo

sugo

by Muralnick
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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estimate_savings

Read-onlyIdempotent

Compare token counts saved by sending Sugo Markdown instead of source files, with raw vs. cleaned text and image costs per LLM provider.

Instructions

Report the tokens saved by sending Sugo's Markdown instead of the file: raw against cleaned text, and for images the image cost per LLM provider.

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": {
      "path": "<path>"
    },
    "name": "estimate_savings"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesFile to read, inside a folder the user allowed; relative paths are taken from those folders.
providersNoProviders to compare (claude, gemini, openai); all if omitted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, so the safety profile is covered. The description adds that the tool compares raw file cost against cleaned Markdown and computes per-provider image cost, which is genuinely useful context. It stops short of explaining what is returned (counts, percentages, per-provider breakdown) or whether the file is fully processed at read time.

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?

A single sentence with no filler, and the core action is stated first. The wording is compressed to the point of being slightly hard to parse, but no clause is redundant.

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?

With no output schema and a tool whose sole purpose is producing a report, the description should at least sketch the return shape (token counts, comparisons, per-provider image costs). It hints at the compared quantities but leaves the agent unable to predict the response structure.

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 `path` and `providers` are already documented in the schema, including the allowed provider values. The description's mention of "per LLM provider" loosely reinforces the `providers` parameter but adds no format or default semantics beyond the schema. Baseline 3 applies.

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

Purpose3/5

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

"Report the tokens saved" names a verb and a resource, but the follow-on clauses ("raw against cleaned text", "image cost per LLM provider") are dense and ambiguous about what exactly is being compared. It never references the sibling convert_document or explains how this differs from it, so the agent must infer the boundary.

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 when-to-use or when-not-to-use guidance, no prerequisite statement, and no mention of the alternative convert_document. The agent is left to guess whether this is a pre-conversion estimate or a post-conversion report.

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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