Trust Engine MCP

benchmark_results

Compare Claude Opus, Sonnet and Haiku 5.5 cost per solved exercise on a public Aider benchmark to decide whether a cheaper model is good enough for coding tasks.

Instructions

Use when asked whether a cheaper Claude model is good enough, or for evidence on cost vs quality: Trust Engine AI's public Aider benchmark (27 coding exercises with hidden tests, Nov 2026) for Claude Opus, Sonnet and Haiku 5.5: exercises solved, cost per solved exercise, which exercises each failed, method (protocol fixed before the run) and raw data links. One benchmark, not your tasks.

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": "benchmark_results"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 12, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the load and does it well: it discloses data vintage (Nov 2026), methodology (protocol fixed before the run), coverage (27 exercises, three models) and links to raw data. It omits operational traits such as whether results are a static snapshot, live-fetched, or cacheable, but the substantive disclosure is strong.

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?

Front-loaded with the usage trigger and then packed with concrete detail; every clause (model list, cost metric, failure data, method, raw links) adds information rather than restating the name. It is dense and heavily punctuated, but not padded.

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?

An output schema exists, so return-value explanation is unnecessary, and the description still summarizes the payload well enough to set expectations. For a zero-parameter benchmark lookup, nothing an agent needs in order to call or interpret it is missing.

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?

Zero parameters, so there is no schema semantics to compensate for; baseline applies. The description usefully tells the agent that no filtering or model selection argument is needed because all three models are returned together.

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?

States a specific resource and scope: a public Aider benchmark of 27 coding exercises with hidden tests (Nov 2026) covering Opus, Sonnet and Haiku 5.5, with solved counts, cost per solved exercise, failures, method and raw data. The closing clause 'One benchmark, not your tasks' cleanly separates it from anything task- or account-specific.

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?

Opens with an explicit trigger ('Use when asked whether a cheaper Claude model is good enough, or for evidence on cost vs quality') and closes with an explicit exclusion ('not your tasks'). It does not name a specific sibling tool to use instead, so it stops short of full alternative 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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