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thejaredchapman

EvalForge Lite

by thejaredchapman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

evaluate_prompt

Get pre-run feedback on a prompt's clarity and specificity before running a model comparison, using your credentials or server-side keys.

Instructions

Get pre-run feedback on a prompt's clarity/specificity before running a comparison.

An explicit, separately-triggered LLM call (uses your credentials) — not run
automatically as part of run_comparison. Rate-limited independently from
run_comparison's 3-per-8h budget. Pass `creds` as {"openrouter"?: str,
"bedrock"?: {...}, "vertex"?: {...}, "foundry"?: {...}} to use Amazon Bedrock,
Google Vertex AI, or Microsoft Foundry; a bare `api_key` is treated as an
OpenRouter key. `judge_backend` picks which backend runs the evaluation.
If the operator has set server-side keys for a backend (see https://github.com/thejaredchapman/evalforge-lite/blob/main/docs/hosting-and-server-keys.md), those are used for it automatically and creds for it are not needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
credsNo
promptYes
api_keyNo
judge_backendNoopenrouter

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so well: it discloses that this is an explicit LLM call consuming user credentials, that it is rate-limited independently (3-per-8h context), and that server-side operator keys may be substituted automatically. These are meaningful behavioral traits not derivable elsewhere.

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-loads the purpose in the first sentence, then layers behavioral detail. Dense but every sentence earns its place; the credential-shape sentence is heavy but necessary given 0% schema coverage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-param, no-annotation tool with no output schema, the definition covers purpose, trigger semantics, rate limits, and credential handling. The only mild gap is that it does not describe the shape of the returned feedback, which is minor.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: it documents the creds dict shape, that a bare api_key is treated as OpenRouter, and that judge_backend selects the evaluation backend. Three of four params gain real meaning beyond the bare schema titles.

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 verb+resource+scope: 'Get pre-run feedback on a prompt's clarity/specificity before running a comparison.' It explicitly contrasts itself with run_comparison ('not run automatically as part of run_comparison'), letting an agent distinguish it from the sibling without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Explicitly states when it triggers (separately, before a comparison), that it is not automatic as part of run_comparison, and that it has its own rate-limit budget distinct from run_comparison's 3-per-8h. The alternative and its selecting condition are named.

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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