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llmledger

llmledger

by pouriaetab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Suggest For Project

suggest_for_project
Read-onlyIdempotent

Suggests a model and effort level for a prompt or described project, naming value, higher-rated, and lower-cost picks with cost per task.

Instructions

Suggest a model and effort level for a prompt or a described project: it guesses the kind of work and names the best value (the cheapest model the votes cannot tell apart from the top), the top rated and the cheapest rated, each with its cost per task. Use when the user asks "which model should I use for this" and the work itself is in hand. Not for named candidates (compare_models) or for browsing by limits (find_models).

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": {
      "prompt": "<prompt>"
    },
    "name": "suggest_for_project"
  }
}

Input Schema

NameRequiredDescriptionDefault
payNoapi: dollars; claude or chatgpt: compare within that planapi
answerNoTypical answer lengthmedium
promptYesThe project's starting prompt, or a description of the work

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so safety is covered; the description adds the non-obvious behavior that it infers the type of work from the prompt and that 'best value' is defined by vote-indistinguishability from the top model rather than raw benchmark rank. It does not say how stable/repeatable the suggestion is or how the pay/answer settings change the ranking, which keeps it below a 5.

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 purpose in the first clause, then the usage rule and exclusions; no filler sentences. The parenthetical defining best value is dense and slightly convoluted, which is the only cost to readability.

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?

Since an output schema exists, the description needn't enumerate return values, yet it still names the three recommendation slots and the cost-per-task figure, which is enough for an agent to know what to do with the result. The only gap is not explaining that the pay setting switches the comparison basis between dollars and plan tiers, which affects how results should be interpreted.

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% and both enums are documented inline, so the schema carries the parameter burden. The description mentions effort level and cost per task but does not elaborate on how 'pay' (api vs claude vs chatgpt) or 'answer' length shift the recommendation, adding little beyond the schema. Baseline 3 applies.

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 (suggest) and resource (model + effort level for a prompt/project), and explains what the recommendation contains: best value, top rated, cheapest rated, each with cost per task. It explicitly distinguishes itself from siblings compare_models and find_models by name, so an agent can pick it without opening any schema.

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?

Gives an explicit trigger ('when the user asks "which model should I use for this" and the work itself is in hand') plus two named exclusions with the conditions that select them (compare_models for named candidates, find_models for browsing by limits). This is the full when/when-not/alternative pattern.

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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