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mfbaig35r
by mfbaig35r

run_estimate

Estimate the cost of a table run before executing it. Counts exact requests, excludes already-filled cells, and shows savings from cached unit prefixes.

Instructions

Report what run_table would cost, without running it or spending anything.

Counts the exact requests the run would send. Cells that are already filled, locked, or reviewed are excluded, because a run would skip them. Reports the cost both with and without the cached unit prefix, so the saving from that design is visible.

Call this before any run over more than a handful of rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoPrice against this model instead.
tableYesTable number as the corpus defines it, e.g. '01' or '13'.
refillNoInclude cells that already carry a value.
columnsNoRestrict to these column names.
providerNo'openai' or 'anthropic'.
unit_idsNoRestrict to these rows.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the tool does not execute anything ('without running it or spending anything'), explains that filled/locked/reviewed cells are excluded (since a run would skip them), and notes it reports costs both with and without the cached unit prefix. This is transparent about scope and side effects (none), going beyond a simple 'estimate' label.

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?

The description is compact and front-loaded with the core purpose, then elaborates on mechanics and usage in three tightly-written sentences. Every sentence adds value: purpose, exclusions, cost comparison, and when to call. There is no filler or redundancy.

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?

The tool has an output schema (not shown but flagged as present) and complete parameter coverage, so the description does not need to describe return format. It covers what the tool does, what it excludes, how it reports cost, and when to use it. Nothing an agent needs to decide whether to call it or to understand its behavior is missing.

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?

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds behavioral context (e.g., exclusions related to refill and cell state) but does not directly explain any parameter beyond what the schema already provides. It helps an agent infer how refill interacts with exclusions, but that is indirect. Since the schema already documents each parameter thoroughly, the description does not need to add more.

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 opens with 'Report what run_table would cost, without running it or spending anything,' which names the specific verb (report), the resource (cost of run_table), and explicitly distinguishes it from actually running. It also explains the counting of exact requests and exclusions, so an agent can immediately tell this apart from the sibling run_table tool.

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?

The description gives an explicit trigger: 'Call this before any run over more than a handful of rows.' This is a direct usage guideline that tells the agent when to invoke it, and it implies the alternative (run_table) without needing to name it. It also clarifies the purpose as a pre-run check, which is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.