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Glama

Estimate Time

estimate_time
Read-onlyIdempotent

Estimate how long one call and a full job take for a model, showing usual and slow-day times from your logged runs and given tokens, calls, and concurrency.

Instructions

How long one call and the whole job take, as a usual time and a slow day, learned from your own timed runs of that model called that way (log_run). Says so when there are no runs yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wayNoHow the model is calledapi
callsNoHow many calls the job makes
modelYesA model name, as the catalogue or Ollama names it
thinkingNoThinking turned on
concurrencyNoCalls running at once
input_tokensYesPrompt tokens per call
output_tokensYesAnswer tokens per call, including hidden thinking

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), so the bar is lower. The description nonetheless adds real behavioral context: the estimate is learned from the caller's own timed runs via log_run, and it explicitly discloses the empty-case behavior ('Says so when there are no runs yet'). That empty-state disclosure is meaningful information not present in the annotations.

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?

Two sentences, front-loaded with the core output shape and finishing with the dependency and the empty-case note. The clause 'of that model called that way' is slightly convoluted, but no sentence is wasted.

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?

An output schema exists, so return-value documentation is not required. With 100% schema coverage and the description covering the data source, the output shape, and the no-data case, an agent has what it needs; only the explicit when-to-use routing against siblings 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?

Schema description coverage is 100%, so the schema already documents model, way, tokens, calls, concurrency, and thinking. The description adds no parameter-level detail beyond what the schema provides, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb+resource: it estimates how long one call and the whole job take, and it names the distinguishing output form (a usual time and a slow day). This separates it from estimate_cost and forecast_job by naming 'time' versus cost. It stops short of explicitly contrasting itself with forecast_job, so it is not a full 5.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: the description names log_run as the data source, which tells an agent the estimate depends on prior timed runs. It does not say when to prefer this over estimate_cost, forecast_job, or local_or_cloud, so the agent must infer the routing.

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