Skip to main content
Glama

replay_skill

Runs a saved skill step by step without a model, re-finds each target by role and name, and reports what happened and where it stopped.

Instructions

Execute a learned skill step by step without a model and report what happened.

Each step re-finds its target by role and name on the current screen; values the skill learned from a page are re-read from the current page. The first line says whether all steps ran or at which step replay stopped and why, followed by a short trail of what was seen after each step and a fresh observation. If replay stopped, continue the task from the current state with the other tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesSkill name exactly as printed by list_skills.
start_urlNoOptional URL to open before replaying, when the skill starts on a web page.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses that no model is needed, that targets are re-found by role/name on the current screen, and that learned values are re-read from the current page rather than cached. It also describes the output shape and failure behavior. It omits side-effect/permission context, but core runtime behavior is well covered.

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?

Three tight sentences, front-loaded with the purpose, then mechanism, then output/failure handling. Nothing is filler and each sentence carries distinct information.

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 values need not be explained, yet the description still summarizes the report format helpfully. Combined with mechanism and failure routing, it is nearly complete; only side-effect implications of executing a skill are left implicit.

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 both parameters are already documented with their formats. The description adds behavioral context about re-finding targets and re-reading values, which informs how replay uses the skill, but does not extend the meaning of either parameter beyond the schema. Baseline 3 is appropriate.

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 ('Execute a learned skill step by step without a model') and adds the distinguishing scope that it runs without a model and reports results. This clearly separates it from the atomic action siblings like click and type_text.

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?

Provides explicit post-failure routing: 'If replay stopped, continue the task from the current state with the other tools.' This tells the agent what to do next when this tool ends abnormally. It does not, however, say when to prefer replay over manually re-issuing atomic steps, so no full alternative-selection guidance.

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