Skip to main content
Glama

smart_fill

Fill form fields by matching labels using fuzzy logic (exact, prefix, substring, token). Optionally click a submit button. Returns results and suggests available labels on miss.

Instructions

⭐ Fill form fields by label text (fuzzy match). LLM-friendly alt to fill_form which requires DOM refs.

Args:
    fields: {"Label": "value", ...} — keys match form field labels
        (case-insensitive, fuzzy: exact > prefix > substring > token).
        Labels resolved from <label>, aria-label, placeholder, name.
    submit_label: optional button text to click after filling
        (e.g., "Create", "Sign in"). Fuzzy-matched on action button text.

Behavior:
    - Each field: locates input → focus → clear → type value
    - Returns per-field result + list of available labels if missing
    - On miss: error includes candidates so the LLM can retry with
      the correct label name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYes
submit_labelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations, so description fully covers behavior: fuzzy match algorithm (exact > prefix > substring > token), per-field steps, error returns with candidates, available labels on miss.

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?

Efficiently structured with Args and Behavior sections. Every sentence adds value, no 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?

Complete for a complex tool: explains algorithm, error handling, return values (via output schema). No gaps given the task.

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 coverage is 0%, but description thoroughly explains fields parameter (keys match labels, case-insensitive, resolution sources) and submit_label. Adds meaning beyond schema.

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?

Clearly states it fills form fields by label text with fuzzy matching, explicitly distinguishes from fill_form which requires DOM refs. Verb-resource pair is specific.

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?

Describes it as an LLM-friendly alternative to fill_form, implying when to use. Lacks explicit when-not or exclusions, but context is clear.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/RobithYusuf/mcp-stealth-chrome'

If you have feedback or need assistance with the MCP directory API, please join our Discord server