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fill_field

Fill text fields in job applications with a value, recording the field's role and reason for reuse, verifying typed input against read-back to catch mismatches.

Instructions

Type a value into a text-like field.

ref comes from browser_state. role names what the field is for (for example "phone", "first_name") and reason explains why this value is correct; both are recorded so the same field is faster to fill next time. There is no version of this tool without them, on purpose.

The value is typed character by character and then read back. If the read-back disagrees with what was typed, the tool reports a mismatch instead of pretending to have succeeded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refYes
roleYes
valueYes
reasonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it does well: it discloses that the value is typed character by character, read back, and that mismatches are reported instead of silently succeeding. It also reveals that role and reason are recorded for future speed, providing valuable behavioral insight beyond the simple action.

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?

The description is well-structured and front-loaded: a one-sentence purpose is followed by parameter semantics and then behavioral detail. The 'no version without them' sentence is slightly emphatic but useful in preventing agents from omitting required fields. No wasted words.

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?

Given the output schema exists, return values are covered elsewhere. The description covers what the tool does, when to use it, parameter meanings, recording behavior, and verification behavior. The only minor gap is not defining precisely what counts as a 'text-like field', but this is not essential for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it explains three of the four parameters: ref comes from browser_state, role names the field's purpose, and reason justifies the value. The value parameter is self-evident from the tool name and action. This is solid compensation for the schema gap, though value formatting details are not addressed.

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 opening sentence 'Type a value into a text-like field' states a specific verb and resource, making the tool's function immediately clear. It distinguishes itself from siblings like select_option, set_checkbox, and upload_file by targeting text-like fields specifically, and the role examples ('phone', 'first_name') reinforce the intended use.

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?

The description clearly states this tool is for text-like fields, which gives an agent clear context for when to use it. It does not explicitly name alternatives or state when not to use it, but the text-like field qualifier is sufficient guidance for most selection scenarios.

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