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dialectforge

FlowState by DialectForge

Official

log_change

Record component changes with field, old/new values, reason, and change type in a project, so shared AI memory can track what changed and why.

Instructions

Log a change to a component. Write: requires project="" (or project_id).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoYour model name, if you know it (optional; never guessed)
reasonNo
projectNoProject name or id. Required for every write.
new_valueNo
old_valueNo
field_nameYesWhat changed
change_typeNo
component_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, so "Write" is partly redundant, but the description adds a genuinely useful constraint beyond the schema: project is effectively required for every write even though the schema marks only field_name as required. It says nothing about side effects, reversibility, or return behavior.

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 short sentences with the purpose front-loaded and no wasted words. The telegraphic "Write: requires..." fragment is slightly clipped but efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter mutation tool with no output schema and 38% schema coverage, the description covers only the project prerequisite. An agent still lacks guidance on the old/new value pairing, change_type enum usage, and what logging a change actually does.

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 coverage is only 38%, so the description must compensate. It usefully pins down the project parameter's requirement, which the schema understates, but says nothing about the other six undocumented parameters (new_value, old_value, reason, change_type, model, component_id).

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 ("Log a change to a component") that an agent can distinguish from siblings like log_learning and log_work. It does not explicitly name those siblings or clarify the boundary, so it falls short of a 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?

It gives a prerequisite ("Write: requires project=...") but no when-to-use vs when-not guidance and no routing among the many log_* siblings. Usage is implied rather than explained.

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