Skip to main content
Glama

save_identity_tool

Store user facts like name, location, or job, and add or remove items from lists such as hobbies or skills, keeping personal context accurate and up to date.

Instructions

Save a personal fact about the user. op='set' for scalar facts (name, location, job). op='add_items' with items=[] for adding to a list (hobbies, skills). op='override' only when the user explicitly corrects a previous fact. op='remove_items' with items=[] to remove from a list.

Input Schema

NameRequiredDescriptionDefault
opYes
fieldYes
itemsNo
valueNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.1

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the semantics and caution level of each operation, including the restricted 'override', which implies mutation/overwrite behavior. However, it omits persistence guarantees, permissions, and what happens to existing values on overwrite.

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?

Front-loaded with the purpose, then one tightly scoped line per operation with zero filler. Every sentence earns its place by mapping an op value to its intended use.

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-format details are appropriately omitted, and the tricky op-selection logic that drives correct invocation is well covered. The remaining gap is the semantics of the 'field' and 'value' parameters, which are left to the caller's guess.

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 0% and no enums are defined in the schema, so the description must compensate. It effectively documents the four valid 'op' values and the role of 'items', but leaves 'field' (what fields are valid?) and 'value' entirely unexplained, so coverage of the 4 params is only partial.

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 and resource ('Save a personal fact about the user'), which clearly identifies its domain as identity/personal facts. It does not explicitly contrast itself with near siblings such as save_note_tool or remember_turn_tool, so an agent must infer the distinction from the word 'personal fact'.

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 conditional guidance per operation: 'set' for scalars, 'add_items' for lists, 'remove_items' to remove, and notably 'override' only when the user explicitly corrects a fact. That conditional restriction is a genuine when-to-use rule. It stops short of naming alternative tools or stating when NOT to use this tool at all.

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