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Glama

people

Retrieve everything known about an individual by name, including recordings, conversations, meetings, chats, and stored facts to build context for interactions.

Instructions

Everything known about one person: how often and when their voice was recorded and who else was there, the latest conversations with them (with a short summary), calendar meetings they were invited to, chats with them on the Mac, their direct chats with the user on Zalo (how much, the groups they write in, the latest days with what each was about), what the memory has gathered about them, and the newest facts that involve them. Their Zalo name also finds them.

When several people match, the answer lists their names: call again with one of them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe person's name as the user calls them, e.g. "Tuấn" or "anh Tuấn". A part of a name works when it matches whole words.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
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 behavioral burden. It does disclose the broad read scope and the multi-match disambiguation behavior, which is real context, but says nothing about permissions, result size, pagination, or performance characteristics of what is clearly a heavy aggregation call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded, but the core sentence is an overlong run-on enumerating data sources, several of which the output schema already covers. The trailing disambiguation note earns its place; much of the middle enumeration does not.

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?

For a single-parameter tool with 100% schema coverage and an output schema present, the description is largely complete; it even redundantly enumerates return contents. The main gap is the absence of any routing guidance relative to the sibling tools.

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?

With a single parameter and 100% schema description coverage, the schema already documents name matching, partial-name matching, and examples. The description adds only the marginal note that a Zalo name also resolves the person, which is a small increment over the schema's 'name as the user calls them'.

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?

The opening clause 'Everything known about one person' states a clear resource and scope, and the enumeration of aggregated domains (voice recordings, conversations, calendar, chats, Zalo, memory, facts) makes the tool's remit unambiguous. It does not, however, explicitly differentiate itself from the sibling tools search, summary, and timeline, so it stops 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?

The note 'When several people match, the answer lists their names: call again with one of them' is a genuine follow-up workflow instruction, which is useful. But there is no explicit guidance on when to prefer this tool over the siblings search/summary/timeline, so usage is only implied.

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

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