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JinyangWang27

people-context

import_content

Extract header-only contact candidates from supported sources and stage them atomically, preventing duplicate imports and allowing forced re-imports for repeat processing.

Instructions

Extract and atomically stage header-only candidates from a supported source without bodies.

Accepted source_type values are email, mbox, vcard, ics, linkedin, outlook, and whatsapp. self_sender is an optional chat-export label for the user, such as a display name or a bare phone number, used to omit the user's own messages.

A path import records a receipt for the file it read, so importing that exact file again reports the existing batch instead of staging a second copy. forced says the repeat is intentional: it stages the same content as a distinct processing session and never weakens the duplicate rule for later calls. It is also the only way past source_previously_redacted after a hard forget — and for mbox, which is read from a path and cannot be resubmitted as inline content, the only way at all.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo
forcedNo
contentNo
self_senderNo
source_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.1

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the minimal annotations, the description discloses substantial behavioral details: path imports record a receipt so re-imports report the existing batch, forced stages a distinct processing session without weakening the duplicate rule, and only forced can bypass source_previously_redacted after a hard forget. It also explains the mbox-specific limitation and notes atomic staging, giving the agent a realistic model of side effects.

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?

The description is dense but every sentence contributes useful information: purpose first, then source types and self_sender, then the critical path/forced/dedup semantics. It is appropriately sized for the complexity of the tool and avoids filler or restating the schema.

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?

The description covers source-type constraints, the dedup receipt behavior, forced re-imports, hard-forget edge cases, and mbox restrictions. It does not explicitly state whether path and content are mutually exclusive or clarify the content parameter directly, but the output schema and rich behavioral notes make the tool actionable for an agent.

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?

With 0% schema description coverage, the description carries the full parameter burden. It explicitly lists valid source_type values, explains self_sender, and clarifies path and forced behavior. The content parameter is only indirectly referenced via 'inline content' in the mbox note, so it lacks a direct definition, preventing a perfect score.

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 description opens with a specific verb phrase, 'Extract and atomically stage header-only candidates from a supported source without bodies,' clearly identifying both the action and the resource scope. It immediately distinguishes this tool from general body-import or full-content staging operations, and the source_type list makes the domain concrete.

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 gives clear contextual guidance: accepted source_type values are enumerated, self_sender is explained for chat exports, and the path/forced semantics detail when a repeat import is intentional. It does not explicitly name sibling alternatives like stage_candidates or commit_import, so it stops short of a full when-to-use-versus-X explanation, but the intended usage context is clear.

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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