Skip to main content
Glama

add_team_member

Add one human teammate to the current company by email. Creates a Command Center approval card (sensitive, every call). On approve: invite email + roster row. Required: email, role (job title, or team / manager). Optional: name. No bulk. No permission designer — team is the default access; pass role=manager for the manager preset. Use when the operator (or CoS) needs to add a person who is not yet on get_team_members.

Routing: Add / invite a human teammate by email → this tool (approval card). For AI agents use interview_for_hire. To see who is already on the company use get_team_members.

[sensitive-tier — EVERY call needs a manager's approval (per-send human rail): each request queues its own approval card and sends exactly once on approve. There is no standing grant for this tool.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional full name (e.g. "Yuichi Ichi"). If omitted, derived from the email local-part.
roleYesJob title (stored on their profile) or access preset: team / manager. Other strings are titles on the team preset.
emailYesInvitee email. Must be the exact address — never guess.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.9/5.0
Behavior5/5

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

Despite no annotations, the description fully discloses the sensitive nature: 'Creates a Command Center approval card (sensitive, every call)' and explains the approval process, including that it requires manager approval, queues individual approval cards, and sends exactly once on approve. It also notes there is no standing grant, which is critical behavioral context.

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 with front-loaded purpose and clear sections. While it is a bit verbose (three paragraphs), every sentence contributes meaningful information. No redundancy, but could be slightly tightened without losing clarity.

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

Completeness5/5

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

Given no output schema, the description explains what happens on approval (invite email + roster row), covers all input parameters, the approval process, routing, and alternatives. It is comprehensive for a tool that adds a team member, leaving no critical gaps in understanding.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds significant value: it clarifies that 'role' can be a job title or access preset ('team' or 'manager'), that 'name' is optional and derived from email if omitted, and that 'email' must be exact. It also reinforces the required parameters and their semantics beyond the schema.

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 clearly states 'Add one human teammate to the current company by email' and differentiates from siblings by referencing interview_for_hire for AI agents and get_team_members for viewing existing members. The verb+resource combination is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: 'Use when the operator (or CoS) needs to add a person who is not yet on get_team_members.' Also states when not to use ('No bulk. No permission designer') and directs to alternative tools for AI agents and viewing existing members. The routing section clearly maps scenarios to tools.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

Resources