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Get Agent Context

get_agent_context
Read-onlyIdempotent

Return auth mode, plan, usage, preferences, service config, and operating instructions.

Input Schema

NameRequiredDescriptionDefault
uidNoOptional Firebase uid. Must match the URL uid unless local fallback is enabled.
bearer_tokenNoOptional Firebase ID token. Hosted clients normally authenticate with an API key in the Authorization header or ?uid= in the URL instead.
conversation_idNoCreator-list key for durable state. Reuse a returned list_id for follow-ups; when omitted, MCP uses the active generated list for this tool chain.
preferences_modeNoHow to apply preferences for this call: 'stored' uses users/{uid}.agentPreferences, 'override' uses agent_preferences only, and 'none' disables stored filtering for this run.
agent_preferencesNoOptional per-call filtering preferences. Use with preferences_mode='override'. For backwards compatibility, passing this without preferences_mode implies override.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and a closed world, so the safety profile is covered. The description's enumeration of returned content is modest added value, but it says nothing about auth requirements despite the schema hinting at non-trivial auth (URL uid matching, local fallback, bearer tokens).

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?

A single front-loaded sentence with zero filler; the payload contents are listed in the most scannable position possible. Nothing could be removed without losing information.

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?

With no output schema, the description's field list is the only spec of what comes back, which is genuinely useful for a five-parameter context-fetch tool. But it stops at names, giving no sense of return structure, size, or when the call is expected in an agent's lifecycle, leaving a gap for a bootstrap-style tool.

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?

All five parameters carry 100% schema description coverage, including the preferences_mode enum and the interaction between agent_preferences and preferences_mode. The description adds nothing beyond the schema, so the baseline of 3 applies.

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 verb 'Return' plus a concrete enumeration of the payload (auth mode, plan, usage, preferences, service config, operating instructions) makes the tool's output scope unambiguous. However, it never distinguishes itself from siblings like get_preferences or get_account, which overlap conceptually with the 'preferences' and account/plan content named here.

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

Usage Guidelines2/5

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

There is no statement of when to call this tool, no prerequisites, and no named alternative. Notably, get_preferences is a direct sibling that also surfaces preferences, yet the description gives no rule for choosing between them, leaving the agent to infer.

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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