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Read ai context

read_ai_context
Read-only

The account's AI context: what the blog is about, its pricing tiers, value props, testimonials, design direction and tone presets. Every AI-personalised lead magnet is generated from exactly this plus the visitor's own answers. It belongs to the whole account and is shared by every website on it, so a connection limited to one website can read it but not change it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoA project id from list_projects. Runs this call in that project instead of the connection default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover readOnly/destructive, so the description earns credit for adding scope semantics: the record is account-wide and shared across every website, and a website-scoped connection may read but not write it. That is genuinely actionable auth/scope context not present in the annotations, though return shape and edge cases (e.g. what an unset context looks like) are not covered.

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?

Three sentences, front-loaded with the content inventory, then the generation dependency, then the scope caveat. No filler, though the closing sentence is longer than it needs to be for the constraint it conveys.

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?

With no output schema, the description usefully enumerates the fields the context contains, effectively proxying a return-value summary, and no parameters are required. For a zero-required-param read tool this is nearly sufficient; only failure/empty-state behavior is unaddressed.

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 100%, and the single 'project' parameter is already documented as a project id from list_projects with default-override behavior. The description adds nothing about it, so the baseline 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 description enumerates exactly what the AI context holds (blog topic, pricing tiers, value props, testimonials, design direction, tone presets), which is far more specific than the name alone. The verb is only implicit until the closing sentence ('can read it but not change it'), but the resource is unmistakable and is differentiated from update_ai_context.

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?

It explains that AI-personalised lead magnets are generated from this, which implies why an agent would read it, and it clarifies the read-only nature of a single-website connection. However, there is no explicit 'use this when' statement and no named alternative for the mutation case beyond the oblique 'not change it'.

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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