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get-llm-prompt-task-suggestions

Read-only

List open publisher-order suggestions for the active project from the suggestion engine, including queued ones not yet in the inbox. Each covers a topic cluster: returns the prompts it covers (highest impact first), the topic, task id/sequence, content recommendation (product + publication type), and publisher shortlist. Link-fix and site-fix suggestions come from get-suggestion-inbox. If this tool returns { ok: false, error }, it failed. Explain the error to the user concisely in plain language and ask them to help resolve it (for example, select a valid project) — do not retry the same call blindly.

Input Schema

NameRequiredDescriptionDefault
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
advertiserProjectIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint true and destructiveHint false, so the safety profile is covered. The description adds real behavioral value: it discloses that queued items not yet in the inbox are included, details the return contents and ordering, and explains the failure shape and recovery step.

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?

Front-loads the purpose and return contents, then the sibling routing, then error handling. Slightly long but every sentence carries information; the error-handling sentence is a touch verbose.

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 carries the return-shape burden and does so well by enumerating prompts, topic, task id/sequence, content recommendation, and publisher shortlist. Only minor gap is the unexplained advertiserProjectId parameter.

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 67%; the two analytics params (context, llm_model) are documented in-schema. The advertiserProjectId has only a pattern and no description, and the description only weakly implies it via 'the active project.' Baseline 3 given the partial coverage.

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?

States a specific verb and resource: 'List open publisher-order suggestions for the active project from the suggestion engine,' with the added detail of including queued items. Clear what it does, but it does not distinguish itself from the sibling get-llm-prompt-suggestions, which shares nearly the same name.

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?

Routes link-fix and site-fix suggestions to get-suggestion-inbox and adds recovery guidance for the error case. However, it never addresses the closest-named sibling get-llm-prompt-suggestions, leaving the primary ambiguity unresolved.

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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