Skip to main content
Glama

Queue deep context

prepare_deep_context

Queue a durable context pack from a task description to supply AI coding agents with relevant codebase context; uses late-interaction reranking when configured.

Instructions

Queue a durable, quality-first context pack (late-interaction reranking when configured).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notifyNo
repo_pathNo
task_descriptionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.2

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already reveal readOnlyHint=false, idempotentHint=false and openWorldHint=true, so safety and repeatability are covered. The description adds real context the annotations don't: the work is queued asynchronously and is 'durable' and 'quality-first' (implying a slower, higher-cost path). However it omits what a caller gets back (a job id?) and how to retrieve results, which matters for a non-idempotent queueing operation.

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?

A single front-loaded sentence with no filler or redundancy. It is efficient, though the efficiency is partly achieved by leaving required information out.

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

Completeness2/5

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

For an async, non-idempotent job-queueing tool with no output schema and 0% parameter documentation, the description is far too thin: it says nothing about the job lifecycle, result retrieval (a get_background_job sibling exists), or how repeated calls behave.

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

Parameters2/5

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

Schema description coverage is 0% across three parameters, so the description carries the full burden and gives no meaning for task_description, notify, or repo_path. Parameter names are mildly self-explanatory, which is the only thing preventing a 1.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The verb 'Queue' and the object 'context pack' are stated, but 'context pack' is undefined jargon and the description never distinguishes itself from the sibling prepare_task_context. An agent cannot tell which of the two preparation tools to pick from the text alone.

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 when-to-use guidance, no statement of prerequisites, and no exclusion of alternatives despite prepare_task_context and prepare_deep_context being adjacent siblings. The parenthetical about reranking reads as an implementation note, not selection guidance.

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