Skip to main content
Glama

compile_brief

Compiles a brief from source documents by resolving an LLM via a capability ladder, enforces wikilink emission, and writes to a sink with automatic superseding.

Instructions

Compile a brief from caller-supplied source documents and write it to the briefs sink. Resolves the LLM via the D-10 capability-first ladder (MCP Sampling → local Ollama → caller prepared_text → structured error). Enforces D-11 wikilink emission per source (appends a ## Sources footer when the LLM omits them) and writes through DeliveryAdapter. On target collision, auto-supersedes the prior brief via the Phase 2 supersede chain (D-12).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vaultYesVault name (registered in [vaults] config block)
targetYesStable cross-version handle for the brief (e.g. 'atlas-q3')
source_doc_idsYesDocIds the brief is compiled from; deduped, capped at 50 (D-03)
purposeYesFree-form purpose; bounded so list_briefs stays scannable
max_tokensNoHint for the LLM ladder; default 2000
prepared_textNoD-10 tier 3 fallback when no LLM is reachable — verbatim body to stitch in
sinkNoOverride the default `_memory/_briefs` sink
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals several key behaviors: LLM resolution via a defined ladder, automatic wikilink enforcement with a footer, writing through DeliveryAdapter, and auto-superseding on target collisions. This goes beyond a simple 'compile brief' statement and helps the agent anticipate internal processing.

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?

The description is concise at three sentences. The first sentence clearly states the core action, and subsequent sentences add critical behavioral details without redundancy. Every sentence is informative and earns its place.

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?

Given the tool's complexity (7 parameters, no output schema), the description covers key behaviors like LLM resolution and supersede but fails to mention what the agent should expect as an output or return value. It also doesn't explain error conditions or when to use optional parameters like prepared_text or sink. With 100% parameter schema coverage, some gaps are mitigated, but overall completeness is adequate but not thorough.

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%, so the baseline is 3. The description does not add extra parameter-specific meaning beyond what the input schema already provides; it mentions source documents and LLM resolution but doesn't elaborate on individual parameters like vault, target, or purpose.

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 the tool's purpose: compiling a brief from caller-supplied source documents and writing it to a sink. It uses specific verbs ('compile', 'writes') and resource ('brief'), and includes unique behaviors like LLM resolution and wikilink enforcement that distinguish it from siblings like write_note or assemble_dossier.

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?

The description does not explicitly state when to use this tool over alternatives like assemble_dossier or write_note. It describes internal mechanisms (LLM ladder, supersede) but lacks guidance on use cases or conditions. No exclusions or prerequisites are mentioned.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/owrede/vault-memory'

If you have feedback or need assistance with the MCP directory API, please join our Discord server