Skip to main content
Glama
Sign In
knowl

knowl

by dat999zx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

knowl_context

Read-only

Fill a token budget with diversified project context for subagent briefings or fixed-size prompts. Broad across categories, not ranked for one subject.

Instructions

Fill an explicit token budget with diversified project context. Use only when you have a budget to fill -- briefing a subagent, or packing a fixed-size prompt. For a specific question use knowl_query instead: this spreads across categories to fill the budget rather than ranking for one subject, so it is deliberately broader and less precise.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tokenBudget": "<tokenBudget>"
    },
    "name": "knowl_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
taskNoWhat the context is for, in a phrase. Steers selection when query is broad or absent.
queryNoWords naming the subject to centre the pack on. Omit to pack the project's standing context.
explainNoInclude excluded-item diagnostics.
tokenBudgetYesToken ceiling for the pack, 100-4000.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv5.23.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, covering safety. The description adds meaningful behavioral context beyond that: it 'spreads across categories to fill the budget rather than ranking for one subject' and is 'deliberately broader and less precise.' This explains the tool's selection strategy, which is valuable and not inferable from the schema. It doesn't describe the return format, but that's a minor gap given the read-only annotation.

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?

Two sentences with zero redundancy. The core purpose is front-loaded, then usage constraints, then the alternative with its rationale. Every clause earns its place, and the structure is ideal for an agent scanning descriptions quickly.

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?

Given the tool's moderate complexity, the description is complete enough. It explains what the tool does, when to use it, and how it differs from the key sibling. There is no output schema, but the description implies the output is a context pack sized to the budget. Minor missing details like the exact composition of the pack or how 'diversified' is enforced are not critical for invocation.

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 each parameter (tokenBudget, task, query, explain) is already documented in the schema. The description adds some context about the query parameter ('steers selection') and the overall behavior, but it doesn't provide parameter-specific syntax or additional constraints beyond what the schema states. The baseline 3 applies; the description adds marginal but not essential value.

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: filling an explicit token budget with diversified project context. It names a specific verb ('fill') and resource ('token budget with project context'), and explicitly differentiates from the sibling knowl_query by contrasting its behavior ('spreads across categories' vs 'ranking for one subject'). This makes it unmistakable what this tool does and how it differs.

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

Usage Guidelines5/5

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

Usage is precisely scoped: 'Use only when you have a budget to fill -- briefing a subagent, or packing a fixed-size prompt.' It also gives an explicit alternative: 'For a specific question use knowl_query instead.' This is a textbook example of when/when-not guidance, leaving no ambiguity for an agent.

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

Deploy Server

Other Tools