Skip to main content
Glama

context_resolve

Retrieve the full working context from your AI Team OS, including active project, team, members, and loop status, to enable auto-filling of parameters.

Instructions

Get the current active OS context — active project, active team, member list, loop status.

This is the infrastructure for all simplified operations. A single call returns the complete context of the current working environment, allowing Leader or other tools to auto-fill parameters like project_id, team_id, etc.

Returns: Context dict containing project / team / agents / loop

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the return value (context dict) but does not discuss side effects, failure modes, or authentication requirements. For a read-only retrieval tool, this is minimally adequate.

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 two succinct sentences plus a one-line return specification. Every sentence adds information without redundancy. The structure places the core purpose first, followed by usage rationale.

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 simplicity (no parameters, low complexity) and the presence of an implied output schema, the description covers the essential aspects: what it does and what it returns. It could mention that this is the canonical context retrieval tool, but it is sufficient.

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

Parameters5/5

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

The tool has zero parameters, which sets a baseline of 4. The description adds value beyond the empty schema by explaining the output context and its utility for auto-filling parameters in other tools. This justifies a score of 5.

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 retrieves the current active OS context, listing specific components (project, team, member list, loop status). This verb+resource combination is distinct from the many sibling tools that perform specific actions.

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?

The description explains that this tool provides the 'infrastructure for all simplified operations' and enables auto-filling of parameters like project_id and team_id. It implies usage before other operations needing context. While it doesn't explicitly state when not to use, the context is clear.

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/CronusL-1141/AI-company'

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