Skip to main content
Glama

error_budget_update

Update the error budget for a team after a task completes, appending the result to a sliding window of recent tasks and recalculating autonomy level.

Instructions

Update error budget after a task completes.

Automatically called after task completion. Appends to the sliding window (last 20 tasks) and recomputes the autonomy level.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYesTeam ID
task_successYesTrue if task succeeded, False if failed/blocked

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description bears full burden for behavioral disclosure. It reveals that the tool 'appends to the sliding window (last 20 tasks) and recomputes the autonomy level', which gives concrete behavioral detail. It does not mention side effects or permissions needed, but the core behavior is clear.

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: three short sentences that front-load the purpose, then add context. Every sentence adds value without repetition or unnecessary detail. It is well-structured for quick understanding.

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 low complexity (2 parameters, no nested objects), 100% schema coverage, and the presence of an output schema (covering return values), the description is largely complete. It explains the sliding window and autonomy level recomputation. Missing details about return values are covered by the output schema, and the automatic invocation context is clear.

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?

Since the input schema already provides 100% coverage with descriptions for both parameters (team_id, task_success), the description does not add further meaning beyond stating that it is called after task completion, implicitly linking task_success to the result. No additional parameter semantics are provided.

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 verb 'Update', the resource 'error budget', and the context 'after a task completes'. It distinguishes itself from the sibling tool 'error_budget_status' which likely only queries the budget, making the purpose specific and unambiguous.

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 explicitly says 'Automatically called after task completion', which guides the agent that this is not a manually invoked tool under normal circumstances. However, it does not provide explicit when-not-to-use scenarios or mention alternative tools beyond the automatic invocation context.

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