Create a monthly LLM budget allocation for a team or department
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Human-readable budget name | |
| api_key | No | API key for the request | |
| providers | No | LLM providers to track: openai, anthropic, google, mistral, cohere | |
| department | No | Department or team name | |
| monthly_limit_usd | Yes | Monthly spend cap in USD |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It merely states the action without mentioning side effects, idempotency, required permissions beyond an api_key, or what happens on duplicate names. This is a sparse disclosure for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that is front-loaded with the action and resource. No unnecessary words or repetition of schema properties.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with five parameters, no annotations, and no output schema, the description is too minimal. It does not explain what the API returns, any prerequisites beyond the api_key, or the distinction between team and department, leaving the agent with incomplete operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all five parameters. The description adds no parameter-specific detail beyond the phrase 'monthly LLM budget allocation,' which loosely echoes monthly_limit_usd. It does not compensate for or add nuance to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (create) and the resource (monthly LLM budget allocation) with a target audience (team or department). It distinguishes itself from sibling tools like get_budget and department_report, which are about retrieval or reporting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when creating a budget allocation), but it does not explicitly discuss alternatives or exclusions. No guidance on when to choose this over department_report or weekly_report, though the verb 'create' gives some contextual distinction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: budgets, departments, alerts, providers, and reports. No overlapping purposes.
All tool names follow consistent snake_case verb_noun pattern: create_budget, department_report, get_budget, etc.
Six tools cover the core functionality of budget planning without being too many or too few.
Covers creation, retrieval, alerts, providers, and reports. Missing update/delete for budgets, but core workflows are supported.