Write a text file into a persistent Linux machine. Use this instead of shelling out with cat/echo — no quoting to get wrong.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Machine id | |
| mode | No | Unix mode in decimal, e.g. 493 for 0755. Omit unless the file must be executable. | |
| path | Yes | Absolute path inside the machine, e.g. /root/app/main.py | |
| content | Yes | File contents as text |
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?
Annotations already declare this as destructive (destructiveHint: true) and idempotent (idempotentHint: true), so the safety profile is known. The description adds that it writes to a persistent Linux machine and only text files, but does not mention overwriting behavior or directory creation. Still, it does not contradict annotations.
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 two sentences, front-loading the core purpose and then giving a practical usage tip. Every word is useful with no fluff.
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 simple write tool with comprehensive schema and safety annotations, the description is sufficiently complete. It could mention that it overwrites existing files, but the destructive hint covers that. The 'persistent' context is valuable, and the sibling tools are well differentiated.
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%, with clear parameter descriptions for id, path, content, and mode (including decimal example). The description itself adds no extra parameter meaning, but the schema does the heavy lifting, so baseline 3 is appropriate.
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 tool writes a text file to a persistent Linux machine, with a specific verb and resource. It distinguishes itself from sibling tools like scalix_computer_read_file and scalix_computer_exec by explicitly recommending it over shelling out with cat/echo.
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 provides clear usage context: use this to write text files without quoting issues, instead of shelling out. It names the alternative (cat/echo) but doesn't explicitly state exclusions such as binary files or when to use read_file, leaving a small gap.
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.
Tools are grouped by service prefix and generally target distinct resources/actions. A few execution surfaces could be confused (sandbox_run vs computer_exec vs fn_invoke; build_create vs fn_deploy vs run_deploy), and storage_list is overloaded for both buckets and objects, but descriptions clarify the boundaries well.
The scalix_ prefix plus snake_case is used throughout, and most tools follow <service>_<verb>_<noun>. Minor deviations like scalix_search, scalix_status, and scalix_usage omit a service-domain qualifier, but the overall pattern is predictable and easy to navigate.
53 tools is a very large surface. While the server covers a broad multi-service cloud platform, the count falls well beyond the 25+ threshold and will likely feel overwhelming; many service areas could reasonably be split into separate servers or trimmed.
Several service lifecycles have obvious gaps: the KV store has get/list/set but no delete, storage has upload/download/list but no delete for objects or buckets, functions have deploy/list/invoke but no delete/update, and cron has create but no list/delete. These missing operations create dead ends for agents managing common resources.
