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Cerebrium Docs

by ai.cerebrium

query_docs_filesystem_cerebrium

Read-onlyIdempotent

Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at / that contains ONLY the Cerebrium documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks.

This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its .mdx path (e.g. /quickstart.mdx, /api-reference/create-customer.mdx) to head or cat. To search the docs with exact keyword or regex matches, use rg. To understand the docs structure, use tree or ls.

Workflow: Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path.

Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run --help on any command for usage.

Each call is STATELESS: the working directory always resets to / and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with && or pass absolute paths (e.g., cd /api-reference && ls or ls /api-reference). Do NOT assume that cd in one call affects the next call.

Examples:

  • tree / -L 2 — see the top-level directory layout

  • rg -il "rate limit" / — find all files mentioning "rate limit"

  • rg -C 3 "apiKey" /api-reference/ — show matches with 3 lines of context around each hit

  • head -80 /quickstart.mdx — read the top 80 lines of a specific page

  • head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx — read multiple pages in one call

  • cat /api-reference/create-customer.mdx — read a full page when you need everything

  • cat /openapi/spec.json | jq '.paths | keys' — list OpenAPI endpoints

Output is truncated to 30KB per call. Prefer targeted rg -C or head -N over broad cat on large files. To read only the relevant sections of a large file, use rg -C 3 "pattern" /path/file.mdx. Batch multiple file reads into a single head or cat call whenever possible.

When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the .mdx extension. For example, /quickstart.mdx becomes /quickstart and /api-reference/overview.mdx becomes /api-reference/overview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYesA shell command to run against the virtualized documentation filesystem (e.g., `rg -il "keyword" /`, `tree / -L 2`, `head -80 /path/file.mdx`).

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations, the description discloses critical behavioral traits: the filesystem is a sandbox not a real machine, no writes/network/process control are allowed, each call is stateless with working directory reset, and output is truncated to 30KB per call. It also explains the supported commands and path-to-URL conversion. This substantially exceeds what the annotations alone provide.

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 long but every section earns its place: safety clarification, workflow, command list, statelessness warning, concrete examples, truncation guidance, and URL conversion. It is well-structured with headers and examples, and the most critical scoping information is front-loaded in the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description is remarkably complete. It covers what the filesystem contains, what commands work, how to explore and read pages, statelessness implications, output limits, and how to reference pages in responses. An agent has everything needed to invoke the tool correctly and interpret results.

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?

Although the schema covers the single `command` parameter at 100%, the description adds extensive practical meaning: supported commands, examples, chaining with `&&`, absolute paths, truncation behavior, and reading multiple files in one call. This goes far beyond the schema's one-line parameter description.

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 states a specific verb and resource: "Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/`" containing only Cerebrium documentation and OpenAPI specs. It clearly distinguishes itself from the sibling search_cerebrium by explaining the tool is for exact keyword/regex matching, structural exploration, and reading specific pages by path, rather than broad conceptual search.

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?

The description gives explicit workflow guidance: "Start with the search tool for broad or conceptual queries" and "Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path." It also specifies how to chain commands in one call and warns about statelessness, leaving little ambiguity about when and how to use it.

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

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TDQS

A4.4/5.0
Disambiguation3/5

search_cerebrium and query_docs_filesystem_cerebrium both serve document retrieval and could be confused for similar purposes, though their descriptions differentiate semantic search from exact filesystem-style queries. submit_feedback is clearly distinct, but the boundary between the two retrieval tools still requires careful reading.

Naming Consistency4/5

All tools use lowercase snake_case and a verb-first style, but naming is not perfectly uniform: query_docs_filesystem_cerebrium and search_cerebrium include the server name while submit_feedback does not. The pattern is mostly predictable and readable.

Tool Count5/5

Three tools is well-scoped for a documentation server: semantic search, filesystem-style document access, and feedback submission. Each tool serves a clear purpose and the count feels appropriate rather than excessive or thin.

Completeness5/5

The tool set covers the core documentation workflow: searching for information, reading full pages, exploring structure, and submitting feedback on outdated or incorrect content. There are no significant missing operations for the stated domain.

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