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get_environment_status

Retrieve the full project environment status in one call, covering dependencies, build system, compile database, index, and LLM backend. Read-only aggregation reduces session-start round-trips.

Instructions

Return the complete project environment status in one call.

Read-only. Aggregates five domains into a single call so the LLM can see everything at session start without extra round-trips:

  • deps — dependency audit (run_full_check), each entry with an optional action (message + shell command). status="skipped" means a prerequisite is missing (e.g. libclang-so skipped because libclang-python is absent) — not a failure.

  • build_system — detected build system, None when unknown.

  • compile_db — whether compile_commands.json exists and its entry count. Reported as {"exists": false, ...} before init (no config to resolve the path from, and loading one would create empty config files).

  • index — the FULL get_active_build() result, unchanged (its action lives in index_message).

  • llm — LLM backend status with an optional action.

When the project is not initialized (index.status == "not_initialized"), only the config-independent dependency subset runs (checks that do not need a project config) — Ollama/model/db/build checks are skipped.

Args: project_root: Project root directory. Auto-detected from CWD if omitted.

Returns: dict: {init_status, deps, build_system, compile_db, index, llm}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_rootNoProject root. Auto-detected if omitted. Pass explicitly when the project is not the server cwd.
Behavior5/5

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

With no annotations provided, the description carries the full burden and excels: it explicitly states 'Read-only,' clarifies that 'status=skipped' is not a failure, explains compile_db behavior before init, and describes how uninitialized projects limit the check set. This goes well beyond the structured schema.

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 longer than average, but every sentence earns its place: it front-loads a one-sentence summary, then uses clear bullets to explain each returned domain, and ends with Args/Returns sections. The structure makes a complex aggregate tool easy to parse.

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?

Despite having no output schema, the description documents the return dict keys, field semantics, and important edge cases such as not_initialized behavior and skipped dependency conditions. This is more than sufficient for an agent to invoke the tool and interpret results correctly.

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?

The single parameter project_root has 100% schema description coverage, and the description's Args section largely repeats the schema text ('Auto-detected if omitted'). Since the schema does the heavy lifting, baseline 3 is appropriate; no additional semantic meaning is added.

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 opens with a specific verb and resource: 'Return the complete project environment status in one call.' It clearly distinguishes itself from sibling tools like check_dependencies, check_ollama, and get_active_build by presenting itself as an aggregate of five domains.

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 recommends using this tool at session start to avoid extra round-trips, and explains conditional behavior for uninitialized projects. It does not explicitly name alternative tools for specific domains, but the aggregate-vs-single-purpose distinction is clear.

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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