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check_ollama

Verify the LLM backend is running and the configured embedding and chat model is installed before using semantic search or symbol explanation.

Instructions

Check whether the LLM backend is running and the configured embedding/chat model is installed.

Read-only: yes. No side effects. Call before smart_search, semantic_search, or explain_symbol (when on-demand fallback is expected — pre-computed analysis returns instantly without the LLM backend).

Args: project_root: Project root. Auto-detected if omitted. Used to locate the project's LLM configuration. project: Project name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both.

Returns: dict: {ollama_enabled (bool), status (str — "ok"|"disabled"| "not_configured"|"model_missing"|"embedding_unavailable"|"error"), ollama_running (bool), ollama_url (str), configured_model (str), num_ctx (int), installed_models (list[str]), chat_api (dict — {configured (bool), model (str)}: whether an external chat API replaces Ollama, and the model it names), configured_embed_model (str), embedding_installed (bool), message (str, on error/disabled), model_details (list[dict], when Ollama running), suggest_cloud (bool), vec_available (bool), vec_error (str, optional), debug_log (str, optional — only when debug logging is enabled)}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoProject name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both.
project_rootNoProject root. Auto-detected if omitted. Used to locate LLM config. Falls back to auto-detection when omitted. This field also accepts a project name or a project_id, but project is the clear field for those.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.30.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / project
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Project name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both.",
+  "title": "Project"
+}
    • changedInput schema / properties / project_root / description
      Previous value: -"Project root. Auto-detected if omitted. Used to locate LLM config. Falls back to auto-detection when omitted."New value: +"Project root. Auto-detected if omitted. Used to locate LLM config. Falls back to auto-detection when omitted. This field also accepts a project name or a project_id, but project is the clear field for those."
  2. Changed1 schema field changedv0.25.3
    • changedInput schema / properties / project_root / description
      Previous value: -"Project root. Auto-detected if omitted. Ignored by this tool."New value: +"Project root. Auto-detected if omitted. Used to locate LLM config. Falls back to auto-detection when omitted."
  3. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it meets it with 'Read-only: yes. No side effects.' It also exposes the complete status/return behavior, including error/disabled states, external chat API replacement, and conditional fields. It does not mention timeout or network-failure behavior explicitly, but the 'error' status and message field cover that outcome.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and usage before moving to arguments and return values, which is good structure. The returns block is long but justified because there is no output schema to document the response shape. The only minor redundancy is repeating the schema parameter descriptions nearly verbatim.

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?

The description fully compensates for the missing output schema by listing the complete return dict with field types and meanings, including optional/conditional fields. It also documents parameter selection rules, auto-detection, and mutual exclusivity. Nothing essential for invoking check_ollama correctly is missing.

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?

Both parameters are already fully documented in the input schema with 100% coverage, so the description adds little beyond restating the same project_root/project semantics. A baseline of 3 is appropriate because the schema carries the semantic weight and no additional syntax or format details are needed.

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 first sentence states a specific verb ('Check') and resource ('LLM backend... embedding/chat model installed'), which makes the tool's purpose unmistakable. It also names the sibling tools it should precede, helping distinguish it from search/analysis tools. This goes beyond a vague restatement of the tool name.

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 'Call before smart_search, semantic_search, or explain_symbol' and includes the on-demand fallback condition, which is strong usage guidance. It does not explicitly discuss alternatives like check_dependencies or get_environment_status, so the when-not-to-use guidance is slightly incomplete. Overall, the timing and conditional exception are clear enough for correct agent routing.

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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