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veto_session_save

Save the current conversation session to a local SQLite database for later resumption. Use auto_summarize to let Veto generate a structured summary automatically.

Instructions

Saves the current session context to SQLite. TRIGGER: when the user types veto_session_save / veto_save_session / save session, call THIS live MCP tool directly — never write a node script or INSERT into ~/.veto/veto.db by hand (that bypasses project scoping and corrupts state). Set auto_summarize: true to have Veto read the full conversation and generate an accurate structured summary itself — no manual writing needed. Pass session_id to update an existing session in-place.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional labels for this session (e.g. ["auth", "migration", "v1.3"]). Makes sessions searchable via veto_sessions_list query.
modelNoOptional: specific model ID (e.g. "claude-sonnet-4-6", "gemini-3.5-pro", "gpt-5.1"). Veto resolves the exact context window for this model and uses it for auto-save threshold calculations.
contextNoKey context to restore (decisions, current task, file list, etc.). Optional when auto_summarize is true.
summaryNoA brief summary of what was accomplished. Optional when auto_summarize is true.
platformNoAI platform used (claude, gemini, codex). Defaults to "claude".
session_idNoOptional. UUID of an existing session to update in-place. When provided, Veto updates that row instead of inserting a new one — prevents session inflation when refreshing mid-conversation.
task_stateNoCurrent task state — what is done and what is next. Optional when auto_summarize is true.
project_dirNoAbsolute path to the current project directory.
token_countNoApproximate tokens used this session. Veto uses this for context window monitoring.
auto_summarizeNoWhen true, Veto reads the full conversation context via MCP Sampling and generates summary, context, and task_state automatically — including specific file paths, decisions, and a concrete nextAction. Recommended: pass true and omit summary/context/task_state. Falls back to provided values if sampling is unavailable.
connection_typeNoHow you are connected to this AI — "subscription" (Claude Pro, Gemini Advanced) or "api" (API key). Used for usage tracking.
Behavior5/5

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

The description explains the tool's behavior in detail: it writes to SQLite, can update in-place, uses MCP Sampling for auto-summarization, resolves context windows per model. Annotations are minimal, so description carries the full behavioral disclosure burden and does so excellently.

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 information-dense yet concise. It is front-loaded with the core action, followed by a warning, then parameter guidance. Every sentence adds value; no fluff.

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

Completeness4/5

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

The description thoroughly covers the tool's purpose, triggers, and parameters. However, it does not mention return values or success/failure behavior, which is a minor gap given the lack of an output schema. Still, the description is largely complete for a save operation.

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?

Schema coverage is 100%, but the description adds significant meaning beyond the schema. For example, auto_summarize is explained with its automatic generation logic, session_id with its update/insert logic, and model with context window resolution. Each parameter gets additional context that aids correct usage.

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 clearly states the tool 'saves the current session context to SQLite' and specifies triggers (user typing specific commands). It distinguishes itself by warning against manual DB writes, setting clear boundaries.

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 explicitly tells when to call the tool (on specific user commands) and when not to (never write manual INSERT). It provides detailed guidance on using auto_summarize and session_id, making usage unambiguous.

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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