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veto

by jigyasudham
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TypeScript
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veto_session_save

Saves the current AI session context to a local SQLite database, with optional auto-summarization and in-place updates for existing sessions. Enables resuming work across CLI tools.

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.
platformNoThe AI CLI you are running in right now — claude, gemini or codex. Selects the correct context window for threshold calculation. Defaults to the CLI Veto detects from the MCP handshake, so pass it only if you know better.
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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.8.0
    • changedInput schema / properties / platform / enum
      Previous value: -[
-  "claude",
-  "gemini",
-  "codex"
-]New value: +[
+  "claude",
+  "gemini",
+  "codex",
+  "antigravity"
+]
  2. Changed1 schema field changedv3.2.0
    • changedInput schema / properties / platform / description
      Previous value: -"AI platform used (claude, gemini, codex). Defaults to \"claude\"."New value: +"The AI CLI you are running in right now — claude, gemini or codex. Selects the correct context window for threshold calculation. Defaults to the CLI Veto detects from the MCP handshake, so pass it only if you know better."
  3. First observedv2.7.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only say readOnlyHint=false and destructiveHint=false, so the description carries the interesting load and does so well: it discloses the manual-write failure mode (state corruption, lost project scoping), explains that auto_summarize uses MCP Sampling with a fallback to supplied values, and notes that session_id prevents 'session inflation.' Remaining gaps like confirmation behavior or overwrite semantics keep it from a 5.

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 trigger and routing instruction are front-loaded, and the em-dash warnings are load-bearing rather than filler. It is slightly long, but every clause carries operational information, so it stays efficient.

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?

For an 11-parameter mutation tool with no output schema, the description covers the essentials an agent needs to call it correctly: when to invoke, the auto_summarize shortcut, and the session_id update path. The schema already carries the per-parameter detail, so nothing critical is missing, though the multi-parameter default workflow could be spelled out a touch more.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3; the description still adds value by explaining the recommended parameter interplay ('pass auto_summarize: true and omit summary/context/task_state') and the in-place update semantics of session_id. That goes beyond the individual per-field descriptions without fully documenting all 11 params itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Saves the current session context to SQLite,' which is immediately distinguishable from veto_sessions_list (read) and veto_session_restore (rehydrate). It does not name those siblings explicitly, so differentiation relies on the reader inferring from the verb, keeping this just below a 5.

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?

It gives an explicit trigger ('when the user types veto_session_save / veto_save_session / save session'), names the problematic alternative it wants avoided (hand-writing a node script or INSERT into ~/.veto/veto.db), and states why that is wrong (bypasses project scoping, corrupts state). It also states the condition that selects update-in-place via session_id. This is exactly the when/when-not/alternative structure that earns a 5.

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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