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invinoveritas

by com.babyblueviper

execute

Destructive

Paid secure Python execution that serious agents actually use. Run real code (data pipelines, backtests, repo analysis, scraping + processing, small automation) with full audit trails and cryptographic proofs. Use use_workspace=true (recommended for any non-trivial work) to get a persistent per-agent development environment at /workspace. Restrictive mode for safety; Permissive mode (higher tier) for arbitrary code inside a hardened container.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
tierNo
stdinNo
agent_idNoOptional caller agent ID
languageNopython
permissiveNoAllow arbitrary imports and code (container is the security boundary). Priced higher.
use_workspaceNoMount a persistent per-agent workspace at /workspace (rw). Files, git clones, and installed packages survive across calls. Strongly recommended for any non-trivial multi-step work.
timeout_secondsNo

TDQS

A4/5.0
Behavior4/5

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

Annotations already signal destructive/open-world behavior. The description adds meaningful context: persistent workspace, audit trails, cryptographic proofs, and hardening details. It stops short of detailing specific side effects like network access, but enough for initial understanding.

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?

Three sentences with a front-loaded purpose. The phrase 'serious agents actually use' is minor fluff, but the rest is dense and useful. Efficient overall.

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

Completeness3/5

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

For an 8-parameter execution tool with no output schema, the description covers workspace persistence and security modes but omits return format (stdout/stderr), error behavior, and resource constraints. Adequate for basic use but incomplete for complex scenarios.

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?

Schema description coverage is only 38%. The description adds context for use_workspace (recommended) and permissive (higher tier), but does not clarify tier levels, code formatting, timeout behavior, or stdin. It partially compensates but leaves gaps.

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 identifies the tool as Python code execution with specific use cases (data pipelines, backtests, repo analysis). It distinguishes from siblings by emphasizing secure, audited execution and explicitly mentions running real code.

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?

Provides concrete use cases and strongly recommends use_workspace for non-trivial work. It doesn't list exclusions or alternatives, but no direct sibling alternative exists, and the mode guidance (restrictive vs permissive) helps selection.

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

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is potential confusion between 'decision' and 'reason', both offering advisory output. Also, 'review', 'witness', 'prove', and 'verify_proof' overlap in the proofs space, though descriptions differentiate them. Overall, an agent can disambiguate with careful reading.

Naming Consistency4/5

All tool names use lowercase and underscores (snake_case), which is consistent. However, the verbs vary: some are imperative (e.g., 'browse', 'execute'), while others are nouns (e.g., 'signals', 'ledger'), breaking a strict verb_noun pattern. Overall, the naming is readable and mostly predictable.

Tool Count2/5

With 30 tools, the surface is too large for a well-scoped server. Many functions could be separated (e.g., memory, workspace, feedback, marketplace). This excess makes it harder for an agent to navigate and select the right tool quickly.

Completeness3/5

The tool set covers core CRUD for memory and workspace, plus feedback, marketplace purchase, bounties, and verification. However, there is no tool to list or search marketplace listings, and workspace creation is only implicit via 'execute'. These gaps hinder fluid workflows.

Resources