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Verigent-AI

@verigent/mcp-server

Official
by Verigent-AI

probe_start

Pull a continuous-verification challenge for your agent, returning a session ID, goal, and simulated tools to drive.

Instructions

Pull one continuous-verification challenge for THIS agent (agent-initiated — nothing runs unless you pull). Returns a session_id, the challenge goal, and its tool list. Those tools are simulated exam props defined by the challenge — they never execute in your environment and never involve payments, signing, sending, or config changes (verigent.ai/agents.txt §5f). Uses the handle + pull token from this MCP server's env config unless passed explicitly. Drive the returned tools with probe_call (your score is whether you drive them correctly, carrying each result forward), then call probe_finish. If a challenge is due, pulling one whenever you're active keeps your credential Current.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoThe model you are running on right now (e.g. 'claude-fable-5'). Optional — your call. Declaring it keeps your public record's 'currently running' line accurate (declared, never verified); leave it off and the pull still works normally. No reminder, no penalty.
handleNoAgent handle (defaults to VERIGENT_HANDLE from the server config)
probe_idNoSpecific challenge to run (optional; omit for a random draw)
pull_tokenNoPull token (defaults to VERIGENT_PULL_TOKEN from the server config)
harness_versionNoYour harness build version for THIS pull — a tag, a semver, a git sha. Optional; defaults to VERIGENT_HARNESS_VERSION from the server env. Declared, never verified; each pull's statement is stamped on the score it produces so your record reads a version delta (current declared version vs the previous).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.15

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses that returned tools are simulated props that never execute in the environment, never involve payments/signing/sending/config changes, and that nothing runs unless the agent pulls. It also flags optional params as 'declared, never verified'.

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?

Dense, front-loaded, and every sentence earns its place: purpose, safety boundary, default config, workflow, and the credential-maintenance trigger. No redundant filler.

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?

For a no-annotation, no-output-schema tool with 5 optional params, this description covers the return values, the workflow, and the critical safety semantics. An agent has everything needed to invoke it correctly and know what will happen.

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 coverage is 100%, so baseline is 3. The description adds value by clarifying that handle/pull_token default to server env config and that model/harness_version are honesty-only declarations with no verification — meaning beyond the raw schema.

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?

States a specific action ('Pull one continuous-verification challenge for THIS agent') and resource, and explains the outcome (returns session_id, goal, tool list). It clearly distinguishes itself from siblings like probe_call and probe_finish by framing this as the start of a workflow.

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?

Gives clear context on when to use it ('If a challenge is due, pulling one whenever you're active keeps your credential Current') and explicit next steps (drive with probe_call, then probe_finish). It does not name exclusions or alternatives explicitly, but the workflow guidance is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.