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continuum_confirm

Confirm a run's self-reported goal or progress so a blocked agent-reported run can resume; scope the confirmation to goal or progress when other uncertainty remains.

Instructions

Confirm a run's self-reported goal and progress so it can resume. MCP/agent-reported runs are self_certified and would otherwise be stuck at request_human forever. REFUSED unless the server operator set CONTINUUM_MCP_CONFIRM_TOKEN and you present that secret in the handshake _meta.authToken: an agent must not confirm its own self-reported state. The normal path is for a human to run 'continuum confirm <run_id>' on the host. Pass scope=["goal"] or ["progress"] to confirm only that component, leaving any other uncertainty intact (issue #394). Omit scope for a full confirm of both. Mutates the run.

Input Schema

NameRequiredDescriptionDefault
scopeNo
run_idYes
expected_modelNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only say readOnlyHint=false, but the description adds far more: the operation mutates the run, it will be REFUSED without an operator-set token, and an agent must not self-certify. That refusal condition is critical behavioral information an agent cannot get from structured fields.

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?

Front-loaded with purpose, then auth gate, then parameter semantics — good ordering. Dense and nearly every sentence earns its place, though the issue #394 citation and the restated 'Mutates the run' at the end add minor noise.

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 a mutating, auth-gated tool the description covers safety, refusal conditions, and scope semantics thoroughly, and an output schema exists so return values need no explanation. The only gap is expected_model, which is undocumented in both schema and description.

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 0%, so the description must compensate. It does for scope (["goal"] / ["progress"] vs omit for full confirm), but 'expected_model' is never mentioned and 'run_id' is only implied. Strong partial coverage of a 3-param tool, with one clear omission.

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 verb and resource ('Confirm a run's self-reported goal and progress so it can resume') and immediately frames the situation that distinguishes it from siblings like continuum_resume or continuum_record_progress. An agent can tell exactly what this does without opening the schema.

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?

Explicitly names the preferred alternative ('the normal path is for a human to run continuum confirm <run_id> on the host') and the exact precondition for the agent path (CONTINUUM_MCP_CONFIRM_TOKEN presented as _meta.authToken). It also explains the partial-vs-full confirm choice via scope, leaving nothing to inference.

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