plith

rigor_execute

Execute a structured workflow end-to-end. Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits. Classifies the task, selects the optimal tool sequence, and executes each step with the right LLM model. Returns a complete deliverable — solution designs, competitive analyses, governance documents, and more. Supports SSE streaming for real-time progress, webhook callback, or polling. For atomic work — classification, scoring, ranking, entity extraction, query parsing — set preferences.execution to 'direct' and declare preferences.output_contract to get validated JSON records from a single call, routed to the cheapest model that holds the schema.

Input Schema

TableJSON Schema

Name	Required	Description
`context`	No	Additional context for the workflow.
`delivery`	No	Delivery method. Default: polling (MCP clients typically can't handle SSE).
`task_type`	No	Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape.
`preferences`	No	Optional workflow preferences.
`task_description`	Yes	Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No
`status`	No
`poll_url`	No
`execution`	No	Present with value 'direct' when direct execution ran. Absent for standard multi-call execution.
`task_type`	No
`value_class`	No
`workflow_id`	No
`delivery_mode`	No
`available_modes`	No
`estimated_credits`	No
`execution_fallback`	No	True when you explicitly requested direct execution and it could not be honoured — the workflow ran as standard multi-call instead. Never set for an auto-selected attempt, since you did not ask.

TDQS

A4.1/5.0

Behavior4/5

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

Annotations only establish that this is a non-read-only, non-idempotent operation, but the description discloses substantial behavior beyond that: the call 'commits credits,' it 'classifies the task, selects the optimal tool sequence, and executes each step with the right LLM model,' and it supports SSE, webhook, or polling delivery with a complete deliverable as the return. There is no contradiction with the annotations. It could add failure or partial-execution behavior, but the credit-commitment warning alone is significant added value.

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 the core purpose and the most important prerequisite (rigor_plan first) front-loaded before delivery and direct-mode detail. The final sentence is dense, packing atomic task types, execution mode, and output contracts into one long clause, but given the tool's complexity the length earns its place.

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 5-parameter tool with nested objects, a full output schema, and annotations present, the description covers the essential operating model: workflow orchestration, credit commitment, deliverable types, delivery channels, and the direct-execution shortcut. Nothing an agent needs to decide whether and how to call it is missing; remaining details like fallback conditions and the MCP polling default live in the schema, which is acceptable given the schema's richness.

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 100%, so the schema already documents all five parameters in depth, including the nested delivery, preferences, and output_contract objects. The description adds usage-level meaning — how task_type shortcuts classification and when to set execution to 'direct' — which helps an agent decide, but the detailed interaction semantics (fallback conditions, closed-schema validation, model floors) live in the schema descriptions themselves.

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 opening line, 'Execute a structured workflow end-to-end,' pairs a specific verb with a clear resource, and the following sentences detail the orchestration behavior: it classifies the task, selects the tool sequence, and executes each step with the right LLM model. It also distinguishes itself by explicitly directing the caller to rigor_plan first and by framing direct execution for atomic work as a distinct path, so an agent can separate it from the sibling planning, status, and workflow tools.

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?

The description gives explicit sequencing guidance — 'Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits' — and it specifies when to use direct execution: 'For atomic work — classification, scoring, ranking, entity extraction, query parsing — set preferences.execution to "direct".' It names rigor_plan as the alternative but does not route to sibling tools like rigor_status for monitoring or burnrate_* for budget checks, so the exclusions are only partial.

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

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by functionality (cost, dedup, guardrails, pitfalls, quality, rigor). Descriptions clearly differentiate within groups, so an agent can reliably select the correct tool.

Naming Consistency5/5

All tool names follow a consistent pattern of lowercase with underscores, using category prefix (burnrate_, dedupq_, guardrail_, etc.) followed by a descriptive verb or noun. No naming mix-ups.

Tool Count5/5

15 tools cover multiple distinct capabilities (cost management, deduplication, guardrails, pitfalls, quality, workflows) without being excessive. Each tool has a clear role, and the count is appropriate for the server's scope.

Completeness4/5

The tool surface covers core workflows well, but there are minor gaps: no tool to delete/update guardrail policies, no explicit qualitygate rule management, and no workflow cancellation tool. Also, 'qualitygate_trends' is referenced but not provided as a tool. Overall, agents can work around these gaps.

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