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ChiefLab

by io.chieflab.api

chieflab_query_proof_ledger

P80 — query the persistent proof ledger. USE WHEN the user (or a dashboard) asks 'what has ChiefLab actually shipped for this workspace?' or 'show me the launch history.' Returns the proof rows for executed publishes / sends / manual-posts with the artifact URLs, channels, execution modes, and measurement state. Persistent across cold starts when deps.proofLedgerStore is wired to Supabase; falls back to in-memory (warm function lifetime) otherwise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size (1-500, default 50).
runIdNoOptional. Filter to one run's proofs.
workspaceIdNoOptional workspace id (defaults to auth workspace).
sinceTimestampNoOptional ISO timestamp; only return proofs published at or after.
measurementDueOnlyNoWhen true, returns only rows with measurement_due_at <= now AND measured_at IS NULL. Use this to find what's ready for 24h readback.

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided, so the description must carry the burden. It discloses persistence behavior (Supabase vs. in-memory fallback) and outlines the return fields (artifact URLs, channels, execution modes, measurement state). It does not cover rate limits or auth details, but for a read-only query tool, this is sufficient.

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?

Two sentences: the first introduces the tool, the second gives usage guidance and return context. No redundant words. Efficiently front-loaded.

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?

Covers usage, scope, persistence, and result fields. No output schema exists, so the description's mention of return fields helps. Missing details on error cases or empty results, but these are minor omissions for a query tool.

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 parameters are already documented. However, the description adds value by summarizing the return content (artifact URLs, channels, etc.) and explaining the purpose of each parameter group (e.g., 'measurementDueOnly' described separately). This goes beyond the 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?

The description states 'query the persistent proof ledger' and provides specific use-case examples ('what has ChiefLab actually shipped?', 'show me the launch history'). It clearly distinguishes this tool from siblings, none of which perform the same query function.

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?

Explicitly tells when to use ('USE WHEN the user asks...'), but does not mention when not to use or list alternatives. The sibling tools do not include another proof ledger query, so the guidance is clear but could be more comprehensive.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes, e.g., approve_action vs execute_approved_action vs publish_approved_post. However, alias overloading (e.g., chieflab_launch_product and chieflab_get_users_after_build pointing to the same handler) introduces some ambiguity. The detailed descriptions mostly mitigate confusion, but an agent might still struggle to choose between near-identical aliases.

Naming Consistency4/5

Tools predominantly follow a 'chieflab_verb_noun' pattern (e.g., chieflab_approve_action, chieflab_connect_provider). A few exceptions exist (chieflab_help, chieflab_inbox, chieflab_boot) that are single nouns, but these are clearly distinct and the overall consistency is high.

Tool Count3/5

32 tools is on the high side for an MCP server, but the domain of a growth/marketing launch platform naturally requires many operations (launch, approve, execute, measure, iterate, connect providers, etc.). The count is borderline but still manageable; it doesn't reach the 50+ extreme.

Completeness4/5

The tool set covers the full launch lifecycle: create, approve, execute, measure, and iterate. It includes provider connections, manual fallback, brain summary, and work requests. Minor gaps exist (e.g., no explicit tool for deleting a launch or revoking approval), but core workflows are fully supported.

Resources