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freedom-mcp

by com.getfreedomos

get_release_ledger

THE tool for "did this piece ship on this channel" — reads the cross-channel Release Ledger, the queryable truth for every confirmed send (x/linkedin/instagram/facebook/threads, hub letters, Beehiiv) written by the publish rail itself at send time. Use this instead of title-matching or a markdown tracking doc when a reconciler or operator asks whether a piece released, where it released, or wants a recent-releases feed. Returns rows plus a per-piece coverage summary (which channels a piece is KNOWN to have shipped on — never a speculative claim about what's missing).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many recent releases to return, newest first (default 50, max 200).
sinceNoISO timestamp lower bound — only releases at/after this time.
channelNoFilter to one channel.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
piece_keyNoFilter to one piece's releases (e.g. 'output:<pipeline_outputs.id>', 'idea:<content_ideas.id>', 'hub-letter:<slug>').
released_byNoFilter by who released it: 'agent', 'human', or 'system'.

TDQS

A4.3/5.0
Behavior4/5

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

Discloses that the ledger is written by the publish rail at send time and that the coverage summary only includes KNOWN shipped channels, not speculative missing ones. With no annotations provided, this description carries the burden well.

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 that are dense with information: purpose, usage, output. No wasted words, though slightly long; still well-structured and 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?

Given 6 parameters, no output schema, and no annotations, the description covers purpose, usage, and output shape (rows + coverage summary). Lacks error handling but is sufficient for a read-only query tool.

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 coverage is 100% with good descriptions for all 6 parameters. The description adds some context (e.g., 'piece_key' filter examples) but does not significantly extend 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 a specific verb and resource ('reads the cross-channel Release Ledger') and a clear purpose ('did this piece ship on this channel'), which distinguishes it from siblings like title-matching or markdown tracking docs.

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 says when to use ('when a reconciler or operator asks whether a piece released, where it released, or wants a recent-releases feed') and what alternatives to avoid ('instead of title-matching or a markdown tracking doc').

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

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

Resources