freedom-mcp

submit_content_to_pipeline

Submit manual content to a pipeline for transformation. Use when user says "add this to my changelog", "create a newsletter from this", "transform this content", or provides content to be processed. Content will be transformed using the pipeline's persona and ICPs. Social pipelines publish to the pipeline's declared destination (x/linkedin/instagram/facebook/threads — set via update_pipeline; undeclared defaults to x) after human approval; instagram items REQUIRE media_artifact_ids.

[write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

Input Schema

TableJSON Schema

Name	Required	Description
`content`	Yes	Raw content to transform (updates, notes, announcements, etc.)
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`pipeline_id`	Yes	ID of the pipeline to submit to (get from list_pipelines)
`media_artifact_ids`	No	Optional. Artifact IDs (image or video, from generate_image_xai / generate_video, same company) to attach as media on this post. Required for visual social posts — the post publishes with this media attached. Each ID must belong to this company.

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavior: content transformed using pipeline's persona/ICPs, social pipelines publish to declared destinations after human approval, Instagram requires media_artifact_ids, and note about write-tier approval (first-use, from-now-on, just-once).

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?

Description is informative but somewhat lengthy. However, it front-loads the purpose and each sentence adds necessary detail. Slightly verbose but well-structured.

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 4-param tool with no output schema, the description covers key context: transformation pipeline, social publishing behavior, approval flow, and media requirements. An agent has sufficient information to decide when and how to invoke this 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%, but description adds value: pipeline_id from list_pipelines, media_artifact_ids from generate_image_xai/generate_video and required for visual social posts. Not all parameters get extra context, but the additional info is useful.

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?

Clearly states the tool submits manual content to a pipeline for transformation. The verb 'submit' and resource 'content to pipeline' are explicit. Distinguishes from siblings by focusing on manual content submission, not listing or other operations.

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?

Provides explicit usage triggers like 'add this to my changelog' and 'transform this content'. Also explains pipeline behavior (transformation, social publishing, destinations) and first-use approval process, guiding when and how to use it.

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.

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