freedom-mcp

list_leads

List the actual leads (id, name, email) in the current company, optionally filtered to one exact segment tag. READ-ONLY — returns the roster so an agent can act on a segment without asking the operator to paste addresses; it contacts no one and changes nothing. Contactable leads come back under leads; leads carrying the tag but blocked by a safety exclusion (do-not-contact, archived, non-active state) are counted separately and only itemized when include_excluded=true. Use when the operator says 'who is in ', or before enrolling/drafting for named leads. To enroll a whole segment in one call, prefer enroll_by_segment.

Routing: CRM/sales → who is in this segment / list the leads / get lead emails → use this

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Optional — max contactable leads to return (default 100, max 500).
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`segment_tag`	No	Optional — exact segment tag token from crm_leads.source, e.g. 'csv:free-trial'. Omit to list across all segments. No substring matching.
`include_excluded`	No	Optional — when true, also itemize the leads excluded by safety checks (with reasons). Default false (count only).

TDQS

A4.6/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden. It clearly states 'READ-ONLY', 'it contacts no one and changes nothing', and details the safety exclusions (do-not-contact, archived, non-active state) and how excluded leads are handled. This is transparent behavioral disclosure beyond a simple 'list' operation.

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?

The description is somewhat longer but front-loaded with the core purpose, followed by behavior, use cases, and routing. Every sentence adds value, though it could be tightened slightly.

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 tool with no output schema, the description explains the return structure (contactable leads under `leads`, excluded leads counted separately and only itemized when include_excluded=true) and covers usage context and safety exclusions. This is complete for the tool's complexity.

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 baseline is 3. The description reinforces the schema by mentioning filtering by `segment_tag` and the behavior of `include_excluded`, but adds no significant semantic information beyond what the schema already provides.

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 'List the actual leads (id, name, email) in the current company, optionally filtered to one exact segment tag.' This is a specific verb+resource+scope, and it distinguishes from enroll_by_segment by saying 'prefer enroll_by_segment' for whole-segment enrollment.

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?

It explicitly says 'Use when the operator says "who is in <segment>", or before enrolling/drafting for named leads.' It also notes the alternative: 'To enroll a whole segment in one call, prefer enroll_by_segment.' The routing line further clarifies the use case.

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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