freedom-mcp

grant_agent_tool

Grant ONE specific tool to an agent's loadout (tool_access). Use when an operator says "give the tool" / "let use ". The tool name is validated against the live registry at write time — phantom names are rejected, deprecated names auto-map to their successor. For wholesale capability re-derivation use recalibrate_agent_jd instead; connector tools auto-provision on connection.

[sensitive-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
`reason`	No	Optional one-line why — stored in the audit record on the agent's JD.
`agent_id`	Yes	UUID of the agent receiving the tool. Use get_team_roster to find IDs.
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`tool_name`	Yes	Exact registry name of the tool to grant (e.g. "capture_idea").

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description reveals validation behavior (live registry, rejection of phantom names, auto-mapping of deprecated names) and sensitive-tier approval requirements. It does not explicitly state that it's a write operation, but it's implied by 'grant'. No contradiction with annotations.

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?

The description is concise with two paragraphs, front-loading the core purpose. Every sentence adds value without redundancy. It is well-organized: purpose, usage, alternatives, behavior, and approval info.

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?

The tool lacks an output schema, which might warrant a brief mention of return values (e.g., success status or error). However, the purpose and usage are clearly covered, and the parameters are thoroughly explained, making it fairly complete. Minor gap for output.

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 baseline is 3. The description adds context beyond the schema: 'agent_id' can be found via 'get_team_roster', 'tool_name' is validated against the live registry, and 'reason' is optional and stored in the audit record. This enriches the parameter meaning.

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 clearly states the tool's verb ('Grant') and resource ('specific tool to an agent's loadout'). It distinguishes from the sibling 'recalibrate_agent_jd' and mentions behavior for connector tools, making the purpose unambiguous.

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?

The description explicitly states when to use (operator requests like 'give <agent> the <tool> tool') and when not to use (wholesale re-derivation via 'recalibrate_agent_jd'). It also provides context on validation, auto-mapping, and approval tiers.

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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