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Update Agent Policy

update_agent_policy

Create or update a policy/guardrail for an agent.

Policy types: • brand_safety - Content must align with brand guidelines • platform_compliance - Must follow platform ad policies • approval_gate - Require human approval before execution • budget_limit - Cap spending or resource usage • content_filter - Filter certain topics or language

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rulesYesPolicy rules
activeNoWhether policy is active
agentIdYesAgent to set policy for
policyTypeYesPolicy type

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations indicate write access (readOnlyHint=false) and non-destructive hint, which aligns with the description's 'create or update' wording. The description adds behavioral context by explaining what each policy type does (e.g., approval_gate requires human approval), but it does not disclose whether updates replace or merge existing policies, leaving a transparency gap.

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 and well-structured: a clear opening statement followed by a bulleted list of the five policy types. Every sentence and bullet adds meaningful information, with no redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, and the description does not mention return values or side effects. It also lacks details on how updates affect existing policies (replace vs. merge), which is important for a mutation tool. However, the parameter descriptions and policy type explanations cover much of the necessary context, making it adequate but not complete.

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?

With 100% schema description coverage, the baseline is 3. The description goes beyond the schema by providing full semantics for the policyType enum values (e.g., brand_safety – 'Content must align with brand guidelines'), which helps agents construct valid policyType selections. It does not add detail for rules or active, but those are already adequately described in 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 clearly states the action ('Create or update a policy/guardrail') and the target resource ('for an agent'), distinguishing it from read-only sibling tools like get_agent_policies. The enumeration of policy types with practical meanings adds specificity and confirms it is the tool for managing agent guardrails.

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?

The description implies when to use the tool—whenever an agent's policy needs to be created or updated—by listing the policy types that can be set. It does not explicitly mention alternatives or exclusions (e.g., 'to view policies use get_agent_policies'), but the context is clear given the sibling list includes a read-only counterpart.

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

B3/5.0
Disambiguation2/5

With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.

Naming Consistency3/5

Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.

Tool Count1/5

148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).

Completeness3/5

The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.

Resources