SendIt

List AI Agents

list_agents

Read-only

List all available AI agents and their capabilities.

SendIt includes 12 specialized agents: • Strategy Planner - Content strategy from audience/trend analysis • Content Ideation - Topic ideas from trends and calendar gaps • Multi-Format Composer - Platform-optimized content from a brief • Creative Asset - AI image/video generation orchestration • Variant Repurposer - Repurpose content for different platforms • Calendar Optimizer - Optimal posting time suggestions • Listening Analyst - Social mention and sentiment analysis • Inbox Reply - Contextual reply drafts with brand voice • Campaign Builder - Ad campaign structure recommendations • Budget Optimizer - Spend pacing and budget reallocation • Experimentation - A/B test design and analysis • Executive Insights - Executive summary reports

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

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

First observedMay 5, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds the behavioral guarantee of 'List all' (no pagination or filtering). It also provides the full inventory of agents, giving context about what the list will contain. No contradictions 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 consists of one clear purpose sentence followed by a well-structured bulleted list of all 12 agents. Every line provides distinct value, with no filler or redundant information. It is front-loaded and scannable.

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 zero-parameter, read-only list operation with no output schema, the description is fully complete. It states what the tool returns (all agents and capabilities) and enumerates the actual agents. Without complex input/output structures, nothing is missing.

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?

The tool has zero parameters and schema description coverage is 100%, so the schema fully documents the input. Per the rubric, zero parameters baseline is 4, and the description adds no unnecessary param semantics. The absence of parameters is consistent with a 'list all' operation.

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 'List all available AI agents' uses a specific verb and resource, and immediately distinguishes this tool from siblings like list_agent_runs (which lists runs, not agents) and invoke_agent (which runs an agent). It also lists all 12 agents, making the scope unambiguous.

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 clearly implies this tool is for discovering available agents and their capabilities, and there are no exclusions mentioned. It does not explicitly name alternatives like get_agent_policies or invoke_agent, but the context of listing is clear from the description and title.

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

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