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Glama

send_message

Destructive

Reply to customers in an Instagram, WhatsApp, or Messenger conversation by posting a text, image, document, audio, or video message.

Instructions

Send a reply message in a conversation. Use for responding to customers across Instagram, WhatsApp, or Messenger.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesMessage text content
content_typeNotext
conversation_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false and destructiveHint=true, so the safety profile is covered. The description adds that this is a reply inside an existing conversation (hence conversation_id is required), but omits materially relevant behavior such as messaging-window restrictions, rate limits, or failure semantics.

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?

Two short, front-loaded sentences with no filler; the action is stated first and channel scope second. Minor overlap between the two sentences keeps it from being maximally economical.

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?

For a mutation tool with no output schema and only partial parameter documentation, the description is just adequate. It should cover the messaging-window constraint, what content_type expects for non-text sends, and what a caller gets back on success or failure, none of which is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%: only 'content' is documented, while conversation_id and the content_type enum (text/image/document/audio/video) are undocumented. The description says nothing about any parameter and does not compensate for the gap, leaving an agent to guess how content_type interacts with the message body.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Send a reply message in a conversation') and names the supported channels, which implicitly separates it from channel-specific siblings like send_whatsapp_template. It stops short of explicitly contrasting itself with those siblings, so it is clear but not fully differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use for responding to customers' implies the conversational-reply context, but there is no when-not guidance, no mention of the 24-hour messaging window, and no pointer to send_whatsapp_template as the alternative for out-of-window or template-based sends. Usage is inferred rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.