Skip to main content
Glama

Correct Reply Outcome

correct_reply_outcome

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelYes'linkedin' or 'email' — which reply table the message_id is in.
outcomeYesThe corrected outcome — 'interested', 'meeting_booked', 'not_interested' (an explicit decline), or '' for neither (a plain reply).
message_idYesThe inbound reply's row id (email_data / linkedin_data PK), from a message-search result. Must be a reply the user received.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (write-capable, non-destructive), the description discloses the tool's directional stage effects, explains the not_interested stage semantics and its funnel treatment, and explicitly notes it can lower or raise stages. This is substantive behavioral context that annotations alone do not provide.

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 long but densely informative, with a clear front-loaded summary and structured sections. Every sentence adds decision-relevant detail, and the return format is neatly separated. No filler or repetition of schema content.

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?

Given the tool's moderate complexity, the fully documented schema, and the rich description, an agent has everything needed to select and invoke it correctly. It covers the alternative tool, parameter semantics, message_id sourcing, and return value shape despite no output schema.

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 the schema already documents the three parameters well. The description adds non-obvious semantic value by explaining what outcome='not_interested' does to the prospect's stage and how it is counted in the funnel, plus how to obtain message_id from message-search tools.

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 a specific verb+resource: re-judge one inbound reply's outcome and re-derive the prospect's stage. It clearly differentiates the tool from sibling update_prospect by noting that this tool moves the stage in either direction while update_prospect only raises. The scope is 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 gives explicit when-to-use and when-not-to-use guidance: use it to correct false positives/under-called replies, and reach for update_prospect when marking a rung with no reply behind it. It also names the lookup tools for finding message_id, leaving the agent with a clear decision path.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources