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MisarReach MCP Server

by io.github.Misar-AI

submit_lead_feedback

Idempotent

Record whether an AI-generated outreach message for a lead was good or bad, as training signal for future generations.

Use it when the user judges a drafted message — it improves later output rather than changing anything now. It does not edit, resend, or delete the message, and it sends nothing to the lead.

Writes a feedback record; sending the same verdict twice is harmless. Requires an API key. Costs no credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesLead search job UUID
feedbackYesFeedback sentiment
leadEmailYesEmail address of the lead

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=false and idempotentHint=true; the description adds valuable context by stating 'Writes a feedback record', 'sending the same verdict twice is harmless', 'Requires an API key', and 'Costs no credits'. These details go beyond what annotations convey, especially regarding auth and cost.

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?

Four compact sentences, beginning with a clear purpose statement, followed by when-to-use, behavioral notes, and side effects. There is no fluff or redundancy; every sentence earns its place.

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 simplicity (a single write operation with three well-documented parameters, no nested objects, no output schema), the description covers purpose, usage, side effects, idempotency, auth, and cost. It is entirely sufficient for correct agent behavior.

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

Parameters3/5

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

Schema coverage is 100% and each parameter has a concise description (jobId, leadEmail, feedback). The description does not add significant new meaning beyond the schema—it only generally refers to 'feedback'—so it meets the baseline of 3 without needing to compensate.

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 uses a specific verb-resource combination ('Record whether an AI-generated outreach message for a lead was good or bad') and clearly distinguishes the tool from siblings like preview_message or send_to_campaign by stating it is a training signal and explicitly noting what it does not do (edit, resend, delete, send to the lead).

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 states explicitly when to use it ('when the user judges a drafted message') and provides exclusions and clarifications ('it does not edit, resend, or delete the message, and it sends nothing to the lead'), giving the agent clear context for appropriate invocation.

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

A4.3/5.0
Disambiguation5/5

Every tool targets a distinct resource and action: deals, leads, lists, autopilot runs, sales agent config, channels, and discovery. Even where tools share an entity, they are clearly differentiated (e.g., list_deals vs get_pipeline, update_deal vs move_deal_stage), with descriptions explicitly calling out when to use which.

Naming Consistency5/5

All 27 tools follow a strict verb_noun snake_case pattern: create, get, list, update, move, search, score, sync, start, submit, verify, etc. No mixed conventions or vague verbs—each name precisely signals its function.

Tool Count3/5

27 tools is on the heavier side and slightly exceeds the typical well-scoped range. However, the server covers multiple subdomains (deals, lead discovery, autopilot, sales agent, channels), so the count is justified by the breadth of the domain, though it feels dense.

Completeness4/5

Core lifecycles are covered: deals (create, list, update, move), leads (search, sync, list, enrich, score, verify, send), autopilot (start, list, status), sales agent (config, actions, process), and channels (status, update). Minor gaps include no delete tool for deals/leads and no stop-autopilot, but these are likely intentional and not blocking.

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