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MisarReach MCP Server

by io.github.Misar-AI

preview_message

Read-onlyIdempotent

Draft a sample AI-personalised outreach message for a named person, to show what the agent would say.

This is a PREVIEW: nothing is sent, saved, or attached to a lead or campaign. Use it to check tone before committing to a sequence. The person does not need to exist as a lead — pass their name and, if known, role and company.

This is the one tool here that needs no API key. Generative, so the same input yields different wording each time. Returns the drafted message text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPerson's full name (1-100 chars)
roleNoJob title or role (optional, max 100 chars)
companyNoCompany name (optional, max 100 chars)

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior1/5

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

Annotation Contradiction: The description says 'the same input yields different wording each time,' directly contradicting idempotentHint=true. Even though it usefully discloses that nothing is sent/saved and that no API key is needed, the contradiction is serious.

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?

The description is well-structured with a clear lead sentence followed by useful operational details. It is a bit longer than strictly necessary, but each sentence adds relevant context such as no API key, no persistence, and non-deterministic output.

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

Completeness4/5

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

For a simple 3-parameter tool with no output schema, the description covers purpose, use case, side effects, return value, and non-determinism. The only issue is the contradiction with idempotence, which is addressed separately, but the remaining content is largely 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?

Schema coverage is 100%, so parameters are already documented. The description adds valuable context beyond the schema by explaining that the person need not be a lead and that role/company should be passed 'if known.' This clarifies the purpose of the optional parameters.

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 opens with a specific verb and resource: 'Draft a sample AI-personalised outreach message for a named person.' It clearly distinguishes itself from sibling tools by framing itself as a PREVIEW that sends/saves nothing, so it is not confused with send_to_campaign or create_deal.

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?

It provides explicit guidance: 'Use it to check tone before committing to a sequence,' and clarifies that the person does not need to exist as a lead. It does not name explicit alternative tools, but the use case is clearly scoped.

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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