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MisarReach MCP Server

by io.github.Misar-AI

score_leads

Queue AI qualification scoring for leads — either every unscored lead in a search job, or a specific set of ids.

Pass jobId OR leadIds, not both. This runs in the BACKGROUND: it returns a count immediately and the scores appear on the leads afterwards, so re-read them with list_leads rather than expecting scores in this response.

CONSUMES AI CREDITS per lead scored. Requires an API key. Passing leadIds rescores leads even if they already have a score, which bills again — pass jobId to score only what is unscored. Caps at 200 ids per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdNoScore all unscored leads for this job UUID (use this OR leadIds)
leadIdsNoSpecific lead UUIDs to (re)score (max 200, use this OR jobId)

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

This is a standout. The description discloses that execution is background (returns count immediately, scores appear later), that AI credits are consumed per lead, that an API key is required, and — critically — that passing leadIds forcibly rescore leads and re-bills. These are critical operational details well beyond what the annotations (which only mark readOnlyHint: false) convey. No contradiction 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?

Every sentence earns its place. The structure flows from purpose → usage → behavior → important caveats, using a clear 'no' rules and a bolded emphasis for emphasis. The warning about credits/rate limits is prominent without being buried. No wasted words.

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?

Despite having no output schema and no required parameters, the description addresses the surface area comprehensively: what happens when you call it, how to get results, what costs are involved, and what edge cases exist (rescore behavior). There's nothing left ambiguous for a tool of this complexity.

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

Parameters5/5

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

Even though schema coverage is 100%, the description enriches the parameters with high-value semantics: jobId scores only unscored leads while leadIds unconditionally rescores, the mutual exclusivity is reinforced, the upper bound of 200 is explained, and the causal chain of 'rescore → rebilling' is revealed. The description fully carries its weight beyond the schema.

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+resource combination — 'Queue AI qualification scoring for leads' — which immediately distinguishes this from sibling tools like list_leads, enrich_lead, or verify_emails. It clearly communicates what the tool does (queue async scoring) and what it doesn't do (return scores synchronously).

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?

Explicitly states the either/or choice between jobId and leadIds, warns about the 200-id cap, and instructs users to re-read with list_leads for results. It even provides decision guidance for choosing between jobId (score only unscored) vs leadIds (force rescore) based on billing implications — going well beyond any baseline.

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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