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Choose the best AlpineLead OI tool

recommend_next_tool
Read-onlyIdempotent

Use when the user's intent is understandable but you are unsure which AlpineLead OI tool should be called next.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesThe user's sales goal or request in natural language.
has_existing_leadNoTrue when the company is already an AlpineLead CRM/history lead.
has_known_companyNoTrue when a specific company and website are already known.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds the behavioral context that this is a routing/decision-support tool rather than a direct execution tool. However, it does not explain how the recommendation is derived or what the tool returns, though an output schema exists.

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 one sentence that states the exact situation for use. It contains no filler, no repeated schema information, and the key condition ('unsure which tool should be called next') is front-loaded.

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 routing/recommender tool, the combination of title, description, annotations, and output schema provides enough context to invoke it correctly. The description could more explicitly mention how the boolean inputs affect the recommendation, but the parameter schemas with defaults cover them. It is a lean but complete definition.

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 description coverage is 100%, so every parameter (goal, has_existing_lead, has_known_company) is already documented in the input schema. The description adds no extra semantic meaning to these parameters, matching the baseline 3 for high schema coverage.

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?

The title states the tool's function — 'Choose the best AlpineLead OI tool' — and the description clarifies when to invoke it: when the user's intent is understandable but the next tool is uncertain. This distinguishes it from the operational sibling tools, which perform concrete actions. It would earn a 5 if the description itself used a direct verb like 'Recommends the next...' instead of relying on the title.

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?

The description gives an explicit trigger: 'use when... unsure which AlpineLead OI tool should be called next.' This clearly scopes its role relative to the sibling tools. It stops short of saying 'when you already know the tool, call it directly,' so it lacks a full when/alternatives contrast, a minor gap.

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

C2.4/5.0
Disambiguation2/5

Several tools overlap or duplicate: run_sales_mission intentionally duplicates check_sales_mission_status and get_sales_mission_result, and there are multiple capability/routing tools (get_capabilities, match_alpinelead_capability, get_alpinelead_recommendation_packet, recommend_next_tool) with similar purposes. Blank descriptions for several nexus_* tools make their boundaries even harder for an agent to determine.

Naming Consistency2/5

All names use snake_case, but there is no consistent verb_noun pattern. The set mixes prefixed families (agentpub_*, nexus_*, predictive_*_info), noun-only names (winning_pattern, learning_engine), reversed noun_verb names (radar_check, hunter_run), and get/check/run/status variants for closely related operations.

Tool Count1/5

56 tools is far beyond a well-scoped server surface. Many are one-off info, status, or diagnostic tools that could be consolidated into parameterized tools. This places an excessive routing burden on the agent and dilutes the core sales workflow.

Completeness3/5

The core lead-to-CRM workflow is fairly complete: discovery, analysis, qualification, sales kit generation, HubSpot push, and learning/feedback loops are covered. However, there are notable gaps such as no way to list or retrieve saved analyses, no explicit stop/cancel for Hunter or Sales Mission runs, and no update/cancel operations for Nexus tasks.

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