learning_engineUse when the user asks how AlpineLead OI has adapted FIT / NEED / NOW scoring to their actual wins and losses. Returns personal weighting and learning confidence.
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the agent knows this is a safe, non-mutating lookup. The description adds some context by stating what is returned, but it does not disclose additional behavioral details such as caching, staleness, or data source behavior. This fits the baseline expectation for annotation-covered tools.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise, front-loaded with the usage trigger, and every sentence adds meaning. No filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless, read-only, idempotent retrieval tool with an output schema, the description is complete. It explains when to use it and what to expect from the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% coverage, so no parameter-level explanation is needed. The description correctly implies the tool takes no input and simply returns the current model. This satisfies the zero-parameter baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly explains the tool's purpose: it retrieves the adaptive FIT / NEED / NOW scoring model and returns personal weighting and learning confidence. It distinguishes itself by addressing a very specific query about AlpineLead OI adaptation, even though it does not explicitly name sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Use when the user asks...' provides a clear condition for when to invoke the tool. It does not explicitly list when not to use alternatives, but the usage trigger is sufficiently specific to guide an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.