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Analyze pipeline

analyze_pipeline
Read-only

DEAL-CLOSING VIEW: ranks the actual PEOPLE to act on — use for "who are my hottest/highest-ICP leads", "best opportunities", "who should I follow up with / reply to", "help me close deals". Returns opportunities ranked by AI-assessed conversion likelihood + ICP fit (with the reason each was flagged), prospects WAITING ON YOUR REPLY (they spoke last), gone-quiet opportunities to nudge, the warm bench (connected, ICP 8+, no opportunity yet), and the count of AI replies already drafted awaiting approval. Every row carries its evidence; act via get_unibox / send_unibox_reply / get_pending_replies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax leads per section (default 10).
campaignIdNoScope to one campaign. Omit for all campaigns on the active setup.
minIcpScoreNoOnly include leads at or above this ICP score (warm bench always uses at least 8).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only declare it is a safe, closed-world read. The description adds rich behavior beyond that: it discloses the ranking basis (AI-assessed conversion likelihood + ICP fit), the exact sections returned (waiting-on-reply, gone-quiet, warm bench, drafted-reply count), and that every row carries its evidence. With no output schema, this detail is essential and present.

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?

Front-loaded with the primary scoping and intent examples, and every clause carries substantive content. It is dense and slightly run-on, but there is little genuine waste.

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?

For a read-only analysis tool with no output schema, the description fully covers what is returned and how to act on it. Nothing an agent needs to call and interpret it correctly is missing.

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 the schema already documents limit, campaignId, and minIcpScore. The description references ICP fit and the 'ICP 8+' warm bench, which loosely aligns with minIcpScore, but it adds no explicit param syntax or mapping. Baseline 3 applies.

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?

States a specific verb (ranks) and resource (the actual PEOPLE/opportunities to act on), and the all-caps 'DEAL-CLOSING VIEW' frames its distinct purpose against siblings like analyze_performance or analyze_relationship. An agent can tell it apart without opening a schema.

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 names the user intents it answers ('who are my hottest/highest-ICP leads', 'help me close deals') and routes downstream actions to named siblings (get_unibox, send_unibox_reply, get_pending_replies). This is a clear when-to-use with alternatives.

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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