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linkedin-buying-signals

Draft a LinkedIn message

draft_linkedin_message

Write a short LinkedIn connection note for a prospect from their detected signals, in their language. Saves the draft and returns it. Does not send. Drafts follow the house rules (one fact about them, one question, no pitch, under 200 characters) and so must any note passed to send_linkedin_message.

Input Schema

NameRequiredDescriptionDefault
languageNo
prospectIdNo
regenerateNo
linkedinUrlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations only say non-read-only, non-destructive; the description adds the crucial facts that the draft is persisted ('Saves the draft') and that nothing is sent. It also discloses content constraints the agent must satisfy (one fact, one question, no pitch, <200 chars), which annotations cannot convey. Minor gap: no mention of what happens on a duplicate/regenerate call.

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?

Three short sentences, front-loaded with the action and scope, then the persistence/no-send behavior, then the constraints. No filler and nothing repeated from the schema or annotations.

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

Completeness3/5

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

No output schema exists, and the description does cover the return ('returns it') and the side effect. However, with zero schema coverage and all parameters optional, an agent still cannot tell which identifier is required, so the definition is not fully self-sufficient for correct invocation.

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

Parameters2/5

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

Schema description coverage is 0% and the description explains none of the four parameters. 'in their language' hints at the language enum but doesn't clarify whether it overrides detection; prospectId vs linkedinUrl (both optional) is the critical ambiguity, since the tool appears to need one of them, and 'regenerate' is never addressed.

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 (write/draft), a specific resource (LinkedIn connection note), and the source of content (prospect's detected signals). It is immediately distinguishable from draft_email and send_linkedin_message by channel and by the explicit 'Does not send'.

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?

Clearly frames the tool as draft-only and names send_linkedin_message as the downstream alternative, including the rule that any note passed to it must follow house rules. It does not spell out when to choose regenerating an existing draft vs creating a new one, or why linkedinUrl exists as an alternative to prospectId.

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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