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Salesbot - B2B outreach LinkedIn MCP Server with CRM and email

list_pending_approvals

Read-onlyIdempotent

Use for campaign message drafts waiting for approval. One-off MCP messages and invitations waiting for approval are in list_mcp_pending_actions. Return all AI drafts waiting for approval (messages_approved=false). Each draft includes the contact name, headline, company and generated text. Used as input for bulk review by an AI assistant which can iterate through drafts and approve/reject them. Optionally filter by campaign_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (default 25, max 100)
campaign_idNoOptional: filter to a single campaign

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description need not restate them. It adds useful behavior beyond annotations by disclosing the exact filter semantics (messages_approved=false) and the shape of each returned draft, compensating for the absent output schema.

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 core purpose and sibling differentiation, and each following sentence adds routing or return-shape information. The bulk-review sentence is slightly longer than strictly necessary but still carries usage value.

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?

With no output schema, the description steps in to describe returned draft fields (contact name, headline, company, generated text), which is the main gap it needed to fill. Combined with annotations covering the read-only profile and full schema coverage, an agent has what it needs to call this correctly.

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 both parameters (limit and campaign_id) are already documented in the schema. The description only mentions the optional campaign_id filter, which repeats the schema, and says nothing extra about limit behavior; baseline 3 applies when the schema does the heavy lifting.

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 and resource (list campaign message drafts awaiting approval) and explicitly distinguishes itself from the sibling list_mcp_pending_actions, which handles one-off MCP messages and invitations. An agent can tell the two apart without opening either 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 alternative tool and the condition that routes to it, and states the downstream purpose (input for bulk review/approve/reject). This is close to a full when-to-use and when-not-to-use statement.

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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