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DropTrack Add Contact

droptrack_add_contact

Add a new contact to the current company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoOptional public contact-link slug; generated from contact details when omitted
emailYesContact email address
phoneNoContact phone number
companyNoContact company/organization
websiteNoContact website URL
lastNameYesContact last name
firstNameYesContact first name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoStructured DropTrack result returned by this tool

Schema Changelog

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

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already convey that this is a write operation (readOnlyHint: false) and non-destructive (destructiveHint: false). The description adds no behavioral details beyond 'Add a new contact', such as side effects, required permissions, or behavior when the company context is missing, but it does not contradict the annotations.

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 a single, direct sentence with no filler or redundant phrasing. It places the verb and object first, making the tool's purpose immediately scannable.

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?

For a tool with seven parameters and an output schema, the description covers the basic intent but leaves ambiguity around what 'current company' means and whether a company context must already exist. The 100% schema coverage and output schema reduce the burden, so the description is adequate but not fully self-sufficient.

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?

The input schema has 100% description coverage for all seven parameters, so the structured data fully documents each field. The description adds no additional parameter-level meaning beyond the general sense of 'new contact', which is acceptable under the baseline given 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 description states a specific verb and resource ('Add a new contact') plus a scope ('to the current company'), which clearly conveys the tool's core action. It distinguishes itself from siblings like droptrack_add_contacts_to_list and droptrack_create_contact_list by focusing on a single contact added to a company, though 'current company' is not precisely defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives minimal context ('current company') but no explicit guidance on when to use this tool versus alternatives such as droptrack_add_contacts_to_list or droptrack_create_contact_list. There are no exclusions, prerequisites, or decision rules, leaving the agent to infer usage from the name and sibling list.

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

B3.4/5.0
Disambiguation3/5

Most tools target distinct resources and actions, but several clusters are easy to confuse: get_track_analysis vs get_track_analytics vs get_track_tags, plus analyze_audio/request_track_tagging/auto_tag_tracks overlap in the audio-analysis/tagging space. The descriptions do help separate them, so careful agents can disambiguate, but the naming alone creates real misselection risk.

Naming Consistency4/5

All tools share the droptrack_ prefix, use snake_case, and follow a verb-first noun pattern, with list for collections and get for single items. Minor inconsistencies exist—add_contact vs create_contact_list, browse vs list, auto_tag_tracks—but the overall convention is predictable and readable.

Tool Count2/5

At 55 tools this is far beyond the recommended 3-15 range and well over the 25+ threshold. Many tools are near variants of each other, especially company-level vs label-level ads, analytics, and wallet tools, inflating the surface area and making selection harder.

Completeness3/5

The set covers many domains and some workflows are complete, such as album art generation/polling/acceptance/deletion and track tagging request/poll/apply. However, core lifecycle gaps remain: no update or delete for campaigns, contacts, or contact lists, no playlist mutation tools, and AI press-release/bio workflows end at polling without a save or publish step.