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get_conversation

Get full message history for a conversation. Accepts room_name or talent_slug. Requires employer authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
room_nameNoRoom name from list_conversations
talent_slugNoTalent slug (from search_talent results) — resolves to room name automatically

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state the authentication requirement, which is useful, but it does not confirm read-only status, error behavior, rate limits, or what happens if both parameters are supplied or none are. These gaps leave the agent uncertain about side effects and edge cases, though the 'get' verb implies a safe read operation.

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 exactly two sentences: the first states the core purpose, and the second adds the parameter alternatives and authentication requirement. There is no filler, repetition, or tangential detail. The most important information is front-loaded, making it easy for an agent to quickly parse.

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?

For a simple read operation with two optional parameters and no output schema, the description covers the essentials: what it does, the identifying inputs, and the auth requirement. It could be more complete by clarifying whether at least one parameter is required (schema says required: 0) and what the return structure is, but these are minor given the straightforward nature of the tool and that it returns 'full message history'.

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 coverage is 100% for both parameters, so the schema already documents their purpose and origin (e.g., 'Room name from list_conversations'). The description adds that the tool 'Accepts room_name or talent_slug', reinforcing that they are interchangeable alternatives, a nuance not explicitly in the schema. This is a modest addition, so a baseline of 3 is appropriate rather than a higher score for significant extra meaning.

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?

The description clearly states the operation ('Get full message history') with a specific resource ('a conversation') and scope, which immediately distinguishes it from siblings like list_conversations (listing conversations) and send_message (sending messages). The acceptance of room_name or talent_slug further clarifies the tool's targeted use.

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?

The description indicates when to use it (retrieving full history of a single conversation) and imposes an authentication constraint ('Requires employer authentication'). However, it does not explicitly reference alternatives or when not to use it (e.g., when listing all conversations instead). The context is clear enough for most agents, but it lacks direct exclusion or sibling routing.

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

B3.3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: job posting vs job browsing vs job management vs company management vs talent search vs profile editing vs messaging vs application tracking. Even similar tools like get_companies and search_companies are clearly differentiated by purpose and parameters. Overlapping concepts (e.g., post_job_public vs create_company_job) have explicit differences in authentication and cost.

Naming Consistency4/5

All tools use snake_case and follow a verb-first pattern (add_, get_, create_, update_, delete_, search_, list_, send_, etc.). There are minor deviations like 'show_company_job' instead of 'get_company_job' and 'mark_message_read' which is a verb+noun+adjective, but the overall style is consistent and predictable across the 41 tools.

Tool Count2/5

With 41 tools, this is well into the 'too many' range (25+). While the breadth reflects a comprehensive jobs platform, the number is excessive for an agent to efficiently navigate. Many tools could be consolidated (e.g., profile management could merge add_education/add_experience/update_profile, or company perks could be combined with profile updates). The tool count detracts from usability.

Completeness4/5

The tool set covers the full lifecycle: job posting (create, update, delete, list), job discovery (browse, search, related), company management (profile, perks, tech stack), talent search and messaging, application tracking (save, get, remove, update status), and data analytics (salary, statistics). Minor gaps exist—no delete/update for education or experience, no explicit 'close job' action—but these are edge cases and agents can work around them.

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