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Palmyr

by ai.palmyr

Read TikTok engagement history

tiktok_series

Read the stored per-post history for an account you own: the full time series for one video (video_id), per-video growth over a window (hours), or the latest sample per video (neither). Samples come from tiktok_analytics runs, so history exists only for what you have scraped. Costs 0.001 USDC, paid per-action via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoReturn per-video growth over the last N hours
paymentNobase64 x402 payment payload (X-PAYMENT); omit on first call to receive payment instructions
video_idNoReturn the full sample series for this one video
account_idYesYour identifier for the TikTok account

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses read-only behavior, data provenance, per-action cost, and payment method via x402. It doesn't cover edge cases like empty results or rate limits, but covers the most important behavioral aspects.

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?

Two sentences convey purpose, three modes, data provenance, cost, and payment method without any wasteful words. The structure front-loads the main action and resource.

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?

Given no output schema, it explains the three query modes and data availability constraints. It could detail the output format more, but for a simple read tool, it covers the essential context adequately.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds combinational semantics beyond individual parameter descriptions: video_id yields full series, hours yields growth, neither yields latest sample. This clarifies how parameter combinations behave, adding significant value.

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 tool reads stored per-post history for an owned account, and explicitly lists three modes (video_id for full series, hours for growth, neither for latest sample). It distinguishes from tiktok_analytics by noting history comes from analytics runs.

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?

It provides context on when to use the tool: history exists only for what has been scraped by tiktok_analytics. It also explains how to select the desired mode via parameters. However, it does not explicitly name alternative tools for exclusion, making it clear but not fully explicit.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, clearly separated by domain prefixes (card_, email_, phone_, tiktok_). The only potential overlap is phone_read_messages vs wait_for_otp, but wait_for_otp is specifically for OTP extraction and is described as a replacement for hand-rolled polling, making the boundary clear. Status pollers are also domain-specific (card_status, tiktok_connect_status, tiktok_operation_status) and not ambiguous.

Naming Consistency5/5

All tool names follow a consistent snake_case convention with domain-first prefixes (e.g., card_buy, email_send, tiktok_post, phone_temp_number). Even less common names like wait_for_otp and i402_plan are descriptive and stylistically consistent. There are no mixed conventions or vague verbs.

Tool Count4/5

33 tools is above the typical 3-15 range, but the server covers a broad multi-domain purpose (cards, compute, domains, email, phone, TikTok, Twitter, orchestration). Each domain has a focused and coherent set, so the count feels justified rather than bloated. It is slightly high, but not excessively so.

Completeness3/5

Coverage is solid for email, phone, and TikTok, with full lifecycle operations (create, read, extend, delete/cancel where applicable). However, there are notable gaps: compute has only deploy (no list/stop/delete), domains have only check/register (no list/delete), and Twitter has only post (no read/manage). These gaps could force agents to work around missing resource management.

Resources