TikTok Remote MCP Server

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint=false, so safety is covered. The description adds genuinely useful behavioral detail beyond that: token-based pagination via nextPageToken and the fact that only the first page of top-level comments returns the `post` object. It does not mention rate limits or auth requirements, keeping it at a 4 rather than 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded paragraph that opens with the core purpose and then layers in return fields and pagination. It is efficient and wastes little, though the dense enumeration of returned fields is longer than strictly necessary.

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

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

There is no output schema, so the description must carry the return-value burden, and it does: it enumerates comment fields (text, likes, timestamp, reply count, author details, links) plus the post metadata on the first page. Combined with pagination guidance, an agent has everything needed to call and interpret results.

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

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

Schema description coverage is 100%, so the schema already documents videoId, commentId, and nextPageToken. The prose restates their behavior (numeric video id, replies switch, next-page token) but adds no syntax or format detail the schema lacks, matching the baseline 3 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.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Fetches the comments on a public TikTok video') and explicitly distinguishes two operating modes: top-level comments by videoId vs. replies to a comment when commentId is given. This cleanly separates it from siblings like tiktok_posts and tiktok_search without requiring schema inspection.

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

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

Gives clear usage contexts ('sentiment analysis, engagement research, building comment datasets') and points to the posts/search APIs as the upstream way to discover a video. It lacks an explicit when-not or alternative-selection rule (there is no tiktok-comments sibling to route against), so it falls short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover the safety profile (readOnly, openWorld, non-destructive), but the description adds substantive behavior: results come in pages of ~30, a nextPageToken is returned and must be looped to walk history, and the account must be public. It omits rate limits, auth requirements, and failure modes, keeping it below 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core verb and scope are front-loaded, and the pagination mechanic follows logically. The enumeration of returned fields (likes/comments/shares/plays/collects, cover and playable URLs, music, duration, timestamp) is slightly list-heavy but earns its place given there is no output schema.

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

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

With no output schema, the description correctly carries the burden of describing return values and the pagination contract, which it does thoroughly. It lacks edge-case context (empty/private accounts, rate limits), so it is strong but not exhaustive.

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

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

Schema description coverage is 100%, so both parameters (handle, nextPageToken) are already fully documented in the schema, including the @ optionality and the first-page omit behavior. The description restates handle and the token but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource (fetch a page of videos for a public TikTok account by handle) and enumerates the returned fields, so the agent knows exactly what the tool produces. It does not explicitly name or contrast the nearby siblings (tiktok_profile, tiktok_search, tiktok_comments), so it falls short of a 5.

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

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

It gives concrete use cases (monitor competitor content, track creator engagement, build vetting/analytics datasets) and explains the pagination walk via nextPageToken, which is real guidance. It stops short of stating when NOT to use it or pointing to the profile/search alternative for other intents.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds the meaningful constraint that only public profiles are fetched and discloses the returned data surface, though it says nothing about auth needs, rate limits, or behavior on missing/private handles.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action before the return list and use cases. Minor redundancy between the title restatement "Get TikTok Profile" and the opening sentence, and the four-item use-case list is slightly long but each item is substantive.

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

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

For a single-parameter read tool with no output schema, the description compensates by enumerating the returned fields and the intended use cases. It is nearly complete; only error/empty-profile behavior and rate-limit context are absent, which is a minor gap.

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

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

Schema description coverage is 100% and the single handle parameter is fully documented there, including the optional `@` prefix. The description only restates "by username (handle)" and adds no format or normalization detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb and resource ("Fetches a public TikTok profile by username") and enumerates the exact return set (nickname, bio, avatar URLs, flags, counts). An agent can distinguish this from hasdata_instagram_profile_getInstagramProfile or hasdata_facebook_profile_getFacebookProfile on platform and data shape alone.

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

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

Gives concrete usage contexts — CRM/lead enrichment, influencer vetting before outreach, competitor monitoring, dataset building — which orients the agent well. It stops short of naming sibling tools or stating when NOT to use it (e.g. vs tiktok_posts or tiktok_search), so it is clear context without exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint/openWorldHint/destructive=false, so the safety profile is covered. The description adds genuinely useful context: the token-based pagination model via nextPageToken, the fields returned per result type, and the embedded hasdataLink/hasdataPostsLink pointers.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the one-line purpose, then output shape, pagination, and use cases in sequence. It is on the longer side but each sentence carries distinct information, so little is wasted.

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

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

With no output schema, the description carries the return-value burden and does so by describing video vs user fields and the cross-links. Combined with full schema coverage and safety annotations, an agent has enough to call it correctly, though the sibling boundary remains implicit.

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

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

Schema description coverage is 100%, so the enum, keyword, and nextPageToken semantics are already fully documented in the schema. The description only restates pagination at a high level, adding no syntax or format detail beyond what the schema provides; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb+resource ('Searches TikTok by keyword') and enumerates the two result shapes (videos vs users) with their fields. It stops short of explicitly contrasting with the closely related siblings like hasdata_tiktok_profile or hasdata_tiktok_posts, though the hasdataLink/hasdataPostsLink mentions hint at the boundary.

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

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

Provides concrete use cases: 'content discovery, trend research, influencer discovery, or building datasets from a keyword.' This gives clear positive context for when to reach for it, but names no alternatives or exclusions (e.g., when to prefer the profile or posts endpoints).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.