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Influship

by io.github.Influship

get_posts

Read-only

Fetch a creator's posts, sorted and paginated.

Use this when the user asks to see what a creator has posted (e.g., "show me Jane's last 20 posts", "what are this creator's top-engagement reels?", "pull recent posts from creator-id ABC"). Identify the creator by either creator_id (UUID) OR (platform + username).

sort defaults to "recent" (newest first); use "top_engagement" for the highest- engagement posts, or one of "most_likes" / "most_views" / "most_comments" for a specific metric. limit defaults to 12 and is capped at 50. Pass cursor from a previous response's next_cursor to paginate.

Returns post records (caption, media URL, like/comment/view counts, timestamps), plus has_more and next_cursor for pagination.

Examples:

  • User: "Show @niickjackson's recent Instagram posts" -> use this tool with platform "instagram" and username "niickjackson".

  • User: "Is @niickjackson a fit for Pixel?" -> use this after get_profile when the fit analysis needs recent content evidence, then call match_creators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort orderrecent
limitNoMaximum posts to return
cursorNoOpaque keyset cursor from next_cursor. A cursor is bound to the sort order that produced it; using it with another sort returns 400.
platformNoPlatform (required with username)
usernameNoUsername (required with platform)
creator_idNoCreator ID (use this OR platform+username)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
countNo
resultsNo
has_moreNo
not_foundNo
next_cursorNo
suggested_followupsNo

TDQS

A4.6/5.0
Behavior5/5

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

Adds rich behavioral context beyond the read-only annotation: describes pagination via next_cursor/has_more, limit cap at 50, default sort behavior, and cursor-tied-to-sort error semantics. This gives an agent a clear model of how the tool behaves and what to expect.

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?

Though moderately long, the description is well-structured with a clear lead sentence, parameter guidance, return summary, and concrete examples. Every section adds value and none feels redundant with the schema.

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

Completeness5/5

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

For a list tool with pagination, multiple sort modes, and an OR identifier pattern, the description fully covers identification, sorting defaults, pagination semantics, and return fields. Output schema handles structured return details, and the description supplements with usage context and examples.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds crucial meaning: the OR relationship between creator_id and (platform+username), the semantics behind each sort enum value, and how to use cursor from next_cursor. This goes beyond the schema's field-level descriptions.

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 opens with a specific verb and resource ('Fetch a creator's posts, sorted and paginated'), making the core purpose clear. However, it does not explicitly distinguish this tool from sibling get_instagram_posts, so it misses the sibling-differentiation criterion for a 5.

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?

Provides explicit usage scenarios and examples ('Use this when the user asks to see what a creator has posted'), plus sequencing guidance with get_profile and match_creators. It lacks clear when-not-to-use or alternative-tool exclusions, so it earns 4 rather than 5.

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

A3.9/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: `autocomplete_creators` and `search_creators` both claim the exact same example query ('Who is that fitness coach called Jane?') as their primary use case, creating direct routing conflicts. `get_creator` and `get_profile` also overlap heavily for exact platform+username lookups, with descriptions admitting the choice depends on whether 'profile metrics are the main need' — a thin distinction. `search_creators` further muddies things by dual-routing to legacy semantic search, making it a hybrid that competes with both `autocomplete_creators` and `semantic_search_creators`.

Naming Consistency4/5

The naming follows a mostly consistent verb_noun snake_case pattern: `get_*` covers record fetching, with clear singular/batch pairs like `get_instagram_post`/`get_instagram_posts` and transcript variants. Minor deviations exist (`semantic_search_creators` prefixes a modifier, and `autocomplete_`, `find_`, `match_`, `lookup_`, `render_` each introduce different verbs), but the style is uniform and the verb typically reflects the operation type.

Tool Count3/5

At 28 tools the server is heavy, but the scope is genuinely broad — three platform-specific data surfaces (Instagram, TikTok, YouTube), each requiring profile/video/transcript/listing operations, plus creator search, matching, and rendering. The count is inflated by redundancy, though: four `render_*` tools that could collapse into one parameterized tool, and batch variants of the Instagram raw-data endpoints. It is borderline acceptable for the platform-multiplied domain rather than chaotic bloat.

Completeness4/5

The tool surface covers the full read-only creator workflow: fuzzy lookup (autocomplete/search), exact profile fetch (get_profile/lookup_profiles), discovery (semantic_search/find_lookalike), fit scoring (match_creators), content evidence (get_posts), and presentation (render_*). Notable gaps include no Instagram-specific profile endpoint (odd given TikTok/YouTube have dedicated ones), no YouTube comments, and no audience-demographic data, but agents can complete realistic workflows without dead ends.

Resources