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Influship

by io.github.Influship

lookup_profiles

Read-only

Batch-fetch up to 100 profiles by (platform, username) pairs.

Use this when the user has a list of handles and you need profile data for all of them at once (e.g., "give me follower counts for these 30 accounts I'm considering" or "which of @a @b @c are real accounts?"). One round-trip beats 30 calls to get_profile.

Use this for exact batch handle lookup, not semantic discovery. For one exact platform+username pair, use get_profile. For partial or fuzzy handle/name input, use search_creators or autocomplete_creators. Use semantic_search_creators only for topical/niche/audience discovery where false-positive semantic matches are acceptable.

Examples:

  • User: "Compare @a, @b, and @c on Instagram" -> use this tool for the exact handle batch.

  • User: "Give me follower counts for these 30 accounts" -> use this tool.

  • User: "Find wellness creators in Austin" -> use semantic_search_creators, not this tool.

The response splits results into data (profiles found) and not_found (the (platform, username) pairs that weren't recognized). Profiles are returned in no particular order — re-correlate via the platform/username fields if you need to preserve input order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profilesYesProfiles to lookup

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
countNo
resultsNo
has_moreNo
not_foundNo
next_cursorNo
suggested_followupsNo

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint=true annotation, the description discloses important runtime behavior: the response splits into `data` and `not_found`, and profiles are returned in no particular order, requiring re-correlation via platform/username. It also notes the round-trip efficiency benefit. These are not inferable from the schema or 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 well-structured: a one-sentence summary, a when-to-use paragraph with examples, an explicit alternatives paragraph, and a return-behavior note. Every sentence earns its place, and the most critical information is front-loaded. Despite being a bit long, it is appropriately sized for the tool's complexity and avoids fluff.

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?

The description is complete given the tool's complexity. It covers purpose, usage, examples, alternatives, and return behavior. The existence of an output schema means the description need not re-explain return values, but it still adds critical context like unordered results and the not_found split. Nothing important is missing.

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?

The input schema already covers the `profiles` array with item definitions, required fields, and min/max constraints (100% coverage). The description adds meaningful context by framing these as '(platform, username) pairs' for 'exact batch handle lookup' and explaining that not_found contains unrecognized pairs. This goes slightly beyond the schema's bare 'Profiles to lookup' with extra usage semantics.

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 opens with 'Batch-fetch up to 100 profiles by (platform, username) pairs' – a specific verb, resource, and constraint. It then clearly distinguishes this tool from siblings, noting that `get_profile` handles a single exact pair, `search_creators` and `autocomplete_creators` handle fuzzy input, and `semantic_search_creators` handles topical discovery.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: 'when the user has a list of handles and you need profile data for all of them at once.' It also provides concrete examples of user requests that should and should not use this tool, and names alternative tools for other scenarios (e.g., semantic_search_creators for 'Find wellness creators in Austin'). No ambiguity remains.

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