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Influship

by io.github.Influship

Show creator shortlist

render_creator_shortlist

Use this when discovery or lookup tools have returned results and the user would benefit from scanning 1–8 creator cards. Pass search_id after semantic_search_creators so the exact ranking, confidence, and evidence are preserved. Pass creator_ids only for name/handle lookup results. Always call an Influship data tool first; do not invent IDs. This fetches stored creator details and may consume Influship account or API usage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNoCampaign brief or discovery intent that produced this shortlist.
titleNoShort factual title for this shortlist.
search_idNoSearch ID returned by semantic_search_creators. Prefer this for discovery results so exact ranking and evidence are preserved.
creator_idsNoCanonical creator IDs returned by an Influship lookup tool.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewYes
briefYes
titleYes
creatorsYes
omittedCountYes

TDQS

A4.6/5.0
Behavior4/5

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

Annotations are all false, so the description carries the burden. It discloses that the tool fetches stored creator details, preserves ranking/confidence/evidence, and 'may consume Influship account or API usage.' This adds valuable behavior context beyond the annotations, though it doesn't elaborate on output format (covered by output schema).

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?

Five sentences, none wasted. It front-loads the primary use case, then gives parameter routing, a prerequisite, and a cost warning. The structure is clear and scannable.

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?

The description covers usage context, parameter selection, prerequisites, and side effects. It doesn't explicitly state whether search_id and creator_ids are mutually exclusive, which is a minor gap given neither is required in the schema. Overall it is highly complete for a rendering tool.

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%, so baseline is 3. The description adds important parameter semantics: search_id preserves exact ranking/confidence/evidence, creator_ids are only for lookup results, and the 'do not invent IDs' warning. This goes beyond the schema's field descriptions and clarifies parameter selection rules.

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 renders a shortlist of 1–8 creator cards for scanning, and distinguishes it from siblings like render_creator_profile and render_creator_comparison by specifying the discovery/lookup context. It also specifies the exact resource (creator cards) and the action (render/shortlist).

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 explicitly states when to use the tool ('when discovery or lookup tools have returned results'), and provides conditional guidance for parameters: use search_id after semantic_search_creators, or creator_ids for name/handle lookup results. It also says 'Always call an Influship data tool first; do not invent IDs', which gives a clear prerequisite and an alternative direction.

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